The Economics of Grid-Connected Hybrid Distributed Generation
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The Economics of Grid-Connected Hybrid Distributed Generation

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This is a case study on the economics of hybrid distributed generation embedded in the Orion Networks distribution system in New Zealand

This is a case study on the economics of hybrid distributed generation embedded in the Orion Networks distribution system in New Zealand

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    The Economics of Grid-Connected Hybrid Distributed Generation The Economics of Grid-Connected Hybrid Distributed Generation Document Transcript

    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Decentralised Capacity-Support for Distribution Networks: The Economics of Distributed Energy Resources 24th November 2003 Industrial Research Limited PO Box 20028, Bishopdale Christchurch 8001 New Zealand Tel: +64-3-358-6802 Fax: +64-3-358-9506 Email: i.sanders@irl.cri.nz Web address: http://www.irl.cri.nz Industrial Research Page 1 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Contents Page Section Heading 3 Glossary 4 Abstract 5 Terms of Reference 6 Acknowledgements 7 Summary 8 Introduction 11 Scenarios Examined 13 Distributed Generation Operating Scenarios 17 MCLM Operating Scenario 21 IPP Operating Scenario 24 Other Potential Benefits Available from these Technology Combinations Scenarios 25 Resource Inputs 26 Main Results of the Scenarios 36 Further Discussion of Results 40 Conclusions 44 References 45 Appendix One: Key Assumptions 46 Appendix Two: MGB Model Terms and Conditions 55 Appendix Three: IDES Simulation Modelling Capability 61 Appendix Four: Capacity Metering for General Customers 69 Appendix Five: Legislative Frameworks for DG Facilitation Industrial Research Page 2 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Glossary AC Assessed Capacity BDG Biodiesel Generator / Genset BDG-WTG Biodiesel-Wind Hybrid CHP Combined Heat and Power CPD Control Period Demand DE Distributed Energy DE(R) Distributed Energy (Resources) DG Diesel Generator / Genset DG-WTG Diesel-Wind Hybrid EGB Electricity Governance Board FRST Foundation for Research, Science and Technology GCLM General Customer Load Management / Manager GHG Green House Gas GXP Grid Exit Point IDES Integrated Distributed Energy Systems / Solutions IPP Independent Power Production / Producer IRR Internal Rate of Return MARIA Metering And Reconciliation Information Agreement MCLM Major Customer Load Management / Manager MGB Maria Governance Board MORST Ministry of Research, Science and Technology MTCCDG Model Terms and Conditions for Connection of Distributed Generation NPV Net Present Value PPD Peak Period Demand RDE Renewable Distributed Energy ROI Return On Investment TOU Time Of Use WTG Wind Turbine Generator Industrial Research Page 3 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Abstract This report describes some of the opportunities identified by Industrial Research for investing in distributed generation technologies under existing electricity market arrangements. Some adjustments to existing market practices to produce a “more level playing field” for distributed generation are recommended. In particular we demonstrate the importance of supply capacity in the economics of distributed generation. The delivery of capacity and fair reward from network operators to the providers of this service at all levels is key to the viability of distributed energy technologies in a network environment. Industrial Research Page 4 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Terms of Reference The New Zealand Foundation for Research, Science and Technology (FRST) is funding a program of research to evaluate and demonstrate opportunities for Renewable Distributed Energy (RDE) technologies in New Zealand. The study on which this report is based was undertaken under this program (FRST Contract Number: CO8X0203). Industrial Research Page 5 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Acknowledgements: This report is largely the work of Industrial Research Limited staff but we would like to acknowledge the practical support and advice particularly from Orion Networks staff and local technology developers. Industrial Research Page 6 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Summary S1. New Zealand needs to implement both sustainable generation technologies and sustainable network management practices. Hybrid generation systems involve the combination of two or more conversion technologies, for instance a diesel generator set and a renewable energy photovoltaic collector system. This approach is now well proven in the remote area market where the cost of energy is high. A network connected combination that provides local energy and capacity could have wider application in providing solutions to weak grid and other distribution system constraints, as well as achieving the desirable goal of delivering substantial energy from sustainable resources. S2. This report shows results of an analysis of the costs of grid-connected mini-scale (550kW) wind, diesel and hybrid diesel-wind generator systems. It is shown that valuation of the capacity supplied by a hybrid system under present energy and capacity pricing regimes, can be cost-effective in many regions with specific peak demand or capacity constraints. As grid-sourced electricity costs rise, the diesel- wind hybrid combination can become economic in regions with lower wind speeds, but only if the market rewards the value of the capacity delivered. S3. Our analysis shows that the electricity delivery price schedule proposed by Orion New Zealand Limited for major customer connections and embedded generation significantly improves the financial viability of grid-connected mini-scale wind- diesel hybrid generation in the Canterbury region, where sufficient wind energy regimes exist, or where capacity-support requirements are restricted to no more than several hundred hours per year. Furthermore, the network company benefits from targeted grid-connected Renewable Distributed Energy (RDE) sites might easily surpass the benefits captured by independent network users. S4. Wind-diesel hybrid distributed generation currently offers the best combination in modest to high wind areas for spreading the risk of alternative energy supply. S5. The New Zealand supply industry has enjoyed a number of years in an environment where they make the rules. This has not led to a favourable environment for distributed generation. It is imperative that other network operators and retailers in New Zealand follow and challenge the lead taken by Orion Networks in offering rewards for network support. If a common set of principles for fair payment is not quickly adopted voluntarily by supply authorities throughout the country, regulation to force fair practice is the only alternative. Industrial Research Page 7 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Introduction 1. For the last five years, Industrial Research has been evaluating a wide range of resource opportunities for adopting Renewable Distributed Energy (RDE) technologies in New Zealand. The objective has been to demonstrate the techno- economic viability of micro- (less than 100kW capacity), mini- (between 100kW and 1000kW capacity) and small-scale (between 1MW and 10MW capacity) RDE systems in New Zealand. In the process specialised tools and methodologies have been developed to fulfil this purpose. (Unless ‘scale’ is specifically mentioned, the term ‘small’ will refer to anything from micro-scale to small-scale inclusive). 2. Our research to introduce distributed energy-based systems has been motivated by the promise of more efficient energy utilisation and the opportunity for capturing local renewable energy resources with minimal use of additional infrastructure. This is possible through: I. Local generation solutions that relieve distribution network capacity while maintaining utilisation. II. Technology that will provide alternatives to uneconomic network sections. III. Creating the means for large numbers of small distributed generators to export individually modest quantities of electricity from otherwise uneconomic network assets to different network users. IV. Ability to track slow growth in demand with small matching incremental steps in generation, thus avoiding or delaying major upgrades. These potential network benefits contrast with the more popular view that distributed generation threatens the traditional electricity supply infrastructure by taking away energy delivery but not alleviating capacity demands. 3. In the main, small-scale technology developers have been preoccupied with reducing the costs of their own particular product in the high volume micro- / mini- scale embedded generation marketplace. Unfortunately, no single technology can yet provide the quality of service delivered by the distribution network, at the distribution network price. For example, a wind generator cannot guarantee firm capacity, so the network must provide this; and, while a diesel genset can deliver capacity the cost of energy from a diesel genset is generally too high, so it is relegated to a standby function. This report evaluates the ability of these two technologies to deliver matching energy and firm capacity to complement grid based electricity services. 4. A network connected electricity user can potentially save money by either reducing energy consumed or by restricting the demand on power capacity for the few hours of the year when the network ability to deliver the capacity demanded is under stress. In the latter case, because the invested capacity of the whole delivery infrastructure is at stake, the cost savings can be substantial. The cost of capacity to Industrial Research Page 8 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources supply power (measured in kVA) is often not appreciated – it ranges from around one to over 10 times the cost of actual grid energy delivered (measured in kWh). 5. If the financial instruments are in place to reward the network user for releasing capacity (e.g. via customer load management) or supplying capacity (e.g. via network-embedded generation) when and where it is required, the benefits may be substantial. Embedded generation can extend to injecting both active and reactive power into the network (i.e. capacity support through distributed generation). The capacity value of active power is higher than reactive power. Reactive power has no energy value, i.e. it can do no work, but because it can be used in the distribution system to maintain conditions for power flow, it is worth paying for at times of system stress. 6. A previous publication by Industrial Research Limited has shown that a mix of renewable and dispatchable Distributed Energy Resources (DER) can potentially provide energy and capacity at a reasonable cost, compared with the cost of investing in new assets or upgrading existing infrastructure1. This study extends these principles to show that in some circumstances the wind-diesel genset combination can provide good returns, not only as an alternative to upgrades but also as a third party investment based on current supply industry contract pricing structures. 7. Because of the scale and case-specific conditions that apply to these projects, easily implemented but accurate costing of each project is required. Most of the DER techno-economic evaluation tools available today, are either too specialised (very detailed but narrowly focused – leaving out most of the strategic decision-making process and alternative solutions available), or too broad (providing global coverage but insufficient detail – leaving out most of the tactical decision-making process and data critical for providing optimum solutions) to evaluate micro- to small-scale renewable generation opportunities2. We have developed the tools and methods to address these techno-economic evaluation shortfalls, and applied them to “Integrated Distributed Energy Systems” or IDES scenarios outlined in this report3. 8. Our analysis shows that micro- and mini-scale “own-use” wind energy is not generally financially viable under existing market conditions in New Zealand.4 The smaller the wind turbine, the less viable grid connection becomes. These results can be reversed if in addition to energy income, payment can be gained from guaranteeing capacity-support to a local load or to the distribution network during designated “Peak” and / or “Control” Period Demand (PPD and CPD respectively5,6) The easiest way to do this is to add a diesel genset or similar dispatchable generator to the distributed generation mix. The analysis in this report simply uses the genset to complement the available wind generation and deliver a fixed level of capacity support during network peak demand periods. There are other modes of operation, including the provision of standby power and generating at times of high prices that could add further value. The application of the Industrial Research Page 9 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources technology combination is restricted to rural areas, with at least modest wind regimes. 9. An Integrated Distributed Energy System is a distributed electrical energy system which uses local energy resources to help deliver local services, and is connected in parallel to a conventional supply (which may be a grid supplied distribution system, or a diesel genset etc. It may include electrical generation and heat capture, and electrical and heat storage. The four key characteristics of IDES are that they are: œ Integrated – embedding combinations of energy supply technologies (including non-electrical options) within existing infrastructure. œ Distributed – matching micro- to small-scale supply and use (<10MW / site). œ Energy driven – providing power and heat and storage as required. œ Systems providing solutions – delivering energy services that are sustainable, reliable and at an acceptable cost. These applications are fully detailed in the “ IDES-Introduction” Report and the “ IDES-Overview” Presentation3. A summary of the IDES Simulation modelling capability is given in appendix three. Industrial Research Page 10 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Scenarios Examined 10. The network supplies both energy and capacity in delivering an energy service to a customer. A local generator can potentially offer both of these functions, but will only do so if the rewards are adequate. Obviously, the combined financial return from these two components of service is potentially higher than simply the delivery of energy alone, with no time constraints. 11. A large number of different hybrid Distributed Energy (DE) system sizes and environments have been modelled using the IDES tools and methodologies. This report describes just one detailed study of a 550kW Westwind-550 Wind Turbine Generator (WTG) and a 550kW Diesel Generator (DG) for deployment within the Orion network region of New Zealand. 12. The economic assessment presented uses actual capacity pricing schedules that Orion has / or is proposing to make available to its major customers and network- embedded DE operators5. Typical energy pricing schedules available to major customers (energy users) are used to model realistic market conditions. An analysis of the Net Present Value (NPV), Internal Rate of return (IRR), Return on Investment (ROI), payback period and the equivalent cost / revenue over lifetime in $ / kWh electricity generated was undertaken. 13. Financial results were compared for average annual wind speeds of 5, 7 and 9 m/s, using 10-minute average wind data. A comparison was made between releasing capacity from the network (DE operating as a Major Customer Load Manager7, i.e. MCLM Operating Scenario), and supplying capacity to the network (DE operating as an Independent Power Producer, i.e. IPP Operating Scenario). 14. Under the first scenario, the MCLM used locally on site all the energy and capacity produced to reduce an actual load (so that both the energy bought from the energy retailer and the capacity required from the distribution network were reduced); and under the second, the IPP exported all the energy and capacity produced to the local distribution network (so that all the energy produced was purchased by the energy retailer, and all the capacity delivered over defined periods was purchased by the distribution network). Although the net impact on energy flows is identical, at present the supply industry treats these two scenarios quite differently. 15. These two scenarios are selected as representing opposite ends of the spectrum for DE deployment. A combination of these scenarios exists for a wide range of DE applications, where the system functions either as a net-importer or a net-exporter of energy and / or capacity-support. The MCLM and IPP Operating Scenarios compare the economic and technical performance of the following DE systems (see figures 1 and 2 respectively): A. 550kW Wind Turbine Generator by itself, WTG-Only ($2,725 / kW over life); Industrial Research Page 11 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources B. 550kW Combined Diesel-Wind Turbine Generator Hybrid, DG-WTG Hybrid; C. 550kW Diesel Generator by itself, DG-Only ($399 / kW over life without fuel). DISTRIBUTION NETWORK DISTRIBUTION NETWORK DISTRIBUTION NETWORK CUSTOMER CUSTOMER CUSTOMER LOAD LOAD LOAD DIESEL DIESEL GENSET GENSET A. WTG ONLY MCLM B. DG-WTG HYBRID MCLM C. DG ONLY MCLM Figure 1: WTG-Only, DG-WTG Hybrid & DG-Only Major Customer Load Management (MCLM) Operating Scenarios DISTRIBUTION NETWORK DISTRIBUTION NETWORK DISTRIBUTION NETWORK DIESEL DIESEL GENSET GENSET A. WTG ONLY IPP B. DG-WTG HYBRID IPP C. DG ONLY IPP Figure 2: WTG-Only, DG-WTG Hybrid & DG-Only Independent Power Production (IPP) Operating Scenarios 16. Under the IPP operating regimes illustrated in figure 2, it is assumed that the energy associated with running and maintaining the IPP facility itself is negligible. A more detailed list of assumptions used to evaluate the IPP and MCLM operating scenarios is provided in appendix one. The key results are shown in the “ Main Results of the Scenarios” section of this report. 17. The next three sections summarise the energy and capacity payment options assumed to be available. Industrial Research Page 12 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Distributed Generation Operating Scenarios Major Customers: A Distribution Network Definition 18. The MCLM scenario is only relevant to “ major customers” who are charged according to Time-Of-Use (TOU) capacity taken from the network. According to Orion’ s “ Electricity Delivery Pricing for Major Customer Connections” (Issue three, 31Jan 2003), any connection may be classified as a “ Major Customer Connection” , as long as charges are paid according to the basis given in Orion’ s “ Electricity Delivery Price Schedule for Major Customer Connections” . In particular, a “ Minimum Assessed Capacity” of 300kVA is chargeable (as a major customer). “ The calculated Assessed Capacity (AC) of a connection is the average of the 12 highest half-hourly kVA demands from those occurring on working weekdays between 7.30am and 8.30pm (i.e. half hours ending 8.00am and 8.30pm), during (the previous) 12-month period as long as the average of the 12 highest anytime half-hourly kVA demands is not greater by more than 20% of this value” 7. The figure for AC that is put in place at the start of the season (e.g. on the 1st October for Zone A Winter-based connections) is applied on all invoices through to 30th September of the following year. The twelve highest demands that are recorded during this twelve-month billing period are then used to determine the AC value that will be in place from 1st October of the following year. The AC value is apportioned on a daily basis over the 12-month period. “ If the average of the 12 highest half-hourly kVA demands measured at any time during the 12-month period exceeds the “ Calculated AC” by more than 20%, then specific consideration will be given with regard to the implications on Orion’ s network investment and Orion will negotiate an appropriate “ Adjusted AC” with the Contracted Party” 7. 19. From a distribution network perspective, major customers are typically energy users not requiring the use of the 400V distribution network infrastructure. Customers connected to the 400V distribution network are typically referred to as general customers, and they normally include all domestic energy users and most small to medium sized commercial energy users. This level of energy delivery service is significantly more expensive to provide than that used by major users (typically large commercial and industrial energy users) who are connected to the distribution network directly at 11kV for example5. This study does not evaluate the potential of distributed energy capacity support to the network by general customers, but a further report in 2003, entitled “The Economics of Micro-Scale Embedded Wind- Diesel Generation” will do so. Major Customers: An Energy Retailer Definition 20. From an energy retailer’ s perspective, a major customer is typically one that has Time-Of-Use (TOU) metering. A TOU meter records energy in half-hourly intervals (the intervals in which it is bought and sold in the wholesale market). Industrial Research Page 13 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Meridian Energy’ s major customers typically spend over $30,000 per site, per year, on total electricity costs8. Major Customers: Energy and Capacity 21. Meridian Energy will charge major customers either by a TOU price related to a fixed or variable energy purchase volume contract (of typically one to three years duration), or will charge the wholesale spot price (plus some administration charge) related to the actual half-hourly electricity wholesale energy market price. Orion New Zealand Limited will charge major customers for capacity: (a) during a specified demand period, known as a “ Control Period Demand” (CPD); and, (b) at anytime for an “ Assessed Capacity” (AC). CPD and AC charges are priced in $ / kVA / year. General Customers: Energy and Capacity Charges 22. Meridian Energy will typically charge general customers a domestic and / or commercial tariff that may / may not be affected by the time the electricity is used (depending on the tariff(s) selected by the customer). Orion New Zealand Limited will charge the Retailer for their general customers for an estimate of the capacity they used during the specified demand period, known as a “ Peak Period Demand” (PPD). This estimate is subsequently washed up on the Retailer invoices when the actual Retailer peak is known at the end of the season. This peak demand charge is re-priced by the Retailer as part of the Retailer bundled energy charge. For example, a price of 12.86 cents / unit (kWh) for a residential customer of Meridian is such a bundled charge. The government has imposed an arbitrary cap on the fixed charge component that a network company can charge domestic customers for their share of the PPD cost. If this fixed charge component exceeds the 10% cap, then some of the PPD capacity cost is transferred to a variable charge based on average energy use. Major Customers as Generators: Energy and Capacity Payments 23. Energy retailers (for example Meridian Energy), will typically buy energy from major customers at a TOU price that is either: (a) directly related to the wholesale market spot price, or (b) a fixed / variable energy volume pricing contract. Orion New Zealand Limited is willing to pay network-embedded generators for providing capacity during the PPD time periods (see later for an explanation), whether or not they are operating as major customer connections5. This payment will include real and reactive power support. General Customers as Generators: Energy and Capacity Payments 24. We (the authors) are not directly aware of any contractual arrangements / legal prerequisites, obliging energy retailers to purchase electricity from an embedded generator operated by a general customer (but net metering is an option in this case). General customers without TOU metering will require alternative energy and Industrial Research Page 14 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources capacity monitoring arrangements to reconcile costs with payments for the distribution and transmission networks, the energy retailer and the general customer. Orion New Zealand Limited (as indicated above) will make payments to Retailers (since Orion does not have contractual arrangements with General Customers) for passing on to General Customers for providing capacity during the PPD5 via network-embedded generation. This payment will include real and reactive power support. Energy supplied by a generator will have to be bought by an energy retailer at a pre-determined price or under a pre-determined contractual arrangement. 25. Net-metering is the practice of using bi-directional metering to measure consumption and generation of electricity by a small generation facility (such as a house with a wind or solar photovoltaic system). The energy produced or consumed is purchased from or sold to the facility at the same price. Metering using a reversible meter, has been advocated as a low cost means of accounting for net purchases / sales by small consumers, using appropriate energy / capacity profiles fitting the electricity delivery schedule as a basis for determining costs and payments5. The disadvantage of using such a method is that it cannot account for different TOU energy costs or for capacity support, and so we do not see this approach as adequate. (For metering exported energy at any level, we recommend the minimum installation should be a two-rate meter, switchable by ripple control or a similar control signal). The value of this approach is addressed in a further paper. The Influence of Distributed Generation on Major Customers 26. In the next two sections we will describe the MCLM and IPP operating scenarios. The MCLM operating scenario describes the impact of on-site distributed generation on a major customer’ s energy consumption and capacity requirement costs / savings. The IPP operating scenario describes the impact of energy production and capacity delivery on a network-embedded generator’ s costs / savings (the generator may or may not be owned by a major customer – but will be considered here to be so – in order to describe contractual arrangements for selling electricity to a retailer). 27. Options for embedded generation capacity payments to or from Orion are complex. In the Orion network, there are three mechanisms: (1) Assessed Capacity (AC), (2) Control Period Demand (CPD), and (3) Peak Period Demand (PPD). AC and CPD apply to major customers only, operating under the MCLM scenario. The AC is determined by the load (capacity) drawn at anytime by a major customer. Major customers operate under either a summer (representing pre-dominantly rural loads) or winter (representing predominantly urban loads) CPD regime, depending on their location in the network. PPD is used to calculate line rental fees for general customers, and to pay network-embedded generators operating under the IPP scenario (whether they are classified as general or major customers). Embedded generators also operate under either a summer or winter PPD regime, depending on their location in the network. The different payment options for these two extremes Industrial Research Page 15 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources are summarised in figure 3 below. Note that in the GCLM / MCLM scenario, embedded generation if present, does not export power so it simply modifies the energy and capacity requirements of the load connections, as seen by the supply system. Customer Status Scenario Payment Options (Typical) Line Rental Fee (GCLM) Load Management Single / Multiple Energy Tariff(s) (may / may not be time-related) General Customer (GC) Connection Fee (IPP) Peak Period Demand Pricing Generation TOU Energy Pricing or Net-metering Connection Fee (MCLM) Load Assessed Capacity (AC) Pricing Management Control Period Demand (CPD) Pricing TOU Energy Pricing Major Customer (MC) Connection Fee (IPP) Generation Peak Period Demand (PPD) Pricing TOU Energy Pricing Figure 3: Summary of Typical Payment Options for the Different Operating Scenarios Industrial Research Page 16 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources MCLM Operating Scenario Energy Payment Options 28. Energy savings possible are simply determined from the energy wholesale pricing schedule negotiated between the IPP or MCLM and the energy retailer. An energy pricing schedule proposed by Meridian Energy was used in this study. This schedule is shown in table 1 below. The average energy purchase price in this schedule is 5.47 cents / kWh. Table 1: Typical Wholesale Energy Pricing Schedule PRICE SCHEDULE - Variable volume for 3 Years Area: Christchurch Customer: Industrial Research Ltd Business Day Non-Business day Month 0000-0330 0400-0730 0800-1130 1200-1530 1600-1930 2000-2330 0000-0330 0400-0730 0800-1130 1200-1530 1600-1930 2000-2330 Mar-03 3.55 4.51 6.72 6.10 5.79 5.06 3.92 3.40 4.78 4.30 4.25 4.14 Apr-03 3.56 4.51 6.73 6.10 5.79 5.06 3.92 3.41 4.79 4.30 4.25 4.14 May-03 4.62 5.37 7.10 6.11 7.59 6.21 4.86 3.48 4.98 4.42 6.25 4.86 Jun-03 5.16 6.00 7.93 6.83 8.48 6.93 5.42 3.89 5.56 4.94 6.98 5.43 Jul-03 5.00 5.81 7.67 6.61 8.21 6.71 5.25 3.76 5.38 4.78 6.76 5.26 Aug-03 4.91 5.71 7.54 6.50 8.07 6.60 5.16 3.70 5.29 4.70 6.64 5.17 Sep-03 4.22 4.91 6.49 5.59 6.94 5.68 4.44 3.18 4.55 4.04 5.72 4.45 Oct-03 3.45 4.38 6.53 5.92 5.62 4.92 3.81 3.31 4.65 4.18 4.13 4.02 Nov-03 2.75 3.49 5.21 4.72 4.48 3.92 3.03 2.64 3.70 3.33 3.29 3.21 Dec-03 2.50 3.17 4.72 4.28 4.07 3.56 2.75 2.39 3.36 3.02 2.98 2.91 Jan-04 2.67 3.38 5.04 4.57 4.34 3.80 2.94 2.55 3.59 3.22 3.18 3.11 Feb-04 3.12 3.95 5.90 5.34 5.07 4.44 3.44 2.99 4.19 3.77 3.72 3.63 Mar-04 3.96 5.02 7.48 6.78 6.44 5.63 4.36 3.79 5.32 4.78 4.73 4.61 Apr-04 3.96 5.02 7.49 6.79 6.44 5.64 4.36 3.79 5.33 4.79 4.73 4.61 May-04 5.14 5.98 7.90 6.80 8.45 6.91 5.40 3.87 5.54 4.92 6.95 5.41 Jun-04 5.74 6.68 8.82 7.60 9.44 7.71 6.04 4.32 6.19 5.50 7.77 6.05 Jul-04 5.56 6.46 8.54 7.35 9.13 7.47 5.84 4.19 5.99 5.32 7.52 5.85 Aug-04 5.46 6.35 8.39 7.23 8.98 7.34 5.74 4.11 5.89 5.23 7.39 5.75 Sep-04 4.70 5.47 7.22 6.22 7.73 6.32 4.94 3.54 5.07 4.50 6.36 4.95 Oct-04 3.84 4.87 7.27 6.59 6.26 5.47 4.24 3.68 5.17 4.65 4.59 4.48 Nov-04 3.06 3.89 5.79 5.25 4.99 4.36 3.38 2.93 4.12 3.70 3.66 3.57 Dec-04 2.78 3.52 5.26 4.76 4.52 3.96 3.06 2.66 3.74 3.36 3.32 3.24 Jan-05 2.97 3.76 5.61 5.09 4.83 4.22 3.27 2.84 3.99 3.59 3.54 3.46 Feb-05 3.47 4.40 6.56 5.95 5.65 4.94 3.82 3.32 4.67 4.19 4.14 4.04 Mar-05 4.22 5.36 7.99 7.25 6.88 6.02 4.66 4.05 5.69 5.11 5.05 4.92 Apr-05 4.23 5.36 8.00 7.25 6.88 6.02 4.66 4.05 5.69 5.11 5.05 4.92 May-05 5.49 6.39 8.43 7.27 9.02 7.38 5.77 4.14 5.92 5.26 7.43 5.78 Jun-05 6.13 7.13 9.42 8.12 10.08 8.24 6.45 4.62 6.61 5.87 8.30 6.46 Jul-05 5.94 6.90 9.12 7.86 9.76 7.98 6.24 4.47 6.40 5.68 8.03 6.25 Aug-05 5.84 6.79 8.96 7.72 9.59 7.84 6.13 4.39 6.29 5.59 7.89 6.14 Sep-05 5.02 5.84 7.71 6.64 8.25 6.75 5.28 3.78 5.41 4.81 6.79 5.29 Oct-05 4.10 5.21 7.76 7.04 6.68 5.84 4.52 3.93 5.52 4.96 4.90 4.78 Nov-05 3.27 4.15 6.19 5.61 5.33 4.66 3.61 3.13 4.40 3.96 3.91 3.81 Dec-05 2.97 3.77 5.61 5.09 4.83 4.23 3.27 2.84 3.99 3.59 3.55 3.46 Jan-06 3.17 4.02 5.99 5.43 5.16 4.51 3.49 3.04 4.26 3.83 3.79 3.69 Feb-06 3.70 4.70 7.01 6.35 6.03 5.28 4.08 3.55 4.99 4.48 4.43 4.32 4.17 5.06 7.11 6.30 6.83 5.77 4.49 3.55 5.03 4.49 5.33 4.62 5.87 Business Day (Average) 4.58 Non-Business Day (Average) 251 Days 5.471 Overall Average 114 Days In this study, only the average wholesale energy price (5.47 cents / kWh) was used in the simulations. (The IDES tools may be used however, to accommodate entire pricing schedules like the one shown in table 1 above). The impact on the accuracy Industrial Research Page 17 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources of the results from using only the average wholesale price in this study, was found to be negligible for the energy pricing schedules used and distributed generation scenarios evaluated. Capacity Payment Options 29. There are several ways an MCLM may obtain savings for reducing capacity demand from the distribution company. These will vary depending on the rules that the distributor decides to apply6. For major customers on the Orion network, capacity savings may be made by minimising peak demand at all times, based on the Assessed Capacity (AC) pricing schedule, and in addition, more specifically during the seasonal Control Period Demand (CPD)7. The CPD capacity for a major customer is simply the average capacity drawn from the network during every control period scheduled by the network company for a particular season (depending on location, the customer operates under a winter or summer season regime). In order to achieve maximum CPD capacity reduction savings, it is necessary to schedule operation of embedded generation to coincide with every CPD in the season. For general customers operating on the 400V distribution network, CPD and AC benefits are not currently available. 30. CPD is used by Orion to determine the capacity cost contributions from major customers for maintaining the distribution network and to encourage major customer demand reductions at times of network constraint. A major customer control period will be at least 15 minutes long and will be preceded by a 15 minute warning sent by a ripple control signal. The overall financial advantage of providing capacity-support during a CPD depends on the unpredictable duration of CPD over the season. 31. Depending on their location in the network, customers operate under either a winter or summer season CPD regime. During winter, because of the impact of heating demand (which is temperature dependent), the load connected to the Islington, Addington, Bromley and Papanui Grid Exit Points (GXPs) is managed through the application of CPD. This is also known as the winter peaking zone (or zone A), which is primarily urban and has significant industrial, commercial and residential components to the load. During summer, because of the impact of rural irrigation loads (which are rainfall and hydro-storage dependent), the load connected to the Springston and Hororata GXPs is managed through the application of CPD. This is also known as the summer peaking zone (or zone B), which is primarily rural and has a significant irrigation pump component to the load. 32. Summer load management is between 1 November and end of February, and winter load management is between 1 May to 31 August. Total seasonal duration of CPD varies from a few minutes up to 150 hours5. Data for the past three summer and nine winter CPD seasons was available for analysis. The duration of chargeable major customer control periods over the past three seasons for the summer seasonal CPD is shown in table 2 below. The duration of chargeable major customer control Industrial Research Page 18 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources periods over the past nine seasons for the winter seasonal CPD is shown in table 3 below. Table 2: Summer Control Period Demand for the Past Three Seasons Chargeable Major Customer Control Periods Season Control Period Covered Cumulative Duration Summer 2000-01 1 Nov 00 – 28 Feb 01 81.1 Hours Summer 2001-02 1 Nov 01 – 28 Feb 02 1.6 Hours Summer 2002-03 1 Nov 02 – 28 Feb 03 31.4 Hours Average over 3 years 2000-2003 Control Periods 38.0 Hours Table 3: Winter Control Period Demand for the Past Nine Seasons Chargeable Major Customer Control Periods Season Control Period Covered Cumulative Duration Winter 1994 1 May 94 – 31 Aug 94 61.1 Hours Winter 1995 1 May 95 – 31 Aug 95 80.9 Hours Winter 1996 1 May 96 – 31 Aug 96 52.1 Hours Winter 1997 1 May 97 – 31 Aug 97 53.8 Hours Winter 1998 1 May 98 – 31 Aug 98 9.6 Hours Winter 1999 1 May 99 – 31 Aug 99 34.6 Hours Winter 2000 1 May 00 – 31 Aug 00 20.0 Hours Winter 2001 1 May 01 – 31 Aug 01 181.6 Hours Winter 2002 1 May 02 – 31 Aug 02 111 Hours Average over 9 years 1994-2002 Control Periods 67.2 Hours 33. Under Assessed Capacity (AC) charges, load management (which includes on-site distributed generation) can also be used to reduce AC costs. The AC cost is generally determined by the running average of the 12 highest half-hourly kVA demands that have occurred between 7.30am and 8.30pm weekdays for a prior fixed 12-month period. The pricing regime for major customers on the Orion distribution network is shown in table 4 below5. Table 4: Capacity Pricing Schedule for Major Customers in the Orion Network Pricing Definition Line Transmission Delivery Fixed (Connection) - $500.05 ----- - $500.05/year Control Period Demand - $60.00 - $21.92 - $81.92/kVA/year Assessed Capacity - $24.40 - $22.00 - $46.40/kVA/year (A “-” sign is used to indicate payment from the customer). Industrial Research Page 19 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources 34. Since AC is charged on the basis set out in paragraph 18, and apportioned on a daily basis over a 12-month period, a control algorithm must attempt to keep all peaks below a certain level to avoid AC costs creeping up, or attempt to clip demand peaks to achieve a lower average. When the diesel genset is used to reduce consumption, fuel is consumed at a cost higher than energy purchased from the network. Therefore excessive use of the genset to reduce capacity charges will result in negative overall savings. Customers who have taken measures to reduce their Assessed Capacity – even after its value has been set for the next 12-month period (see paragraph 18) – may still be able to make special arrangements with Orion Networks Limited for their daily apportioned AC value to be modified appropriately. CPD costs are charged on the average demand over all the control periods in the season. If the cumulative duration of total clip times is long, it may be more cost effective not to deliver full capacity at times to restrict diesel running costs through increased efficiency and reduces fuel costs, and this introduces some interesting profile dependent operating choices. 35. AC was calculated in this study before and after the impact of distributed generation on a major customer’ s demand was considered. Using 12 months of load profiling data from an experimental site, the impact of distributed generation on lowering the highest AC demand peaks was determined. Our analysis showed that – due to the shape of the load profile – by far the greatest cost-effective AC reduction occurred by clipping only the four highest peaks in the year. AC cost reduction gained by clipping more peaks was negligible. (In other words, an uneconomic amount of diesel was involved with clipping additional peaks in order to bring the AC cost down further). 36. The CPD schedule for the summer 2002-03 is given in table 5 below for reference. Table 5: Duration of Chargeable Control Period Demand for Major Customers during the Summer 2002-03 Season CPD Date Start Time End Time CPD Duration 11 Feb 2003 11:00 11:17 17 mins 07 Jan 2003 20:03 01:46 343 mins 07 Jan 2003 18:09 19:36 87 mins 06 Jan 2003 17:56 00:49 413 mins 05 Jan 2003 00:22 00:57 35 mins 04 Jan 2003 23:24 23:39 15 mins 03 Jan 2003 23:23 01:07 104 mins 03 Jan 2003 22:03 22:49 46 mins 03 Jan 2003 18:04 19:33 89 mins 03 Jan 2003 00:23 01:09 46 mins 02 Jan 2003 23:26 23:44 18 mins 01 Jan 2003 00:26 01:09 43 mins 30 Dec 2002 23:29 23:52 23 mins 26 Nov 00:42 00:42 0 mins 2002 07 Nov 17:50 21:14 204 mins 2002 06 Nov 23:20 01:23 123 mins 2002 06 Nov 17:57 22:51 294 mins 2002 Total 31 Hrs 40 Minutes Industrial Research Page 20 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources IPP Operating Scenario Energy Payment Options 37. An energy pricing schedule was proposed for use in this study (see table 1). Meridian Energy agreed in principle to pay the energy prices given in the table 1 energy pricing schedule, less a 10% administration charge on all prices: for every kWh of energy exported by a network-embedded generator at a major customer’ s site. This of course means that for the IPP scenario, a return for energy of 10% less than the MCLM case is available. The average energy export price under this arrangement for the pricing schedule given in table 1, is 4.92 cents / kWh (5.47 cents / kWh, less 10%), and this figure was used in the study. Capacity Payment Options 38. A pure IPP operator is not able to gain benefit from CPD and AC, because no significant demand is taken from the distribution network. Depending on their location in the network, network-embedded generators will operate under either a winter or summer season PPD regime. As for the CPD, the summer PPD applies to the predominantly rural portion of the network and the winter PPD applies to the mostly urban portion of the network. However the PPD times are different from the CPD periods discussed earlier (compare tables 5 and 9). 39. Summer PPD load management is between 1 October and 31 March, and winter load management is between 1 April to 30 September. Total seasonal duration of PPD is approximately two to three times the seasonal CPD duration, or up to approx-imately 450 hours5. This means that to collect PPD payments, a generator operating as an IPP must typically operate for 2 to 3 times as long as a generator operating under MCLM, but as can be seen by comparison between tables 4 & 8, the payments are substantially higher. Data for the past three summer and two winter PPD seasons was available for analysis. The total duration of chargeable half-hourly peak demand periods over the past three seasons for the summer seasonal PPD are shown in table 6 below. The total duration of chargeable half- hourly peak demand periods over the past two seasons for the winter seasonal PPD are shown in table 7 below. Table 6: Summer Peak Period Demand for the Past Three Seasons Chargeable Peaks for Generator Capacity Pricing Season Peak Period Covered Cumulative Duration Summer 2000-01 1 Oct 00 – 31 Mar 01 307.5 Hours Summer 2001-02 1 Oct 01 – 31 Mar 02 53 Hours Summer 2002-03 1 Oct 02 – 31 Mar 03 108.5 Hours Average over 3 years 2000-2003 Peak Periods 156.3 Hours Industrial Research Page 21 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Table 7: Winter Peak Period Demand for the Past Two Seasons Chargeable Peaks for Generator Capacity Pricing Season Peak Period Covered Cumulative Duration Winter 2001 1 Apr 01 – 30 Sep 01 318.5 Hours Winter 2002 1 Apr 02 – 30 Sep 02 233 Hours Average for 2 years 2001-2003 Peak Periods 275.8 Hours 40. The summer PPD tends to be less than the winter PPD, as borne out by the results displayed in tables 6 and 7 for the past two or three seasons. Any fuel-driven generator will therefore cost more to run under the winter PPD. Furthermore, the Grid Exit Points (GXPs) associated with summer PPD correspond to predominantly rural regions of the distribution network, while the GXPs associated with winter PPD correspond to urban regions where planning consents for this technology combination may be much harder to acquire and utilise. In this study, the site selected was connected to a summer PPD GXP. 41. The target network peak demand for summer and winter is set in consultation between Orion and the retailers. A load group refers to one or more controllable water heating ripple channels, and is part of the total “ controllable load” contributing to the peak demand. The energy and capacity drawn from the grid at different times by these controllable loads can be varied to accommodate network peak demand constraints. Having a number of controllable load groups enables Orion to manage / minimise the peak demand. The PPD consists of chargeable half-hours when one or more load groups has been shed for more than 15 minutes. This information is used in billing calculations for general customers. Some load groups (primarily residential water heating) are controlled directly by the Orion load management system, and other load groups are controlled by individual customers, in response to pricing signals. 42. The current pricing regime for network-embedded generators, wanting to sell capacity to the Orion distribution network is shown in table 8 below5: Table 8: Capacity Pricing Schedule for Network-Embedded Generators in the Orion Network Pricing Definition Line Transmission Delivery Fixed (Connection): GC - $0.00 ----- - $0.00/year Fixed (Connection): MC - $500.05 ----- - $500.05/year Peak Period Demand $66.70 $33.30 $100.00/kVA/year Real power pricing schedule for generators larger than 30kW – go to http://www.oriongroup.co.nz/ for full details GC = General Customers. MC = Major Customers. (A “+” sign indicates payment to the customer). Under this proposal, Orion will pay an Independent Power Producer (IPP) connected to the appropriate network area for either summer or winter peak period generation based on the average capacity provided over the season at a rate of $100/kVA/year5. (The charge for connecting a Major Customer’ s generator to the Industrial Research Page 22 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources network is $500.05/year). In this study, the actual PPD schedules for the last three summers were used to determine when the diesel generator had to operate to receive PPD payments. The IDES tools3 developed for this purpose, used 10-minute intervals to match the diesel generator’ s operating schedule and the wind energy available with the PPD schedule for the season investigated. (The PPD schedule for summer 2002-03 is given in table 9 below for reference). The average wind capacity available over the PPD schedule for the season was used to calculate the PPD payments from the wind generation. The diesel component of the DG-WTG Hybrid system was used to cover only the capacity shortfall, when the wind turbine could not provide 550kW of capacity during any time period in the PPD schedule. Operating efficiency of the diesel genset was adjusted depending on its operating level (kW output delivered). Table 9: Duration of Chargeable Peak Half-hours for Summer 2002-03 PPD Season (108.5 hours in Total) Date Half Hour Ending Duration Cumulative Thu, 27-3-2003 19:30, 20:00, 20:30, 21:00, 21:30, 22:00 3.0 108.5 Wed, 26-03-2003 19:30, 20:00, 20:30, 21:00, 21:30, 22:00, 22:30 3.5 105.5 Tue, 25-03-2003 19:00, 19:30, 20:00, 20:30, 21:00, 21:30, 22:00, 22:30, 23:00 4.5 102.0 Mon, 24-03-2003 19:30, 20:00, 20:30, 21:00, 21:30, 22:00, 22:30, 23:00, 24:00 4.5 97.5 Fri, 21-03-2003 20:00, 20:30, 21:00, 21:30 2.0 93.0 Thu, 20-03-2003 19:30, 20:00, 20:30, 21:00, 21:30, 22:00, 22:30 3.5 91.0 Wed, 19-03-2003 20:00, 20:30, 21:00, 21:30, 22:00, 22:30 3.0 87.5 Tue, 18-03-2003 20:00, 20:30, 21:00, 21:30, 22:00, 22:30, 23:00 3.5 84.5 Mon, 17-03-2003 18:30, 19:00, 19:30, 20:00, 20:30, 21:00, 21:30, 22:00, 22:30, 23:00 5.0 81.0 Fri, 14-03-2003 01:00 0.5 76.0 Thu, 13-03-2003 21:30, 22:00, 22:30, 23:00 2.0 75.5 Wed, 12-03-2003 01:00, 01:30 1.0 73.5 Tue, 11-03-2003 01:00, 20:00, 20:30, 21:00, 21:30, 22:00, 22:30, 23:00, 24:00 4.5 72.5 Mon, 10-03-2003 18:00, 18:30, 19:00, 19:30, 21:00, 21:30, 22:00, 22:30, 23:00, 24:00 5.0 68.0 Fri, 7-03-2003 01:00 0.5 63.0 Thu, 6-03-2003 17:00, 17:30, 18:00, 18:30, 19:00, 21:30, 22:00, 22:30 4.0 62.5 Thu, 13-02-2003 22:30 0.5 58.5 Mon, 10-02-2003 17:30, 18:00, 18:30, 22:30 2.0 58.0 Wed, 8-01-2003 00:30, 01:00, 01:30, 02:00, 02:30 2.5 56.0 Tue, 7-01-2003 00:30, 01:00, 01:30, 18:30, 19:00, 19:30, 20:00, 20:30, 21:00, 7.5 53.5 21:30, 22:00, 22:30, 23:00, 23:30, 24:00 Mon, 6-01-2003 18:00, 18:30, 19:00, 19:30, 20:00, 20:30, 21:00, 21:30, 22:00, 6.5 46.0 22:30, 23:00, 23:30, 24:00 Sat, 4-01-2003 00:30 0.5 39.5 Fri, 3-01-2003 01:00, 01:30, 17:30, 18:00, 18:30, 19:00, 19:30, 20:00, 20:30, 6.5 39.0 22:30, 23:00, 23:30, 24:00 Thu, 2-01-2003 22:30, 23:00, 24:00 1.5 32.5 Wed, 1-01-2003 00:30, 01:00, 01:30 1.5 31.0 Tue, 31-12-2002 00:30, 01:00, 17:00, 17:30, 18:30, 19:00, 24:00 3.5 29.5 Mon, 30-12-2002 19:00, 22:30, 23:00, 24:00 2.0 26.0 Fri, 27-12-2002 24:00 0.5 24.0 Tue, 24-12-2002 01:00, 24:00 1.0 23.5 Mon, 11-11-2002 18:30, 19:00, 19:30, 20:00, 20:30 2.5 22.5 Thu, 7-11-2002 00:30, 01:00, 01:30, 02:00, 18:00, 18:30, 19:00, 19:30, 20:00, 6.5 20.0 20:30, 21:00, 21:30, 22:00 Wed, 6-11-2002 15:30, 16:00, 16:30, 18:30, 19:00, 19:30, 20:00, 20:30, 21:00, 7.5 13.5 21:30, 22:00, 22:30, 23:00, 23:30, 24:00 Tue, 5-11-2002 17:30, 22:00 1.0 6.0 Mon, 4-11-2002 22:00, 22:30 1.0 5.0 Wed, 23-10-2002 16:30, 17:00 1.0 4.0 Tue, 22-10-2002 17:30, 18:00, 18:30, 19:00, 19:30, 20:00 3.0 3.0 Industrial Research Page 23 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Other Potential Benefits Available from these Technology Combinations Scenarios Cogeneration Heat 43. Depending on the duration of the capacity delivery periods, substantial heat output is available from the diesel engine described in the scenarios (see figures 1 and 2). In some circumstances it may be worthwhile making use of this heat for water or space heating applications. The specialised tools and methodologies used in this study3 can be used to calculate the additional value to the operation of using this heat. This value was not considered in these scenarios. Standby Plant 43. In some customer applications the loss of service can be very costly. If the appropriate controls and switching arrangements are in place, this technology can be used to provide continuous capacity in the event of a grid supply failure. If the cost of downtime is higher than the average cost of running for example: a diesel generator operating continuously at the required capacity – additional risk reduction returns are available. This value was not considered in these scenarios. High GXP Prices 44. The diesel genset can be used to generate additional revenues (which may be considerable) whenever the wholesale price-linked contract price rises above the cost of operating the generator (for example, when national energy transmission constraints or energy generation shortfalls occur). This value was not considered in these scenarios. Industrial Research Page 24 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Resource Inputs 45. Wind: A wind energy data set of 10-minute values over a complete year was used with 5, 7, 9 m/s average wind speeds. Diesel: The diesel price used was 70.3c/litre delivered. For other model inputs and assumptions see Appendix One. Industrial Research Page 25 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Main Results of the Scenarios MCLM Operating Scenario 46. For the DG-Only option, the simulation models were set to calculate the financial return based on a full 550kW of capacity being delivered from the diesel generator during the CPD schedule and for the four highest AC peaks (see paragraph 35). For the DG-WTG Hybrid option, 550 kW combined generation capacity from wind and diesel was delivered during the CPD schedule and for the four highest AC peaks (with diesel generation meeting the capacity shortfall when less than 550kW of wind turbine capacity was available). A summary of the results for the MCLM operating scenario is given under two different financing arrangements: (a) 10% interest on borrowing all required funds from the bank (see table 10) and 0% interest (see table 11) for example as a result of channelling corporate funds directly into the project. Table 10: Summary of Annual MCLM Operating Scenario Results for 10% Interest on Finance over 20 years at an average annual wind speed of 7m/s MCLM Scenario Results System Used CPD Season CPD Length Finance IRR Payback 550kW WTG-Only Summer 2001-02 1.6 Hrs 10% int. 11.93% 7.73 yrs 550kW WTG-Only Summer 2002-03 31.4 Hrs 10% int. 10.90% 8.25 yrs 550kW WTG-Only Summer 2000-01 81.1 Hrs 10% int. 9.43% 9.11 yrs 550kW DG-WTG Hybrid Summer 2001-02 1.6 Hrs 10% int. 9.56% 9.03 yrs 550kW DG-WTG Hybrid Summer 2002-03 31.4 Hrs 10% int. 9.45% 9.09 yrs 550kW DG-WTG Hybrid Summer 2000-01 81.1 Hrs 10% int. 9.20% 9.23 yrs 550kW DG-Only Summer 2001-02 1.6 Hrs 10% int. 24.99% 4.05 yrs 550kW DG-Only Summer 2002-03 31.4 Hrs 10% int. 24.38% 4.14 yrs 550kW DG-Only Summer 2000-01 81.1 Hrs 10% int. 23.25% 4.31 yrs Table 11: Summary of Annual MCLM Operating Scenario Results for 0% Interest on Finance over 20 years at an average annual wind speed of 7m/s MCLM Scenario Results System Used CPD Season CPD Length Finance IRR Payback 550kW WTG-Only Summer 2001-02 1.6 Hrs 0% int. 20.47% 4.85 yrs 550kW WTG-Only Summer 2002-03 31.4 Hrs 0% int. 19.55% 5.06 yrs 550kW WTG-Only Summer 2000-01 81.1 Hrs 0% int. 18.29% 5.37 yrs 550kW DG-WTG Hybrid Summer 2001-02 1.6 Hrs 0% int. 18.39% 5.34 yrs 550kW DG-WTG Hybrid Summer 2002-03 31.4 Hrs 0% int. 18.31% 5.36 yrs 550kW DG-WTG Hybrid Summer 2000-01 81.1 Hrs 0% int. 18.11% 5.41 yrs 550kW DG-Only Summer 2001-02 1.6 Hrs 0% int. 32.84% 3.08 yrs 550kW DG-Only Summer 2002-03 31.4 Hrs 0% int. 32.27% 3.13 yrs 550kW DG-Only Summer 2000-01 81.1 Hrs 0% int. 31.21% 3.23 yrs The above tables summarise the median wind speed case. At first glance it appears that the diesel generator only option provides the best return. But this is a high-risk Industrial Research Page 26 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources option dependent on CPD payments alone and low CPD hours for income. The wind-diesel hybrid provides lower risk by spreading income between capacity and energy and is more attractive at higher wind speed. 47. For the three summer CPD seasons evaluated (see table 2) the WTG energy contributions as a percentage of the total kWh of energy supplied from the hybrid combination, will vary significantly with wind speed when the total CPD hours per season is high. In other words, when the wind speed is low and the CPD hours are high, the wind energy percentage drops because more diesel generation is necessary to maintain the capacity during the control demand periods. The effect of this is to reduce the IRR on all options. (A more detailed list of assumptions is given in appendix one). Table 12 shows how much energy would be supplied from the wind at different average wind speeds. Even at 5m/s, 88.45% of the annual energy supplied still comes from the wind, for the longest CPD season (81.1 hours), although in this case the WTG-Only IRR is reduced from 18.29% (0% financing – see table 11) at a 7m/s annual wind speed, to 6.18% (0% financing) at a 5m/s annual wind speed. Table 12: MCLM Scenario Results for Energy Contributions (% kWh) from Wind Energy CPD Hours MCLM Wind @ 5m/s MCLM Wind @ 7m/s MCLM Wind @ 9m/s / Season 1.6 Hrs 99.52% (876 kWh) 99.80% (878 kWh) 99.82% (878 kWh) 31.4 hrs 95.56% (16,503 kWh) 98.11% (16,944 kWh) 98.89% (17,078 kWh) 81.1 Hrs 88.45% (39,453 kWh) 94.54% (42,170 kWh) 96.41% (43,004 kWh) 48. The contribution of CPD capacity and AC support towards the MCLM scenario’ s total annual earnings can be essential to the economic viability of the distributed generation system. Table 13 provides a summary of the energy and capacity contributions towards the annual earnings under different annual wind speeds for the technology options investigated in this study. (These results are presented graphically in figure 4). The values in parentheses represent what the payback periods would have been without any earnings contributions from the CPD and AC. Table 13: MCLM Scenario Results for 0% Interest on Finance (For a Typical CPD Season: Summer 2002-03) Option kWh / year CPD kW Earnings AC&CPD Earnings: Payback - capacity from kWh earnings total per without kW Support energy / yr per year year earnings in ( ) WTG-Only @ 5m/s 926,202 119 $50,663 $21,334 $71,997 10.69 (> 20) WTG-Only @ 7m/s 1,683,957 245 $92,112 $37,803 $129,915 5.06 (8.64) WTG-Only @ 9m/s 2,144,492 334 $117,304 $47,005 $164,309 3.85 (6.33) DG-WTG @ 5m/s 969,222 550 $53,016 $58,747 $111,763 8.27 (> 20) DG-WTG @ 7m/s 1,716,421 550 $93,888 $64,882 $158,770 5.36 (13.64) DG-WTG @ 9m/s 2,168,484 550 $118,616 $66,802 $185,418 4.48 (9.29) DG-Only 52,941 550 $2,896 $48,394 $51,290 3.13 (> 20) Industrial Research Page 27 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources 49. The table 13 results indicate that economic viability without capacity support is not simply dependent on the energy to capacity earnings ratio, as shown by: WTG-Only @ 5m/s (70% energy to 30% capacity earnings ratio); DG-WTG Hybrid @ 5m/s (47% energy to 53% capacity earnings ratio); and, DG-Only (6% energy to 94% capacity earnings ratio). Note that a relatively high WTG-Only capacity support ranging from 119kW to 334kW was obtained. This contribution was achieved because the Orion payment model allows average capacity to be rewarded. If only the minimum capacity at anytime during the CPD was rewarded, then the option would provide much lower financial returns. The equivalent value (in cents / kWh) of the capacity earnings are given in table 14 below. This table shows how much energy has to be delivered in order to avail of the potentially high earnings possible from delivering capacity. Figure 4 shows the above results in graphical form, which demonstrates the highest revenues (savings) come from the wind-diesel combinations but this does not necessarily give the best ROI. Comparison of MCLM Energy and Capacity Savings for the Summer 2002-03 CPD Season $200,000 $180,000 $160,000 Annual Savings $140,000 AC & CPD Savings from Capacity / yr $120,000 $100,000 $80,000 Savings from Energy / yr $60,000 $40,000 $20,000 $0 WTG- WTG- WTG- DG-WTG DG-WTG DG-WTG DG-Only Only @ Only @ Only @ Hybrid @ Hybrid @ Hybrid @ 5m/s 7m/s 9m/s 5m/s 7m/s 9m/s Distributed Generation Option Figure 4: Graphical Comparison of the Earnings Contributions from Energy and Capacity for the MCLM Scenario 50. Table 14 shows that the equivalent value in cents / kWh of capacity supplied by distributed generation systems can be quite substantial, over fairly small periods of time. (Table 14 results are presented graphically in figure 5). It is interesting to note that there is some correlation between high wholesale electricity pricing periods and capacity demand periods. As a result, the overall averaged c/kWh equivalent earnings is more than likely to be significantly higher than figures quoted in table 14. Industrial Research Page 28 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Table 14: Equivalent Earnings in c/kWh Energy for the MCLM Scenario Results in Table 13 Option % Earnings from % Earnings from capacity Averaged c/kWh energy supplied supplied (see note* below): equivalent earnings @ 5.47cents/kWh % Earnings @ Equivalent c/kWh Capacity Total WTG-Only @ 5m/s 70% 30% 536.76 2.30 7.77 WTG-Only @ 7m/s 71% 29% 461.97 2.24 7.71 WTG-Only @ 9m/s 71% 29% 421.36 2.19 7.66 DG-WTG @ 5m/s 47% 53% 319.80 6.06 11.53 DG-WTG @ 7m/s 59% 41% 353.20 3.78 9.25 DG-WTG @ 9m/s 64% 36% 363.65 3.08 8.55 DG-Only 6% 94% 263.44 91.41 96.88 * Note: % earnings from capacity supplied in equivalent c/kWh has been calculated from the actual kWh used in order to provide capacity support, in this case: 31.4 hours CPD (Summer 2002-03) + 4 hours AC Equivalent Earnings in c/kWh for the MCLM Scenario Results Extended to approx. 97c/kWh (Total) 25 Averaged c/kWh Equivalent Earnings 20 15 Averaged capacity earnings Averaged energy earnings 10 5 0 WTG-Only WTG-Only WTG-Only DG-WTG DG-WTG DG-WTG DG-Only @ 5m/s @ 7m/s @ 9m/s Hybrid @ Hybrid @ Hybrid @ 5m/s 7m/s 9m/s Distributed Generation Option Figure 5: Equivalent Total Earnings in c/kWh for the Energy and Capacity Components for MCLM IPP Operating Scenario 51. For the DG-Only option, the simulation models were set to calculate the financial return based on a full 550kW of capacity being delivered from the diesel generator during the PPD schedule. For the DG-WTG Hybrid option, 550 kW combined generation capacity from wind and diesel was delivered during the PPD schedule (with diesel generation meeting the capacity shortfall when less than 550kW of wind turbine capacity was available). Tables 15 and 16 provide a summary of the IPP operating scenario results under two different financing arrangements: (a) 10% interest on borrowing all required funds from the bank (see table 15) and 0% interest (see table 16) for example as a result of channelling corporate funds directly into the project. Industrial Research Page 29 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Table 15: Summary of Annual IPP Operating Scenario Results for 10% Interest on Finance over 20 years at an average annual wind speed of 7m/s IPP Scenario Results System Used PPD Season PPD Length Finance IRR Payback 550kW WTG-Only Summer 2001-02 53 Hrs 10% int. 4.45% 13.32 yrs 550kW WTG-Only Summer 2002-03 108.5 Hrs 10% int. 5.70% 12.03 yrs 550kW WTG-Only Summer 2000-01 307.5 Hrs 10% int. 5.76% 11.97 yrs 550kW DG-WTG Hybrid Summer 2001-02 53 Hrs 10% int. 4.59% 13.15 yrs 550kW DG-WTG Hybrid Summer 2002-03 108.5 Hrs 10% int. 4.35% 13.40 yrs 550kW DG-WTG Hybrid Summer 2000-01 307.5 Hrs 10% int. 3.36% 14.51 yrs 550kW DG-Only Summer 2001-02 53 Hrs 10% int. 28.98% 3.50 yrs 550kW DG-Only Summer 2002-03 108.5 Hrs 10% int. 27.53% 3.66 yrs 550kW DG-Only Summer 2000-01 307.5 Hrs 10% int. 22.10% 4.42 yrs It is interesting to note that the Wind only generation option performs better at higher PPD durations. This is purely coincidental with longer PPD durations representing PPD schedules corresponding to higher wind capacity factors for the wind profiles used. Table 16: Summary of Annual IPP Operating Scenario Results for 0% Interest on Finance over 20 years at an average annual wind speed of 7m/s IPP Scenario Results System Used PPD Season PPD Length Finance IRR Payback 550kW WTG-Only Summer 2001-02 53 Hrs 0% int. 14.26% 6.63 yrs 550kW WTG-Only Summer 2002-03 108.5 Hrs 0% int. 15.22% 6.29 yrs 550kW WTG-Only Summer 2000-01 307.5 Hrs 0% int. 15.26% 6.27 yrs 550kW DG-WTG Hybrid Summer 2001-02 53 Hrs 0% int. 14.38% 6.58 yrs 550kW DG-WTG Hybrid Summer 2002-03 108.5 Hrs 0% int. 14.21% 6.63 yrs 550kW DG-WTG Hybrid Summer 2000-01 307.5 Hrs 0% int. 13.54% 6.86 yrs 550kW DG-Only Summer 2001-02 53 Hrs 0% int. 36.80% 2.75 yrs 550kW DG-Only Summer 2002-03 108.5 Hrs 0% int. 35.41% 2.85 yrs 550kW DG-Only Summer 2000-01 307.5 Hrs 0% int. 30.29% 3.28 yrs 52. Based on the PPD hours experienced, table 17 gives an indication of how much energy would have been supplied from the wind resource at different average wind speeds. Even at 5m/s, 73.57% of the annual energy supplied still comes from the wind, for the longest PPD season (307.5 hours), although in this case the WTG- Only IRR is reduced from 15.26% (0% financing – see table 17) at a 7m/s annual wind speed, to 4.15% (0% financing) at a 5m/s annual wind speed. Table 17: IPP Scenario Results for Energy Contributions (% kWh) from Wind Energy PPD Hours IPP Wind @ 5m/s IPP Wind @ 7m/s IPP Wind @ 9m/s 53 Hrs 93.80% (27,343 kWh) 97.39% (28,389 kWh) 98.22% (28,631 kWh) 108.5 Hrs 89.01% (53,117 kWh) 95.30% (56,870 kWh) 96.69% (57,700 kWh) 307.5 Hrs 73.57% (124,425 kWh) 87.93% (148,712 kWh) 91.70% (155,088 kWh) 53. The contribution of PPD capacity support towards the IPP scenario’ s total annual earnings can be essential to the economic viability of the distributed generation system selected. Table 18 provides a summary of the energy and capacity Industrial Research Page 30 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources contributions towards the annual earnings under different annual wind speeds for the technology options investigated in this study. (These results are presented graphically in figure 6). The values in parentheses represent what the payback periods would have been without any earnings contributions from the PPD. Table 18: IPP Scenario Results for 0% Interest on Finance (For a Typical PPD Season: Summer 2002-03) Option kWh / year PPD kW Earnings PPD Earnings: Payback - capacity from kWh earnings total per without kW Support energy / yr per year year earnings in ( ) WTG-Only @ 5m/s 926,202 187 $45,597 $18,208 $63,805 13.19 (> 20) WTG-Only @ 7m/s 1,683,957 296 $82,901 $29,138 $112,039 6.29 (10.47) WTG-Only @ 9m/s 2,144,492 327 $105,573 $32,179 $137,752 4.95 (7.56) DG-WTG @ 5m/s 1,040,551 550 $51,226 $54,500 $105,726 > 20 (> 20) DG-WTG @ 7m/s 1,767,078 550 $86,993 $54,500 $141,493 6.63 (18.76) DG-WTG @ 9m/s 2,217,994 550 $109,192 $54,500 $163,692 5.59 (11.83) DG-Only 161,805 550 $7,966 $54,500 $62,466 2.85 (> 20) 54. The table 19 results indicate (once again – see paragraph 49) that economic viability without capacity support is not simply dependent on the energy to capacity savings ratio, as shown by: WTG-Only @ 5m/s (71% energy to 29% capacity savings ratio); DG-WTG Hybrid @ 5m/s (48% energy to 52% capacity savings ratio); and, DG-Only (13% energy to 87% capacity savings ratio). Note again (see paragraph 48) that a relatively high WTG-Only capacity support ranging from 187kW to 327kW was obtained. This contribution was achieved because the Orion payment model allows average capacity to be rewarded. If only the minimum capacity at anytime during the PPD was rewarded, then the option would provide much lower financial returns. The equivalent value (in cents / kWh) of the capacity earnings are given in table 19 below. This table shows how much energy has to be delivered in order to avail of the potentially high earnings possible from delivering capacity. Comparison of IPP Energy and Capacity Revenues for the Summer 2002-03 PPD Season $180,000 $160,000 $140,000 Annual Savings $120,000 $100,000 PPD Revenue from Capacity / yr $80,000 Revenue from Energy / yr $60,000 $40,000 $20,000 $0 WTG- WTG- WTG- DG-WTG DG-WTG DG-WTG DG-Only Only @ Only @ Only @ Hybrid @ Hybrid @ Hybrid @ 5m/s 7m/s 9m/s 5m/s 7m/s 9m/s Distributed Generation Option Figure 6: Graphical Comparison of the Earnings Contributions from Energy and Capacity for the IPP Scenario Industrial Research Page 31 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources 55. Table 19 shows that the equivalent value in cents / kWh of capacity supplied by distributed generation systems can be quite substantial, over fairly small periods of Table 19: Equivalent Earnings in c/kWh Energy for the IPP Scenario Results in Table 18 Option % Earnings from % Earnings from capacity Averaged c/kWh energy supplied supplied (see note** below): equivalent earnings @ 4.92cents/kWh % Earnings @ Equivalent c/kWh Capacity Total WTG-Only @ 5m/s 71% 29% 89.74 1.97 6.89 WTG-Only @ 7m/s 74% 26% 90.73 1.73 6.65 WTG-Only @ 9m/s 77% 23% 90.70 1.50 6.42 DG-WTG @ 5m/s 48% 52% 91.33 5.24 10.16 DG-WTG @ 7m/s 61% 39% 91.33 3.08 8.01 DG-WTG @ 9m/s 67% 33% 91.33 2.46 7.38 DG-Only 13% 87% 91.33 33.68 38.61 ** Note: % earnings from capacity supplied in equivalent c/kWh has been calculated from the actual kWh used in order to provide capacity support, in this case: 108.5 hours PPD (Summer 2002-03) time. (Table 19 results are presented graphically in figure 7). Once again (see paragraph 50), it is interesting to note that there is some correlation between high wholesale electricity pricing periods and capacity demand periods. As a result, therefore, overall averaged c/kWh equivalent earnings is more than likely to be significantly higher than figures quoted in table 19. Equivalent Earnings in c/kWh for the IPP Scenario Results Extended to approx. 45c/kWh (Total) 25 Averaged c/kWh Equivalent Earnings 20 15 Averaged capacity earnings Averaged energy earnings 10 5 0 WTG-Only WTG-Only WTG-Only DG-WTG DG-WTG DG-WTG DG-Only @ 5m/s @ 7m/s @ 9m/s Hybrid @ Hybrid @ Hybrid @ 5m/s 7m/s 9m/s Distributed Generation Option Figure 7: Equivalent Total Earnings in c/kWh for the Energy and Capacity Components for IPP Industrial Research Page 32 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Comparing the MCLM and IPP Operating Scenarios 56. The results from tables 10 and 15 have been summarised in figure 8 below. Figure 8 shows the Internal Rate of Return (IRR) trends against the different annual total CPD or PPD hours for an average wind speed of 7 m/s, for the IPP and MCLM operating scenarios. IRR Based on Seasonal Control (MCLM / Load Mgt) or Peak (IPP / Generator) Period Duration, with 10% Interest on Finance with WTG Priced at $2,725/kW over Life 30% DG-Only (Generator) 25% DG-Only (Load Mgt) Internal Rate of Return (IRR) 20% DG & WTG @ 7m/s (Generator) DG & WTG @ 7m/s (Load Mgt) 15% WTG-Only @ 7m/s (Generator) 10% WTG-Only @ 7m/s (Load Mgt) Poly. (DG-Only (Generator)) 5% Linear (DG-Only (Load Mgt)) Linear (WTG-Only @ 0% 7m/s (Load Mgt)) -5% 0 100 200 300 400 500 600 700 800 900 1000 Seasonal Peak / Control Demand Period Duration Figure 8: Comparison of the IRR for the different IPP and MCLM Scenarios 57. Figure 8 shows that, because of the higher fuel content and its cost relative to wind, the longer the seasonal CPD or PPD period, the lower the Internal Rate of Return (IRR). There is no direct relationship between speed and the length of the seasonal CPD or PPD period. In the examples used in this study, the increase in CPD duration corresponded with a decrease in the seasonal average availability of wind; while, the increase in PPD duration corresponded with an increase in the seasonal average availability of wind. Further investigation of average wind speed variations over extended periods of time can be conducted using Monte Carlo simulation methods. (See tables 5 and 9 to compare the CPD and PPD for Summer 2002-03). 58. It is interesting to note that the diesel generator option (DG-Only), operating to provide capacity support to the network under the IPP operating scenario, provides the best financial returns overall. For the DG-WTG Hybrid combination, a modest financial return is provided at a 7m/s average wind speed for the MCLM operating scenario but the IPP operating scenario provided a poor result due to the reduced income gained from capacity savings in the MCLM mode. . For the WTG-Only option, the IPP operating scenario financial returns are poor due to limited capacity support, but the MCLM operating scenario provides better – even attractive financial returns because of the higher value of the energy delivered. From these results, it appears that predictable dispatchable distributed generation is best suited Industrial Research Page 33 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources for IPP applications under the market pricing conditions considered, and that unpredictable distributed generation availability is best applied to the MCLM operating scenario. 59. The effect of different wind speeds and different CPD / PPD durations on wind speed are shown in figure 9 below. Linear extrapolation of the wind energy contributions from the MCLM and IPP Operating Scenario results for the wind- diesel hybrid scenarios given in tables 12 and 17, are shown. Percentage WInd Energy (kWh) Contribution Under Different Annual Wind Speed Regimes and MCLM and IPP Operating Scenarios 100% MCLM Wind @ 5m/s 95% MCLM Wind @ 7m/s 90% MCLM Wind @ 9m/s Contribution from Wind IPP Wind @ 5m/s Percentage Energy 85% IPP Wind @ 7m/s IPP Wind @ 9m/s 80% Linear (MCLM Wind @ 9m/s) 75% Linear (MCLM Wind @ 7m/s) Linear (MCLM Wind @ 5m/s) 70% Linear (IPP Wind @ 9m/s) Linear (IPP Wind @ 7m/s) 65% Linear (IPP Wind @ 5m/s) 60% 0 50 100 150 200 250 300 350 400 450 CPD / PPD Duration (Hours / Season = Hours / Year) Figure 9: Wind Energy Contributions under the Various IPP and MCLM Operating Scenarios Wind generation contributions to the total electricity delivered (in kWh) in the DG- WTG Hybrid systems are substantial. If the renewable wind energy contribution of the total electricity delivered by the DG-WTG Hybrid was rewarded via a new renewable energy incentive, the financial returns could be significantly better. Furthermore, biodiesel could be used to power the genset and deliver 100% renewable energy from this distributed generation combination. 60. The influence of wind speed on the financial viability of the wind-driven distributed generation options is shown below in figure 10. The trends are plotted for a 0% interest rate on borrowing because at a 10% interest level on borrowing, the IRR on the 5m/s wind speed case is not computable. Figure 6 clearly shows that at low wind speeds, (6m/s or less average annual wind speeds) the DG-WTG Hybrid systems are more economical to operate than the WTG-Only systems. This situation is reversed as annual average wind speeds rise above 7m/s. This indicates that the economic consumption of diesel in the DG-WTG Hybrid system is very much tied to the prevailing wind speed and the cumulative seasonal CPD / PPD duration. Financial returns are attractive for wind speeds above 7m/s. The DG- WTG Hybrid combination shows best results at low wind speeds because the diesel Industrial Research Page 34 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources generator component delivers better financial returns on capacity support. At high wind speeds, the energy delivered at lower cost from the wind generator by itself, provides the best financial returns for the MCLM operating scenario. Comparison of Wind-Only and DG-Wind Hybrid Results for the MCLM and IPP Operating Scenarios, Using CPD and PPD Data for Summer 2002-03 30.00% Internal Rate of Return (IRR) with 0% 25.00% Interest on Finance 20.00% Wind-Only MCLM Wind-Only IPP 15.00% DG-Wind Hybrid MCLM DG-Wind Hybrid IPP 10.00% 5.00% 0.00% 5 7 9 Average Annual Wind Speed (m/s) Figure 10: The Influence of Wind Speed on Wind-Only and DG-WTG Hybrid Distributed Generation Options 61. A sample summary spreadsheet with a breakdown of all the costs determined for the scenarios examined in this study, is shown in figure 11 below. ENERGY Firm kW Firm kWh / yr WTG Total kWh/yr WTG kWh/yr Export Diesel kWh/yr Export PRODUCTION 550 59,675 1,683,956.70 1,683,956.70 83,121.04 INCOME OR Retail Firm $ / kWh Buyback $ / kWh Savings / kVA / Yr Retail Savings / Yr Export Income / Yr Capacity Savings / Yr SAVINGS / YR $0.0547 $0.0492 $81.92 $0.00 $86,993.24 $65,499.95 ANNUAL WTG O&M / Year Diesel O&M / Year Fuel Cost / Year WTG O&M / Month (Eq.) Diesel O&M / Mth (Eq.) Fuel Cost / Mth O&M COSTS $20,207 $5,000 $5,569.11 $1,684 $417 $464.09 TOTAL CAPITAL WTG Price WTG Freight Cost WTG Install Cost Diesel Cost / kW No. of Units Diesel Size Diesel Cost BORROWED $320,000 $20,000 $124,826 $217 1 550 $119,422 FIRM CAPACITY REQUIREMENT 550 Fuel Inc. / yr 2% Revenue or Cost in $ / kWh $0.058 Principal $584,247.94 Fuel Cost / litre Inflation / yr 3% NPV $1,274,073.91 Term (yrs) 20 $0.7030 Discount % 5% 550 kW FIRM IRR 16.40% Days / pay 31 MJ/m3 (net) Breakdown IDES COMBINATION Payback (yrs) 5.89 Annual % 0.00000% 37,794 Borrowings WW-550 + Gensets ROI (average) 17.65% Daily % 0.00000% $/GJ WTG Purchase $320,000.00 WTG: $2,725/kW @ 7 M/S/YR Monthly Repay $2,012.41 $18.60 WTG Freight $20,000.00 SUMMER DEMAND CONTROL PERIODS - 2002/03 $/kWh O&M $0.012 Fuel ($ / kWh) WTG Install $124,826.00 Grid Inc./yr 1% $0.0670 Diesel Capital $119,421.94 NPV Year Annual Income Annual O&M Annual Fuel Annual Payments Total Annual Cost PW Factor Disc. Cost Firm kWh Prod. Disc. Prod. 1 $152,493.19 -$25,207.47 -$5,569.11 -$24,148.91 $97,567.70 0.952 $92,921.62 1,767,123.66 1,682,974.91 2 $154,018.12 -$25,963.69 -$5,680.49 -$24,148.91 $98,225.02 0.907 $89,092.99 1,767,123.66 1,602,833.25 3 $155,558.30 -$26,742.60 -$5,794.10 -$24,148.91 $98,872.68 0.864 $85,409.94 1,767,123.66 1,526,507.86 4 $157,113.88 -$27,544.88 -$5,909.98 -$24,148.91 $99,510.11 0.823 $81,867.21 1,767,123.66 1,453,817.01 5 $158,685.02 -$28,371.22 -$6,028.18 -$24,148.91 $100,136.70 0.784 $78,459.72 1,767,123.66 1,384,587.63 6 $160,271.87 -$29,222.36 -$6,148.75 -$24,148.91 $100,751.85 0.746 $75,182.58 1,767,123.66 1,318,654.88 7 $161,874.59 -$30,099.03 -$6,271.72 -$24,148.91 $101,354.92 0.711 $72,031.05 1,767,123.66 1,255,861.79 8 $163,493.34 -$31,002.00 -$6,397.16 -$24,148.91 $101,945.26 0.677 $69,000.57 1,767,123.66 1,196,058.85 9 $165,128.27 -$31,932.06 -$6,525.10 -$24,148.91 $102,522.19 0.645 $66,086.72 1,767,123.66 1,139,103.67 10 $166,779.55 -$32,890.02 -$6,655.60 -$24,148.91 $103,085.01 0.614 $63,285.25 1,767,123.66 1,084,860.64 11 $168,447.35 -$33,876.73 -$6,788.71 -$24,148.91 $103,632.99 0.585 $60,592.07 1,767,123.66 1,033,200.61 12 $170,131.82 -$34,893.03 -$6,924.49 -$24,148.91 $104,165.39 0.557 $58,003.19 1,767,123.66 984,000.58 13 $171,833.14 -$35,939.82 -$7,062.98 -$24,148.91 $104,681.43 0.530 $55,514.80 1,767,123.66 937,143.41 14 $173,551.47 -$37,018.01 -$7,204.24 -$24,148.91 $105,180.31 0.505 $53,123.20 1,767,123.66 892,517.53 15 $175,286.99 -$38,128.55 -$7,348.32 -$24,148.91 $105,661.20 0.481 $50,824.84 1,767,123.66 850,016.70 16 $177,039.86 -$39,272.41 -$7,495.29 -$24,148.91 $106,123.24 0.458 $48,616.28 1,767,123.66 809,539.71 17 $178,810.26 -$40,450.58 -$7,645.19 -$24,148.91 $106,565.56 0.436 $46,494.20 1,767,123.66 770,990.20 18 $180,598.36 -$41,664.10 -$7,798.10 -$24,148.91 $106,987.24 0.416 $44,455.41 1,767,123.66 734,276.38 19 $182,404.34 -$42,914.02 -$7,954.06 -$24,148.91 $107,387.34 0.396 $42,496.82 1,767,123.66 699,310.84 20 $184,228.38 -$44,201.44 -$8,113.14 -$24,148.91 $107,764.88 0.377 $40,615.45 1,767,123.66 666,010.32 21 $0.00 $0.00 $0.00 $0.00 $0.00 0.359 $0.00 0.00 0.00 22 $0.00 $0.00 $0.00 $0.00 $0.00 0.342 $0.00 0.00 0.00 23 $0.00 $0.00 $0.00 $0.00 $0.00 0.326 $0.00 0.00 0.00 24 $0.00 $0.00 $0.00 $0.00 $0.00 0.310 $0.00 0.00 0.00 25 $0.00 $0.00 $0.00 $0.00 $0.00 0.295 $0.00 0.00 0.00 Total $3,357,748.10 -$677,334.04 -$135,314.73 -$482,978.30 $2,062,121.04 $1,274,073.91 22,022,266.76 Figure 11: Project Financial Analysis Spreadsheet Industrial Research Page 35 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Further Discussion of Results 62. The figure 8 results show that the most economic IPP and MCLM operating scenarios, in descending order, are: (1) DG-Only IPP; (2) DG-Only MCLM; (3) WTG-Only MCLM; (4) DG-WTG Hybrid MCLM; (5) WTG-Only IPP; and, (6) DG-WTG Hybrid IPP (depending the length of the seasonal PPD). The wind energy contributions to the total electricity delivered (in kWh) by the DG-WTG Hybrid systems is substantial (see figure 9). If the wind energy contribution of the total electricity delivered by the DG-WTG Hybrid system was rewarded via a new renewables incentive, the financial returns could be significantly better especially where extended operating periods exist). Furthermore, biodiesel could be used to deliver 100% renewable energy from the plant. 63. Scenarios incorporating renewable energy delivery, will become more attractive for example, when WTG lifecycles cost are reduced through: (a) technology innovation; (b) economies of scale / production (impact of lower unit costs illustrated in figure 12); (c) renewable energy incentives (see figure 13); (d) further increases in grid energy prices; (e) an accurate reflection of Time-Of-Use pricing (see figure 14) in the buying and selling of electricity. Figure 12 shows how the wind generator combination maintains good returns in the event of longer seasonal peak period durations. It also shows that with lower WTG costs the returns quickly exceed the diesel only option. Internal Rate of Return (IRR) over 20 years, Based on Annual Diesel Genset Running Hours, Operating as a Load Manager 60% 50% Internal Rate of Return (IRR) 0% Financing: DG-Only 40% 0% Financing: DG & WTG @ 7m/s ($2,725/kW) 0% Financing: DG & WTG @ 7m/s 30% ($2,248/kW) 0% Financing: DG & WTG @ 7m/s ($1,772/kW) 20% 0% Financing: DG & WTG @ 7m/s ($1,296/kW) 10% 0% 0 200 400 600 800 1000 Annual Run Time (Hours) Figure 12: Influence of Reducing WTG Lifecycle Costs on the Internal Rate of Return 64. In figure 13, the economic performance of a biodiesel-wind (BDG-WTG Hybrid) hybrid system with various incentives, is compared with that of a diesel generator only taxed at the same rate for greenhouse gas emissions. Industrial Research Page 36 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Influence of Renewable Energy Incentives on the Financial Viability of DG-WTG Hybrid Investments (Lifecycle cost = $2.725/kW) with 0% Financing 40% 35% Internal Rate of Return (IRR) 30% DG-Only Basecase 25% DG-Only -1c/kWh Carbon Tax DG-Only -2c/kWh Carbon Tax 20% BDG-WTG Hybrid Basecase BDG-WTG Hybrid +1c/kWh Incentive 15% BDG-WTG Hybrid +2c/kWh Incentive 10% 5% 0% 0 100 200 300 400 500 600 700 Operating Period (Hours) Figure 13: Influence of Renewable Energy Subsidies on the Financial Viability of DG-WTG Hybrid Systems 65. Figure 9 shows that the kWh energy contributions from the renewable wind resource can be substantial, and hence a cent / kWh incentive for renewable energy (see figure 13) can make a significant difference to the financial viability of kWh energy delivered by wind under existing market conditions. In terms of capacity- support for the distribution network however, individual WTG capacity on its own cannot be guaranteed, and hence cannot provide anytime firm capacity support to the lines company. 66. Figure 14 shows the relationship between the target capacity level delivered, and the ability of the WTG to supply this capacity continuously over the year (for 8760 Continuous (8760 Hours / Year) Firm Load Contributions from a Variable Capacity (10 - 550kW) Diesel Genset Operating with a 550kW WTG at Different Annual Wind Speeds 100% 90% kWh / Year Contributing Towards Firm 80% 70% Load Requirement 60% Diesel Firm Load at 5m/s WTG 50% Diesel Firm Load at 7m/s WTG Diesel Firm Load at 9m/s WTG 40% 30% 20% 10% 0% 10 50 100 150 200 250 300 350 400 450 500 550 Firm Load Delivered (kW) Figure 14: Relationship between Firm Capacity Requirement and Availability Industrial Research Page 37 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources hours) for different levels of support (ranging from 10 to 550kW ‘firm’ capacity). In this example, the percentage capacity support provided by the WTG at 10kW is: (a) about 79% of the annual requirement with an annual wind speed of 9m/s; (b) about 74% of the annual requirement with an annual wind speed of 7m/s; and, (c) about 61% of the annual requirement with an annual wind speed of 5m/s. As the capacity requirement increases 55 times from 10kW to the WTG maximum of 550 kW, the contribution from the WTG in this example decreases by: (a) about 34% (79% to 45% contribution) of the annual requirement with an annual wind speed of 9m/s; (b) about 39% (74% to 35% contribution) of the annual requirement with an annual wind speed of 7m/s; and, (c) about 42% (61% to 19% contribution) of the annual requirement with an annual wind speed of 5m/s. Therefore, the renewable kWh energy contribution over the year is very high (see figure 9), but the renewable kW capacity contribution at times of need may be low (depending on the correlation between wind speed and the firm capacity requirement schedule). It is important to note that delivering a low percentage of wind generator capacity does not proportionately increase the availability – the curves in Figure 14 are relatively flat, the diesel still being used substantially at small levels of capacity delivery 67. In this report, a three year fixed price contract for energy is assumed. Energy retailers are however, less likely to promote this kind of contract in the future, due to the increasing volatility in the energy wholesale market. Time-Of-Use (TOU) pricing will be used more frequently for major energy users to buy and sell (minus an administration cost) electricity to and from the energy retailers. TOU pricing gives more attractive results for distributed energy investments than the typical three year fixed price contract used in this study. High TOU prices may correspond to the PPD / CPD used when supplying capacity to / releasing capacity from the distribution network. The maximum CPD and PPD capacity revenues / savings possible from by a fixed capacity generator remain constant every year, but the fuel costs will vary according to the number of CPD and PPD hours scheduled by the network each year. Obtaining payment for actual TOU prices could offset this variable fuel cost. 68. For the Summer 2002-03 PPD, the average monthly energy wholesale prices (equivalent to Time-Of-Use prices) at the Benmore GXP corresponding to the PPD load schedule are shown in table 20 below. Table 20: Indicative Average Electricity Wholesale Prices Corresponding to Orion Network’s Summer PPD PPD Month PPD Hours Average Price Cumulative Hours Cumulative Average Price Oct 2002 4.0 0.68 cents / kWh 4.0 0.68 cents / kWh Nov 2002 18.5 2.06 cents / kWh 22.5 1.37 cents / kWh Dec 2002 7.0 2.36 cents / kWh 29.5 1.70 cents / kWh Jan 2003 26.5 4.15 cents / kWh 56.0 2.31 cents / kWh Feb 2003 2.5 8.74 cents / kWh 58.5 3.60 cents / kWh Mar 2003 50.0 22.83 cents / kWh 108.5 6.80 cents / kWh Industrial Research Page 38 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources These values are less than the actual prices charged at the Springston and Hororata GXPs on the Summer PPD load management schedule, because to these must be added the cost of electricity transmission from Benmore to the Orion distribution network. From this result we can conclude that if the distributed generation owner had been operating under the MCLM scenario on a Time-Of-Use pricing contract, the average price of energy savings would have been equivalent to at least 6.80 cents / kWh (compared with 5.47 cents / kWh used in this study). Furthermore, if the distributed generation owner had been operating under the IPP scenario, the average price of electricity sold (assuming an administration cost of 10% of the actual wholesale price at the time of delivery) would have been equivalent to at least 6.12 cents / kWh (compared with 4.92 cents / kWh used in this study). 69. These results show that Time-Of-Use pricing (in the existing New Zealand electricity supply and demand market environment), will probably deliver higher financial returns from distributed generation investments than those indicated in this report. The average (monthly) prices (in cents / kWh) corresponding to each month’ s PPD schedule for the summer 2002-03 period, are shown in figure 15 below. Relationship Between PPD Schedule and Corresponding Benmore GXP Average Electricity Wholesale Price 25 Average Wholesale Electricity Price Corresponding to PPD each Month 20 15 MAR 03 10 OCT 02 F E 5 B JAN 03 0 DEC 02 NOV 02 3 0 0 20 40 60 80 100 120 Cumulative PPD Hours For Summer 2002-03 Figure 15: Average GXP Electricity Wholesale Price for each Monthly PPD Schedule During Summer 2002-03 Industrial Research Page 39 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Conclusions Commercial Viability 70. Based on these and other research studies, it is apparent that there are many mini- and small-scale renewable generation projects that are close to viability. These could make a positive contribution, in conjunction with the traditional energy infrastructure, towards alleviating looming energy supply problems. In overseas countries, such desirable emerging technologies are usually given “ kick-start” incentives to allow developers to obtain experience and develop an adequate scale of operation to be self-supporting. These pragmatic approaches to the development of new industry are still out of favour in New Zealand. Potential Impact on Renewable Energy Generation 71. Based on the operating scenarios used in this study, one hundred 550kW DG-WTG Hybrid systems could deliver 168 GWh/year from wind energy with an average wind speed of 7m/s, and 11 GWh from diesel fuel, when supplying capacity for an average of 200hours/year. This represents an average generation of 20MW, and a peak capacity delivered when required of 55MW. This is not an insubstantial portion of the annual increase in demand for electricity across the country (150MW per year). 72. The diesel gensets could alternatively be run on biodiesel, made for example from tallow. The introduction of significant numbers of diesel generator sets for these applications could provide a stable market for biofuel production. Generally no modification is required to run ordinary DG sets on 100% biodiesel. This situation could be improved significantly through appropriate government incentives to harvest and process biodiesel derived from soybean, tallow9 and other suitable feedstocks. Extensive work has already been done in this area, particularly by Massey University. The potential exists for biodiesel to compete on price with ordinary diesel at the pump10. Principles for Fair Access to the Network by Embedded Generation 73. Our research has particularly focused on the provision of capacity support to the network from embedded generation, at sizes down to the smallest level. The following principles need to be applied in the form of implementing consistent mandatory rules that are relevant to the size and impact of the generator. A. Energy Delivered 74. All generators should be able to obtain a price for exported energy that at least reflects the pricing structure imposed on them by the supply industry for purchase of energy, less a regulated administration charge. The administration charge should Industrial Research Page 40 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources be regulated because the environment in which customer or IPP owned distributed generation operates is monopolistic. This energy value is potentially available to major customer size generators, but is not available to general customers as of right. Mandated net metering would effectively provide an adequate energy price but would not allow the customer to obtain capacity payments. B. Capacity Delivered 75. This study demonstrates the impact that a fair payment for delivery of firm capacity can have on the economics of embedded generation. Based on the need to get best value from economies of scale, the study addresses micro- and mini-scale generation, but there is no reason why similar capacity support cannot be delivered from sufficient micro-scale household generation and / or mini-scale commercial level (general customer class) generators to produce the same accumulated (amalgamated) capacity. There should be no barrier to any customer (including general customers) getting a return from network support. Two rate kWh export metering can provide the necessary data to achieve this and should be the minimum acceptable for general customer embedded generation installations. Access to the Orion $100/kVA/year payment would provide significant incentive for export generation at the general customer level. C. Renewable Energy Delivered 76. There is a complete market failure in the implementation of renewable embedded micro- and mini-scale generation. There are a number of reasons for this, which will not be discussed here. We consider that because of this market failure, policy instruments must be implemented to encourage the uptake of new renewable distributed energy technologies as is widespread practice overseas in competing economies (new in this context means technologies that are currently not in common use due in part to cost – this would include wind generation). The main reason for this policy would be to deliver economies of scale to drive prices and develop new industry. Incidentally, it would eventually have an impact on both the electricity supply problems that will continue to face the country for the foreseeable future, and also address New Zealand’ s Greenhouse Gas (GHG) reduction commitments. The financial cost of implementation would be relatively low, as a very small fraction of the current electricity demand would be involved. Policy 77. The Government requested in a policy statement released in December 2000 that the Electricity Governance Board (EGB) draw up non mandatory model arrangements for use of system agreements and develop rules that include the terms and conditions for connecting distributed generation to the network11. Over two years later a draft Model Terms and Conditions for Connection of Distributed Generation (MTCCDG) to a Network has been released by the MARIA Governance Board for comment by industry and other stakeholders. This document lays out Industrial Research Page 41 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources principles that should be followed by industry participants but will not be included in the MARIA rules, so no aspect of it is mandatory. To our knowledge, no rules or terms and conditions have yet been produced. 78. This is an unsatisfactory environment for small-scale embedded generators, who still have to undertake case by case negotiation of virtually every aspect of a connection application. The rules and requirements are still decided by each individual lines company and each individual retailer. 79. It is essential that the industry work towards mandatory nation-wide network technical connection standards and charging/payment rules that include both energy and capacity costs/payments for all levels of embedded generation. 80. In concept, the MTCCDG principles are satisfactory for generators that are large enough to operate as MCLM or IPP operators as described in this report. However for the lower end of embedded micro-scale generation, they fall short on providing the opportunity for reasonable returns from network ancillary benefits (e.g. capacity support). 81. Many distributors have in place connection conditions that are at odds with the proposed MTCCDG principles6. It will be interesting to see if they bother to bring their conditions into line with the principles that their industry committee has developed. Future Directions 82. Five significant steps must be taken if Government policy is going to truly encourage the development of distributed generation in New Zealand. The steps are: Step 1: Adapt and extend Orion Network’ s pricing schedule and Orion Network’ s procedures for DG grid-interconnection into a set of consistent, nationwide regulations. Step 2: For micro-scale own-generation (DG), adopt two-rate kilowatt-hour export metering with pricing signals controlled by the network company, to value and reward the capacity-support from “ behind the revenue meter” ; and, provide a consistent, transparent means of calculating the value of capacity delivered (see appendix four). Step 3: Energy Retailers be required to purchase any energy exported by their customers, at either a Time-of-Use wholesale market price where it is cost effective to fit half hour export metering or at a pricing-schedule equivalent to the existing energy pricing contract between the energy retailer and the DG-operator functioning as an energy consumer Industrial Research Page 42 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources (minus a reasonable administration fee of say 5-10% of the energy price). Step 4: Guarantee long-term continuity of contractual arrangements impacting the financial viability of DG investments, such as Power Purchase Agreements (PPAs) with energy retailers and lines network companies typically required to remain enforced for between 15 and 25 years. Step 5: Streamline / expedite the resource consents applications process for renewable DG and DG load management applications. 83. It is clear by now that regulatory improvements are necessary for grid-connected DG investments. There are several different regulatory approaches to facilitating the DG market (at least four distinct approaches have been identified by Industrial Research – see appendix five for details), each containing its own strengths and weaknesses for facilitating distributed generation in New Zealand. Each regulatory approach provides different threats and opportunities to electricity market incumbents and new market entrants alike. The approach currently supported by Government in its discussion paper on DG12, represents a customer-driven (i.e. an independent DG-operator-driven), utility-response for facilitating DG in New Zealand. This approach is the most risk-adverse of the four identified by Industrial Research for facilitating DG (see appendix five). It also creates the most uncertain and unlikely environment for encouraging the development of DG in New Zealand by prospective investors. We suggest a more progressive regulatory environment for facilitating DG, using the legislative frameworks described in Appendix Five. Industrial Research Page 43 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources References 1. Gardiner A.I, Sanders I.A., Are Microgrids the Answer to Post 2013? EEA of NZ Conference, June 2002. 2. Sanders I.A., Gardiner A.I., Risk Associated with Energy Supply from Distributed Renewable Generation, EEA of NZ Conference, June 2002. 3. Report and presentation available for downloading from the Industrial Research website at: http://www.irl.cri.nz/electrotec/distributed_energy/IDES.htm. 4. Sanders I A, Gardiner A I, Market Opportunities for Dispersed Wind Energy Sources in NZ, EEA of NZ Conference, June 2000. 5. Based on conversations with Orion Network personnel. 6. Roding W., Comparison of Costs and Benefits of Connecting a 500kW Wind Turbine into Major New Zealand Electricity Networks, Windflow Technology Ltd., May 2002. 7. Orion, Electricity Delivery Pricing for Major Customer Connections, Issue: Three, Date: 30 April 2002; and, Orion, Electricity Delivery Pricing for Major Customer Connections, Issue: Three, Date: 31 January 2003 (applicable from 1 April 2003). 8. Meridian Energy website: http://www.meridianenergy.co.nz/majorcustomer/ 9. http://www.eeca.govt.nz/Content/EW_renewables/Reports/Biodiesel final report.doc 10. Based on conversations with Prof. Ralph Sims 11. http://www.med.govt.nz/ers/electric/gps-implem/index.html 12. Government Discussion Paper (GDP), “ Facilitating Distributed Generation’ , Ministry for Economic Development, September 2003. Industrial Research Page 44 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Appendix One: Key Assumptions œ Diesel costs are based on data supplied by Transmissions & Diesels Ltd, giving the following cost algorithms: Capital Cost = $2,042.20(kW Capacity)-0.3552 / kW Maintenance Cost = $1,333.30 + $6.67(kW Capacity) / year œ Operating periods for the diesel generator broken up into continuous 10-minute periods, for matching diesel energy production with the corresponding PPD. œ Cost of diesel prior to conversion = $0.7030 / Litre œ Annual fuel price increase = 2% œ Maximum diesel conversion efficiency = 37% (for a 550kW generator) œ Diesel conversion efficiency varies with its operating capacity (20-100% of max.) œ Diesel plant operating life = 40,000 hours before replacement required œ Wind turbine cost (including O&M) = $2,725 / kW œ Annual CPI (inflation for O&M) increase = 3% œ Project life = 20 years œ Electricity purchase price (from the energy retailer) = 5.47cents/kWh œ Electricity sales price (to the energy retailer) = 4.92cents/kWh œ Annual energy price increase (buying and selling) = 1% œ Discount rate = 5% œ Cost of finance = 0% or 10% paid monthly over 20 year fixed term œ Annual wind speeds investigated for the 550kW wind turbine = 5, 7, and 9m/s œ Scenarios run for 2000-01, 2001-02, and 2002-03 summer PPD seasons œ Cost of metering systems required to implement the scenarios shown, excluded œ Results do not consider tax deductions on loan repayments or on the net annual gains made from running profitable operations. Industrial Research Page 45 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Appendix Two: MGB Model Terms and Conditions Model Terms and Conditions for the Connection of Distributed Generation to a Network Background The Government Policy Statement: Further Development of New Zealand’ s Electricity Industry (GPS) of December 2000 requires the Electricity Governance Board (EGB) to develop as part of the model arrangements for the use of distribution systems the terms and conditions for connecting distributed generation to a network. The GPS also requires the EGB develop rules that are consistent with the Guiding Principles contained in the statement and “ … ensure that the use of new electricity technologies and renewables, and distributed generation, is facilitated and that generators using these approaches do not face barriers” . A key Guiding Principle in this regard is the requirement that “ … the full costs of producing and transporting each additional unit of electricity are signalled so that investors and consumers can make decisions consistent with obtaining the most value from electricity” . The Electricity Governance Establishment Committee (EGEC) allocated the task of developing the model arrangements for the use of distribution systems, and the associated work relating to terms and conditions for distributed generation, to its Transport Working Group (TWG). The initial focus of the TWG was on establishing Part F of the Rulebook covering transmission investment and the approval process for transmission pricing methodologies, and the model arrangements for use of distribution systems. In late 2001, however, the TWG initiated work on distributed generation, but before this work had advanced very far, EGEC requested the TWG put the work on hold for budgetary reasons. In May 2002, the MARIA Governance Board (MGB) made an offer to EGEC to sponsor completion of the model arrangement and associated work. This offer was accepted, and a team of 4 retailers, 4 distributors, 2 consumer representatives, and an independent chair was appointed to make up the Model Distribution Arrangements Project (MDAP) team. MDAP’ s initial focus was on the model arrangements but as it neared completion of that work it commissioned a Distributed Generation Sub-Group (DGSG) to investigate the terms and conditions for the connection of distributed generation. This group reported it to it in February 2003. After consideration of the report, MDAP agreed on certain Industrial Research Page 46 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources aspects of the terms and conditions and had this draft version prepared for further scrutiny by the DGSG. The attached model terms and conditions are the outcome of that further scrutiny by the DGSG. They are intended to provide the principle terms and conditions for inclusion in the contractual arrangement between a Distributor and a party wishing to connect generation equipment to the Distributor’ s network. They do not constitute a model contract or agreement in its full legal form. This focus is consistent with the requirements of the GPS which implicitly recognised that the contract for the connection of a 30MW embedded generator should differ somewhat from that required for hooking up a domestic solar system, and called for the development of model “ terms and conditions” and not model “ agreements” or “ arrangements” . Categories of Distributed Generation Distributed generation, or embedded generation as it is sometimes called, ranges from very small-scale units, such as household solar systems, connected to the low voltage distribution network via an inverter to large power stations with capacity well in excess of 5MW. While the technical aspects of connection vary considerably according to supply the principles of connection and charging should not. What should determine these is the GPS requirement that “ … the full costs of producing and transporting each additional unit of electricity are signalled so that investors and consumers can make decisions consistent with obtaining the most value from electricity” . Those investing in distributed generation should face the full incremental costs and receive the full incremental benefits that flow from their decision. Industrial Research Page 47 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Model Terms and Conditions For Connection of Distributed Generation to a Network Network Connection and Safety 1 The Generator must comply with the Network Connection and Safety Requirements of the Distributor. 2 The Distributor must publish its Network Connection and Safety Requirements. [Orion’ s Electricity Network Procedure NW70.10.04 is appended as an example.] 3 The Distributor’ s Network Connection and Safety Requirements must: œ Reflect the safety, power security and power quality issues that are actually associated with the various categories of distributed generation; œ Not cover matters other than safety, power security and power quality; œ Include a provision for consultation on any changes; and œ Maximise the net public benefits they create. 4. The Distributor has the right to inspect the Generator’ s Equipment, after notification, at any time to ensure that its Network Connection and Safety Requirements are being fulfilled. Metering 5. The Generator shall comply with the current MARIA Rules regarding metering and must arrange for installation of meters with at least two registers; one to record inflows of energy and one to record outflows. Payments 6. The Generator will pay the Distributor: a) The incremental costs of assets put in place by the Distributor as a result of the Generator’ s decision to install Equipment. Assets dedicated to the Generator’ s exclusive use or installed at the Generator’ s request and assets necessary to reinforce the Network because of the Generator’ s decision shall be included. The incremental costs shall include the cost of capital to the Distributor. This payment may take the form of a one-off charge at the discretion of the Distributor. Industrial Research Page 48 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources b) Any reasonable incremental operational costs of the Distributor that result from the actions of the Generator. 7. The Generator will not pay the Distributor a charge for connection to the Distributor’ s network or for injecting energy into it, except where the charge is in lieu of a payment under clause 6(a) above. 8. The Distributor will pay the Generator or Retailer: a) An avoided transmission rebate to reflect the net benefits to the Distributor, after its reasonable costs, of any incremental reduction in transmission charges achieved as a result of the actions of the Generator. b) A deferred network investment rebate to reflect the net benefits to the Distributor of any reduction or deferral of its requirement to invest in its Network that results from the actions of the Generator. The uncertainty of network investment may be factored into the calculation of this rebate. The value of the rebate will be calculated on the basis of the term of the contract between the Distributor and the Generator or ten years, whichever is the lesser. 9. The calculation of the payments between the Distributor and the Generator shall be transparent. 10. Unless it is economically efficient to do otherwise, a very small scale Generator, rated up to 14 kVA and connected “ behind” load, should be charged by the Distributor on the basis that they have a residential line and do not impose or save transmission or investment costs on the Distributor. 11. Unless it is economically efficient to do otherwise, a very small scale Generator, rated up to 14kVA and connected “ behind” load, should be charged or paid by the Retailer on the basis of the net flow of energy. Liabilities 12. The Distributor has no liability for damage to the Generator’ s Equipment or persons resulting from the operation of the Generator’ s Equipment. 13. The Distributor has no liability for damage to the Generator’ s Equipment resulting from the remote or automatic operation of the Distributor’ s Equipment except when it has been negligent, and then its liability is limited to the direct losses of the Generator. 14. The Distributor has the right to disconnect the Generator in the event the Generator has failed to comply with the Distributor’ s Network Connection and Industrial Research Page 49 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Safety Requirements. The Distributor will give reasonable notice before disconnection, where practicable to do so. 15. The Generator shall be liable for any direct damage to the Distributor resulting from the operation of the Equipment other than in conformity with the Distributor’ s Network Connection and Safety Requirements. Term and Contract Renewal 16. A contract between a Distributor and a Generator shall not be for a term of less than seven years, unless the parties agree otherwise. 17. Not less than twelve months prior to the termination of the contract, the Distributor and the Generator will enter into discussions to agree the terms and conditions that will apply in any subsequent contract. Those discussions will take into account the terms and conditions applying to other similar distributed generation facilities for connection to networks in New Zealand in contracts entered into within the last two years prior to the commencement of discussions. If agreement cannot be reached between the Distributor and the Generator alone, the parties will attempt to resolve the disputed elements with the aid of a mediator. If the disputed elements cannot be resolved with the aid of a mediator, then they will be resolved by binding arbitration. Industrial Research Page 50 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources MARIA GOVERNANCE BOARD 26 March 2003 Model Terms and Conditions for Connection of Distributed Generation This paper presents for the MGB’ s consideration, model terms and conditions for connection of distributed generation developed by the Model Distribution Arrangements Project Team. The paper recommends that the MGB release the model terms for distribution for submissions from MARIA participants and interested stakeholders. Industrial Research Page 51 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Introduction Purpose of this The purpose of this paper is to present the model terms and conditions paper for connection of distributed generation as developed by the Model Distribution Arrangements Project Team (MDAP). It is recommended that the MGB agree to release the attached document for industry consultation. Background The request to develop model distribution arrangements originated from the Government Policy Statement (GPS) released in December 2000. This states that the Electricity Governance Board should: œ Draw up non mandatory model arrangements for use of system agreements which are consistent with the GPS Guiding Principles and ensure that the interests of retailers and users are given equal weight to that of distributors; œ Develop rules and include the terms and conditions for connecting distributed generation to the network; and œ Make these available for consideration by distributors and retailers for development of their own approaches. MDAP terms of The MDAP was appointed under MARIA in May 2002 to complete the reference work started by the Transport Working Group (under the Electricity Governance Establishment Committee) in developing model distribution arrangements. While the result of this work will not be directly incorporated into the MARIA rules, it will be endorsed by the MGB and circulated for use within the industry. Industrial Research Page 52 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Terms and Conditions for Connection of Distributed Generation Summary of MDAP’s initial focus from its terms of reference was the development of work to date on the model interposed and conveyance agreements. Once these model distributed agreements were nearing completion, the MDAP agreed to form a generation Distributed Generation Sub-Group (DGSG). The work of the DGSG was considered by the MDAP at its meeting on 11 February 2003. On the basis of the DGSG’s recommendations, draft terms and conditions were prepared for the approval of both the DGSG and MDAP, before these were forwarded to the MGB. Principle terms The attached document is intended to provide the principle terms and and conditions conditions for inclusion in the contractual arrangement between a Distributor and a party wishing to connect generation equipment to a Distributor’s network. The document is not intended to constitute a model contract or agreement in its full legal form. This approach is consistent with the MDAP’s terms of reference and the GPS. Industrial Research Page 53 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Timeframe for Consultation and Completion Minister’s As the MGB is aware, the Minister of Energy is concerned that this GPS concern about issue in particular, be completed within a prompt timeframe. The latest timeframe for letter from the Minister indicates that “agreed terms and conditions for completion of connection of distributed generation are, in particular, crucial to new this work investment and electricity security, and I have asked my officials to closely review what might be done to expedite matters”1. Deadline for Under the MARIA rules, the MGB is required to provide MARIA submissions participants with 25 working days to make submissions on chapter 2 rule and completion change proposals. This timeframe has been applied to other documents of work released for industry submissions (e.g. the model interposed agreement) even though the outcome of the consultation process will not result in a MARIA rule change proposal. In order to accelerate the completion of this issue, it is recommended that the MGB release the terms and conditions for connection of distributed generation for a shorter time period than normally required by chapter 2 rule changes. A period of three weeks is recommended, as this would allow the MDAP to complete this work by the end of April 2003, provided no substantive issues arise out of the consultation process. Recommendations Recommended It is recommended that the MGB: actions a) Agree to forward this paper, along with the attached model terms and conditions for connection of distributed generation document, to MARIA Participants and other interested stakeholders for submissions; b) Agree to a period of three weeks for consultation on the attached document; and c) Request the Administration Manager, when sending out the document for consultation, to notify MARIA Participants and other interested stakeholders that the period for consultation on this document is shorter than normal. 1 Correspondence from the Minister of Energy to the MGB dated 25 February 2003. Industrial Research Page 54 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Appendix Three: The IDES Simulation Modelling Capability Overview The simulator places a value on the supply of local energy, and compares the cost of this with the grid-supplied energy. For constrained local energy supply, for example from wind or photovoltaic generators, the resource data is input as ½ hourly averages, and all available energy is utilised according to the calculated IDES model output. For fuel based systems, i.e. those using energy stores, more complex despatch options can be defined, depending on the relative value of the local energy supply. A simple block diagram of the simulation process is shown in Figure A3-1. A number of different generic models have been developed to represent existing and emerging IDES technologies. Grid Electricity Electrical Supply Electrical Demand Profile (Generator, transmission and distribution charges) Heat (Ht) IDES IDES IDES Heat Heat Fuel Ht Energy Electrical Energy Demand Fuel Conversion Energy Supply Profile & Storage Supply Figure A3-1: Model Structure The main simulator output is the predicted cost/benefit results from lifetime use of the IDES in relation to grid supply, assuming a continuous grid connection. Annual price adjustments, varying interest rates and different maintenance costs can be incorporated. A large range of intermediate data is available form the simulations. For example, the time varying cost of production to supply locally generated hydrogen can be found directly from simulations using the electrochemical engine model. Distributed Energy Sources In general the IDES models can account for variable efficiency when operating at under full capacity, and cost variation based on capacity. The heat generation component of technologies that produce combined heat and power (CHP), can be included in the simulation to offset electricity supply. The simulator has available the following IDES for standalone, micro-grid or grid-connected applications: Industrial Research Page 55 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources œ Photovoltaic (PV) power œ Solar thermal hot water / electric œ Wind power œ Micro-hydro power œ Energy efficient building design space heating reductions œ Manual / automated building demand side management (DSM) power shifting strategies œ Energy conversion / storage or storage / conversion: for example electrical storage devices – e.g. batteries, capacitors   hydrogen storage   thermal energy storage   mechanical energy storage – e.g. flywheels and compressed air   œ Heat engine cogeneration: for example internal combustion engines   external combustion engines   micro-turbines   steam turbines   fuel cells   œ Variable fuel selection: for example diesel   petrol   liquid biofuels   solid biomass   biogas   LPG   Bottled / pipeline natural gas   Hydrogen   CNG   Dual fuel systems: e.g. diesel-LPG, diesel-CNG   Å Synthetic fuel mixtures: e.g. bio-diesel Different IDES have considerably different heat to power ratios (see figure A3-2). Heat:Power Ratio for Different Distributed Generation Options: at 100%, 80%, 60% & 40% Operating Capacity 100% 100% 100% 100% 100% 1.00 0.90 80% 80% 0.80 80% Power Output Per Unit 80% 1kW Solar PV 0.70 60% 80% 1kW Wind Turbine 0.60 1kW Fuel Cell 60% 60% 0.50 (1MW) Diesel Engine 40% 60% 0.40 (200kW) Dual-fuel Engine 40% 40% (30kW) Micro-turbine 0.30 40% 1kW Stirling Engine 0.20 0.10 0.00 0.00 1.00 2.00 3.00 4.00 5.00 Heat Output Per Unit Figure A3-2: Heat to Power Ratios for Various DES. Hydro generators follow Solar PV and wind Industrial Research Page 56 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Biomass co-generation and solar thermal co-generation are the only renewable energy sources capable of producing both heat and power. Solar thermal is normally used to produce either heat or electricity, and biomass heating systems are still more common than co-generation plants. Grid Supply The conventional central grid costs may be modelled in different ways: œ Average charges for the consumer group: e.g. 10cents/kWh, anytime. œ True multi-part tariffs (with separate fixed, variable and TOU charges): e.g. (a) domestic tariff; and, (b) industrial tariff: (a) domestic tariff for a farm in Kumeroa: (i) day-time (07:00 – 23:00) variable cost: 10.2c/kWh (ii) night-time (23:00 – 07:00) variable cost: 8.8 c/kWh (iii) fixed line cost 74.6 c/day (b) industrial tariff for Gracefield Research Centre: (i) fixed line 750kVA capacity cost: 60c/mth (ii) anytime max demand cost: 772c/mth/kVA (iii) coincident max demand cost – (17:30 – 19:00 weekdays only): 431c/mth/kVA (iv) fixed service (e.g. metering) cost: $30/mth (v) variable TOU energy costs: Time Period: Weekday: Weekend: 00:00 – 04:00 2.454 c/kWh 2.200 c/kWh 04:00 – 08:00 2.667 c/kWh 1.966 c/kWh 08:00 – 12:00 4.079 c/kWh 3.202 c/kWh 12:00 – 16:00 3.015 c/kWh 2.672 c/kWh 16:00 – 20:00 4.469 c/kWh 4.321 c/kWh 20:00 – 24:00 2.755 c/kWh 2.740 c/kWh œ True costs based on ½ hour energy wholesale prices (see Figure A3-3). VARIATION IN THE AVERAGE HALF-HOURLY MONTHLY WEEKDAY WHOLESALE PRICE OF ELECTRICITY FROM OCT96 TO SEP97 5.0 WHOLESALE ELECTRICITY PRICE (CENTS/KWH) 4.5 OCT 4.0 NOV DEC 3.5 JAN 3.0 FEB MAR 2.5 APR 2.0 MAY JUN 1.5 JUL 1.0 AUG SEP 0.5 0.0 0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00 23:00 TIME Figure A3-3: Variation in Energy Weekday Wholesale Prices – half-hour prices can be used for every time period over a year, namely: 17,520 time periods Industrial Research Page 57 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Demand The electrical and heat demands can be simulated in a number of ways: œ Half hourly over a full year. œ Daily half hour demand averaged over each month for a full year. An ancillary analysis tool has been developed to determine the heating component of an electrical load. Where this can be established, the simulator will supply as much heat load as possible from the IDES available heat supply, taking into account the heat exchanger costs and efficiencies. Comparison of the seasonal variation in total electricity demand with heating demand for three farm houses in Kumeroa 40 Average total electrical and heating 35 loads in each half-hour (kWh) Winter Total Load 30 Spring Total Load 25 Summer Total Load Autumn Total Load 20 Winter Heating Load 15 Spring Heating Load Summer Heating Load 10 Autumn Heating Load 5 0 0:00 1:30 3:00 4:30 6:00 7:30 9:00 10:30 12:00 13:30 15:00 16:30 18:00 19:30 21:00 22:30 Time of day Figure A3-4: Typical Heating and Electrical Loads Used in the DES Models Typical heating and electrical loads for incorporating in the IDES models are shown in Figure A3-4. Model Assumptions and Constraints As with any model, there are framework limits within which it operates. The key assumptions and constraints are: œ The simulator result is only as accurate as the supplied data. œ There are complexity limitations on the IDES model resource capture components, but it is considered that the models are of adequate accuracy for the purpose. œ Probably the most important constraint is that to maintain relatively simple functionality, only one IDES type can be modelled within the simulator at any one time – i.e. optimisation between a combination of IDES options can not be automatically performed. This is not a serious limitation within the purpose of the models. Industrial Research Page 58 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources General Model Capabilities The techno-economic models developed use accurate regional and time-specific electricity pricing data, energy supply data, and customer electricity consumption data to identify which IDES technologies are economically and technically viable. Projected Cost Variables With regard to future price projections, models and algorithms have been developed which consider historical fuel prices, the consumer price index and other associated inflation reflective indices, historical electricity prices, production progress cost reductions, economies of production, and economies of scale. The production progress cost reduction is associated with learning/progress curves. A learning curve describes the phenomenon by which the time per cycle to perform a particular task decreases as the number of repetitions of the task increases. (Typically, the effect is strongest when a single product is produced in a single factory by the same manufacturer). Economies of manufacturing or production represent another means by which per unit production costs can be reduced, and are not necessarily directly related to production progress curves. This type of cost reduction is a function of the production rate, which allows the plant to leverage indirect costs more efficiently. Economies of scale cost reductions are based on real manufacturers’ costs per unit projections for bulk orders of different modular technologies, and costs per kW projections for modules of increasing size. Summary of Model Functions and Capabilities A summary of the model functions and capabilities follow: œ Regional renewable energy data – input solar, wind, hydro, biomass or fuel resource supply data for any region in the country, from a single house location to a region or an island or the entire country. œ Regional historical renewable energy resource analysis – this uses whatever historical data is available to determine the best and worst case scenarios over a system’ s lifetime. œ Regional load profile data – individual household, cluster of houses, commercial business, industrial complex, GXP demand level, or aggregated GXP demand level up to total national demand level. œ Type of load profile – any type of demand profile may be used, and the shape and size of this profile may be modified in the model. œ Size of load profile – any size can be modelled. œ Capital cost and interest repayment period – include amortised costings for any time period and any interest rate and loan. œ Renewable energy resource type – especially solar, wind, hydro and biomass in annual, monthly, daily, hourly or half-hourly increments. œ Size – any size of plant and number of plants can be considered. Industrial Research Page 59 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources œ Lifecycle cost – considering capital purchase, installation, O&M, labour, and any other fixed and running costs applicable. œ Capital cost (including installation where necessary). œ Scale-adjusted capital cost – any adjustment can be considered. œ O&M cost – considered as a percentage of the capital purchase cost or the capital and installation cost or as a cents/kWh value for fuel-driven IDES applications etc. œ Efficiency – adjusted to consider size variations and different operating capacities. œ Grid supply cost, including the following options: six 4-hourly weekday energy tariffs and six 4-hourly weekend tariffs, half-hourly wholesale GXP prices, fixed and variable line charges, average line replacement costs, average line operating and maintenance costs, maximum distribution capacity costs, coincident distribution capacity costs, metering costs, average retail, distribution and transmission costs for different network regions, variable electricity price inflation rates and fuel price inflation rates, transmission capacity reduction rebates, variable IDES tariffs which are independent of the grid supply costs, consideration of on-site and dispersed power production issues with respect to both grid supply and IDES supply, and export-metering factor to account for variable returns from exporting surplus electricity generated back to the grid. œ Capital interest rate – variable. œ Lifetime – variable. œ Heat to power ratios where applicable – considering both demand requirements and supply availability. œ Variable electrical and heat production curves for any heat-engine application. œ Operating capacities of the plants where applicable. œ Variable scheduling of operating heat-engine systems from continuous to half- hour periods. œ Different fuel prices and annual inflation rates where applicable, including hydrogen production costs using electrolysers. œ Heat recovery rate and cost – where applicable (including heat recovery inflation rate). Industrial Research Page 60 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Appendix Four: Capacity Metering for General Customers Rewarding the Demand Side Delivery of Distributed Generation Capacity Provided by Large Numbers of Very Small Systems Definitions General customer-generator: an electricity customer whose energy consumption is metered by a totalising kWh meter, is subjected to deemed profiling, and who generates behind the revenue meter, primarily for own consumption. Distribution Company: the organisation that is responsible for power distribution in a specific region, which normally owns and operates the electrical distribution system. Micro-DG: Distributed generation of capacity less than 100kW per site, and generally less than 10kW. Summary There is strong justification for metering very small scale DG capacity Micro-scale distributed generation (micro-DG), i.e. very small-scale distributed generation generally connected behind (on the load side) of a revenue meter, offers a substantial opportunity for environmentally sustainable alternatives to the expansion of centralised supply side generation, transmission and distribution infrastructure. At present the electricity market has no mechanism for valuing the capacity that micro-DG can offer. Indeed, the industry is just getting to grips with how to value and transact the energy associated with DG connections. New Zealand currently has an excellent opportunity to lead the world in setting up a regulatory framework to provide fair access to the network for micro-DG. This paper shows how standard kilowatt-hour metering technology can be used to value and reward the capacity that any micro-scale own- generation plant contributes to the network, down to the smallest size. This metering approach is low cost and economically efficient, and if adopted will simplify the currently propose regulations as applying to small general customers [1]. The basic premise of this proposal is that all generators, no matter how small, are entitled to use of the network, and that they should be rewarded for the capacity, as well as the energy that they deliver to the network. Industrial Research Page 61 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources This proposal is the main conclusion from several years research into the reasons for lack of uptake of very small scale distributed generation. Without the introduction rules that provide for payment of capacity supplied by these generators, an opportunity for better alternatives to supply side expansion will be lost, and consumer choice will be curtailed. Introduction The market structure must allow efficient choice of delivery Efficient and cost effective delivery of electricity services is fundamental to New Zealanders’ economic and social well-being. The environmental impact of providing these energy supplies is significant and of increasing concern, and should be minimized wherever possible. It is imperative that an electricity market regulatory framework is put in place to fully value any energy resource that can be utilized in an environmentally sustainable way. Timely delivery of electricity services requires an energy production component, and a delivery infrastructure. Both the energy component and the infrastructure capacity must be available when required by the customer. The cost of infrastructure capacity can be many times the cost of energy. Distributed generation (DG) that can provide both of these components should be rewarded for both, even if the generation is connected behind a consumer’ s energy meter. For very small-scale distributed generation, new metering is needed to measure its contribution to capacity. Micro-Scale Distributed Generation Research studies IRL has been evaluating the economics of various micro-scale distributed energy technologies for over five years. Many reports and papers predicting technology performance and system economics have been published over this time, notably in Energy Wise News and at the annual New Zealand Electricity Engineers Association Conference, e.g., [4], [5], [6]. This work has shown that based purely on energy sales, very few of these technologies are economic, nor likely to be in the next decade. More recently, we have turned our attention to examining and developing combinations of small-scale DG that will improve the level of firm capacity provided in support of the distribution system. This focus came from the realisation that the electricity supply industry in-general currently views small-scale DG as a problem rather than a possible solution to load growth. We have shown that if the time of use capacity support that small-scale distributed generation technologies can provide is fairly valued [2], some of these technologies are already viable, and will become increasingly more economic within the next few years. This is at present particularly relevant to rural and remote parts Industrial Research Page 62 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources of the system, because of the higher cost associated with electricity delivery to these regions. As DG costs drop and central power costs rise, the economics will improve in wider scale applications [4]. Overall, micro-DG could provide an economically and environmentally attractive alternative to supply side upgrades in generation, transmission and distribution infrastructure in many areas, but only if market access regulations are in place to allow it to occur. The current proposals [1] are inadequate in this regard. Valuing small-scale generation capacity Typical own-generation micro-DG technologies are photovoltaic, wind, micro-hydro, and small fuelled generators. In this proposal, we make no distinction between renewable and fossil resources. Our research shows that combinations of technology such as wind-diesel hybrid generation can provide reduced risk (i.e., more consistent capacity) and improved returns to the owner [2]. The capacity payment received by an owner of these systems should be valued on the basis of the statistical capacity support that they provide during times of peak demand. To support load growth, new centralised and medium-scale distributed generation power plants require associated upgrades to transmission and distribution infrastructure. Depending on the location of the load, this T&D infrastructure can cost $1,500 to $5,000 per kW (e.g. $10,000 to $50,000 per km for LV and MV lines) or even more in remote areas, and is ultimately paid for by all electricity customers. Since micro-DG can avoid most of the incremental transmission and distribution infrastructure costs, measured micro-DG capacity support should be assessed and paid for in the context of these avoided costs, and as above, the cost passed on to electricity customers as line charges. Potential positive impact of micro-DG It is important to note that while many of the technologies have been available for some decades, the level of uptake of micro-DG is practically non-existent. This results from discouragement of private generation in pre-deregulation days, a current absence of uniform access regulations of any sort, and poor economics for the more widely applicable technologies (e.g. PV). However, this should not serve as a reason to draft regulations that close off the opportunity for these technologies to fairly compete and contribute to the generation mix. There are approximately 1.25 million dwellings in New Zealand. It would take an average of 5kW peak generation capacity associated with only 10% of these households to avoid 600MW growth in central generation and delivery infrastructure. Since micro-DG capacity will normally be provided in conjunction with load growth, the cost/kW average is not increased. In fact, through appropriate payment signals, load factor can be encouraged to improve and the overall cost/kW average will reduce, i.e. the system will be operating more efficiently. The potential for wealth creation through an active domestic market in these small-scale energy technologies should also not be overlooked. Industrial Research Page 63 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Technical Background It is generally recognized that individual micro-DG plants of only a few kilowatts have little impact on the distribution system, providing that basic technical standards are complied with. There is still some concern within the supply industry that significant levels of DG penetration in an area may cause network problems to surface, but 20% to 40% of the connected load supplied locally is unlikely to require special management practices. DG distributed across a large number of sites will almost always be technically preferable to a single injection point [7] (e.g. reduced voltage fluctuation), so this provides ample scope for distributed micro-DG to make a substantial positive difference to network loading. An arbitrary maximum is often decided in treatment of very small own generation connected behind the revenue meter. The proposed regulations [1] recommend a maximum generator capacity of 10kW. We find no technical reason why the DG capacity at any site should be limited to a particular low value. It is highly unlikely that every customer on a distribution feeder will want to connect DG, let alone up to a designated limit. Our contention is that general customer DG connections should be allowed up to the individual customer of the service mains capacity. This maximizes the opportunity for network support. For a three phase 100A, 400V ICP, up to 69kVA could be connected (3 x 23 kVA). The distribution company should have the right to restrict new additional connections if it can be shown that the performance of the network is at risk. Market Background MARIA allows energy supplied to general customers with mains of less than 100A capacity to be metered with totalising kilowatt-hour meters. This is usually billed monthly, often by estimate every alternate month. The individual profile of these customers is not known. A collective profile is used. These customers represent the vast majority of electricity connections to the distribution system, and present an ideal base for low cost connection of embedded generation. Our view is that the energy from any distributed generation provided by these customers, when connected behind the energy meter should be treated in the same manner, i.e. any surplus exported energy is simply treated as negative load. A small handling charge to manage the energy reconciliation would be acceptable. The capacity supplied by the generator should be treated in a similar collective manner. There is no need to treat this collective capacity any differently than other individual contributions delivered by larger scale DG plants. All that is required is a metering technique to record the contributions with adequate precision, so that the capacity value attributed to each generator can be calculated and reconciled. A solution is described below. Industrial Research Page 64 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources The Metering Proposal for Small-Scale General Customer- Generator Capacity The approach is very simple, as indicated in fig 1. A two register kilowatt-hour export meter is placed in series with the distributed generator connection on the load side of the revenue meter. This is simply a standard two register import meter with reverse stop fitted (i.e. backwards energy flow is not recorded). It is connected in reverse, i.e. to record export kWh. The two register meter records on-peak and off-peak kilowatt-hours of generation in the different registers. The meter register is switched by distribution company, at the distribution company’ s discretion. This provides a means to determine the average kW capacity provided over any peak control period season, which is the measure used by Orion for DG capacity payments. At least 1 charge period (1/2hr) must be designated by the lines company for capacity payment in each season/year, to ensure that payments will always be made to capacity providers. An external signal for customer use must be provided. Means of signalling this register change would be up to the distribution company, but could include: œ Ripple control œ Radio paging œ Time clock œ Telephone modem œ Or a more specific control signal based for example on the local system voltage level Existing kWh customer loads Distribution System connection Existing Revenue Meter(s): As required by Capacity Meter: energy retailer kWh Two Register Meter with reverse stop, On-pk, off-pk connected as Export register switch Distributed generation Fig 1: Metering general customers for capacity support This capacity payment approach is in principle already implemented by Orion Networks for larger customers. Our proposal is to standardize the application of this principle Industrial Research Page 65 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources down to any capacity level offered by a general customer-generator. At the current Orion offer of $100/ average kW/year, this can deliver a substantial return for a distributed generator (at present only larger half hour TOU metered generators are allowed to access this payment. In remote regions with network constraints, a case for much higher payments can easily be made [8]. Our case study analysis for mini-scale wind-diesel hybrid systems [2] shows that based on Orion payments, typical annual revenue is shared 50:50 between energy production and network peak period demand receipts. Obligations on the Customer-Generator This class of customer generator should not pay additional costs for this connection since in general, there will be no incurred network upgrade costs accruing. Indeed, if a use of system charge was made, this would disadvantage DG against central generation, which pays no such charge. Standard technical connection requirements appropriate for this size of system would need to be complied with. The cost of the capacity metering and installation should be borne by the customer-generator (preferably having the right to own the capacity meter if desired), plus any dedicated plant necessary to deliver the generated power to the ICP. Obligations on the distribution company The distribution company would be required to provide a capacity payment schedule that, at a minimum, offered a fair payment for the on-peak kWh exported each year, or season. The capacity payment schedule offered would be subject to appropriate disclosure regulations, to ensure that a payment which represents avoided T&D costs is offered, less reasonable incurred transaction costs. Other more innovative products could be offered by the distribution company in different regions to promote desirable load-generation patterns. For instance, a premium could be offered for generation exhibiting a high on- peak/off-peak differential in regions with poor load factor to encourage improvements. In times of national energy supply constraint, for example dry year events, promotions could be run to encourage high total generation levels. The distribution company would be responsible for reading the capacity meters and making the capacity payments to the general customer-generator. These would be required on at least a 12 monthly basis, with terminal payments made on request. The distribution company could appoint an agent, who may be a retailer or other third party (such as a meter reading company) to transact these operations. A reasonable handling charge of 5-10% for the meter reading and data processing services would be acceptable. Net metering This capacity metering proposal is very pertinent to the issue of net metering for small consumer-generators, and inherently provides for the proposed metering of exported energy. In this discussion, we take no position for or against net metering, but merely point out that the inclusion of capacity metering will also provide total export energy. Industrial Research Page 66 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources If the existing revenue meter contains a reverse stop, the imported energy is as recorded, and the exported energy is simply the sum of the registers on the new capacity meter. If the existing revenue meter nets import and export, then the total import energy is this reading plus the sum of the registers on the new capacity meter. Export energy is the sum of the registers on the capacity meter, as before. If it is decided under the regulations that total energy export metering is required, the distribution company and relevant retailer would need to enter an agreement regarding collection and sharing of this data. Summary This metering approach provides a simple, cost effective way to record and reward capacity support from micro-DG. œ Statistically accurate capacity pricing signals for the customer-generator to respond to œ Capacity needs managed by distribution companies in a similar way to load control, but not directly controlled œ Generation equipment owned, operated and maintained by customer, so minimum administrative overhead œ Flexible options for encouraging efficient load profiles in response to local needs We strongly recommend that this or a similar approach to value the capacity supplied by micro-DG be adopted by the Electricity Commission under the new distributed generation rules. Industrial Research Page 67 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources References [1] Facilitating Distributed Generation – A discussion paper, Resources and Networks Branch, MED, September 2003, ISBN 0-478 26350-3 [2] The Economics of Grid Connected Hybrid Distributed Generation, I Sanders and A I Gardiner, 2003 Annual EEA Conference, Christchurch; and, The Economics of Mini-Scale Embedded Wind-Diesel Generation, (elaborating on the EEA publication), available at http://www.irl.cri.nz/electrotec [3] Orion Distributed Generation Information Pack, obtainable from Orion New Zealand Limited, PO Box 13896, Christchurch, New Zealand [4] Possible Impact of Micro-Scale Distributed Energy Technologies on Existing Supply, A I Gardiner, I A Sanders, Industrial Research Limited, NZ Energy Conference 2002, Wellington, NZ, 7-8 October 2002 [5] Are Microgrids the Answer for Post 2013?, A I Gardiner and I A Sanders, Electrical Engineers Association of NZ Conference, Christchurch New Zealand, 21-22 June 2002 [6] A Renewable Resource Assessment Atlas of New Zealand, I A Sanders and A I Gardiner, EnergyWise News, EECA, June 2000, Issue 66, pp28-30 [7] Deferment of Upgrades to Weak Lines Through New Technology, R D Brough and A I Gardiner, Electrical Engineers Association of NZ Conference, Christchurch New Zealand, 21-22 June 2002 [8] http://www.energyfoundation.org/documents/costmethod.pdf Industrial Research Page 68 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Appendix Five: Legislative Frameworks for DG Facilitation Introduction The policy approach presented by Government in its DG Discussion Paper [1], represents a rather reactive approach to DG facilitation by electricity market incumbents. The justification for imposing DG regulations is that: “it continues to be difficult to determine what are the likely requirements for connection to the lines network, how costs are going to be shared, the nature of the connection contract and the expected timeframe to conclude a contract” [2]. Government can instead build on existing best-practice employed by various stakeholders in the NZ electricity market, and develop this knowledge base further – but as a result of taking the policy approach espoused in the discussion document, a limited customer-driven, utility-response view of DG facilitation has been created. There are three underpinning facts why this approach is limited and will stifle both competition, and the growth of DG in New Zealand. Fact One: Greater direction (and hence control) of the application process for issuing Resource Consents has been granted to local Councils [3]. Each local Council has its own criteria and prejudices for assessing individual resource consents. When the business interests and political agendas of Councils and DG-operators/owners clash: e.g. in the case of Environment Canterbury (ECAN) versus Orion Networks over fuel-driven DG emissions restricted to peak demand periods, lengthy delays may result. Project Aqua is a classic case of resource consent delays increasing project costs and delaying revenue streams vital to its financial viability [4]. Fact Two: “Difficulties in obtaining long-term agreements to sell electricity to a retailer or major customers have also impeded investment” [5]. Industrial Research can attest to this fact through contact with various prospective and existing non-retailer DG operators/owners. As a result, Industrial Research knows of at least three vertically- integrated generator-retailers in New Zealand who resist / complicate attempts by independent DG producers to sell electricity to them at a reasonable price [6]. Industrial Research described this problem three years ago to the Ministerial Board of Inquiry into the Electricity Market [7]. The problem still remains and in its present form, represents a major impediment to DG facilitation in New Zealand. EECA has also described “the lack of standard agreements for electricity retailers to purchase surplus electricity” [8] as a barrier to developing the DG market in New Zealand. At the moment, energy retailers favour power purchase agreements with major customers whose business retention is worth more than any inconvenience caused by accommodating the purchase of DG electricity exports [9]. Fact Three: “Transmission and lines businesses are not obliged by necessity to include the impact of network embedded and distributed generation assets, or energy management and conservation measures on their network infrastructures” [7]. As a consequence, personnel responsible for infrastructure asset valuation, management and Industrial Research Page 69 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources planning do not consider outsourcing network capacity options. This is the main reason why a customer-driven, utility-response will not create the market environment for the DG industry to develop significantly in New Zealand. “ Renewal accounting of infrastructure assets for performance measurement derivation in transmission and lines businesses, should clearly stipulate that energy management and conservation measures, and network embedded and distributed generators, must be valued on an equal basis with other infrastructure assets. Equal status and financial weighting should be granted to these other options in order to maximise the economic and environmental sustainability and efficiency and reliability of new infrastructure asset management plans, taking into account the latest technologies and techniques” [7]. Until this is done, there will be very little incentive for lines networks to support (let alone encourage) grid-connected DG delivering capacity-support. For justification of this statement, take a look at the alternative tactics adopted by different lines companies for hooking-up and costing the interconnection of a Windflow wind generator to their networks [10]. In order to promote effectively the connection of distributed generation (DG) to distribution networks, appropriate standards, regulations and fair business practices must be applied. The effectiveness of these procedures will determine DG penetration in the New Zealand Electricity Market. Different regulatory strategies will have a greater or lesser chance of succeeding, depending on the legislative framework adopted for facilitating DG in New Zealand. This paper looks at different legislative frameworks for tackling this issue and how much impact they might potentially have on facilitating DG in New Zealand. Legislative Frameworks for Facilitating Distributed Generation There are four successive legislative frameworks that can be adopted for facilitating DG in New Zealand under the prevailing de-regulated electricity market environment that exists. These frameworks in order of progression are: A. Customer-Driven, Utility-Response Framework; B. Least-Cost Utility Asset Management Framework; C. Utility-Driven, Customer Response Framework; and, D. Temporal-Locational Market-Driven Framework. Each legislative framework will be discussed briefly in order of progression, building on the arguments presented in the framework preceding it. A. Customer-Driven, Utility-Response Framework The market incumbents permit DG facilitation on their terms. Prevailing energy supply and energy delivery rules dominate new regulatory arrangements. The Status Quo is more or less defended / protected. Industrial Research Page 70 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources The Customer-Driven, Utility-Response framework follows the philosophy underpinning the suggestions proposed in the Government Discussion Paper (GDP) for facilitating DG in New Zealand [1]. The underlying assumption is “ that the (New Zealand) investment environment is one of flexibility which should encourage investment in distributed generation that is seen as commercially attractive” [11]. From our experience at Industrial Research, if this were true, there should be a greater number of commercially viable DG systems operating in the New Zealand market today. This is not the case because of the lack of adequate disclosure of locational network capacity costs. There are no technical or commercial reasons why many more DG systems could not operate profitably in New Zealand today. This fact is borne out by numerous publications that have been written on this topic by Industrial Research over the past five years [12]. The primary focus of the Customer-Driven, Utility-Response framework in facilitating DG, involves preserving the vested interests of electricity market incumbents while minimising the risks associated with opening up the market to new entrants. The responsibility lies almost entirely with new DG market entrants to create and exploit opportunities that will lead to the establishment of sustainable DG businesses / projects. This process involves the prospective DG-owner / -operator initiating project proposals with various market- / regulatory-stakeholders in order to secure DG-interconnection rights, resource consents and power purchase agreements etc. The time-frame and budget allowed for this process, must be sufficient to ensure that the financial viability of the DG proposal is not compromised, if and when permission has been granted for the proposal to proceed (and provided no additional costs or time delays have been incurred). No business worth its salt would bother to investigate DG investment opportunities under this legislative framework unless they had prior knowledge of the overall impact on their bottom line of potentially costly and lengthy consultations with utilities and regulators – including arguments regarding the technical and economic impact of DG on the lines network (and providing adequate compensation to satisfy the parties involved). This represents a hit and miss affair regarding the technical and financial feasibility of DG proposals from a prospective investor’ s perspective, and depends heavily upon prior knowledge of the strengths and weaknesses of the electricity supply and delivery infrastructure in the locality of the sites proposed. In many cases, this information is not known, resulting in additional time and expense for the lines network company to furnish the prospective DG investor with the information to decide whether a business proposal is worth preparing (yet alone pursuing). Under this arrangement for facilitating DG, utilities will have to identify potential DG interconnection sites for base, intermediate or peak generation. Most likely the utilities will not have conducted interconnection studies for these sites, but based upon current knowledge of general system conditions, will at least select sites that are less likely to cause severe system impacts and expensive network upgrades. Interested DG-operators will still need to independently evaluate each site and assume all the risks should they decide to install DG at one or more sites. Furthermore, any or all of the identified sites may require appreciable network upgrades or may be otherwise unsuitable for a variety Industrial Research Page 71 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources of reasons. Accordingly, the utilities will not warrant or otherwise guarantee the suitability of these sites for the DG-operator to locate new generation on their systems. This represents considerable extra cost to any prospective DG investor, on a project whose financial viability may be marginal at best. This process alone could eat up any profit an investor stands to make by proceeding with a ‘commercially viable’ project. The complex nature of modern electricity planning, which must satisfy multiple economic, technical, social and environmental objectives, requires the application of a regulatory planning process that integrates these often-conflicting objectives and considers the widest possible range of traditional and alternative energy resources. The availability of timely and accurate information on temporal-locational energy-capacity requirements is a prerequisite to informed investor decision-making for facilitating DG – and the basis for developing the three legislative frameworks described next. B. Least-Cost Utility Asset Management Framework Utilities are required to adopt and apply procedures that regularly consider and compare DG opportunities as a valid least-cost alternative to conventional wires and cables business operations. Information disclosure of these asset management practices is required. In New Zealand today, “ Least-Cost Utility Asset Management” is not required for planning lines network company operations or for managing their assets. Basic Asset Management (BAM) practices – defined as the initial level designed to meet minimum legislative and organisational requirements for financial planning and reporting – are applied. BAM requires basic technical management outputs such as: statements on current levels of service, forward replacement programs and associated cashflow projections. BAM will not optimise supply and demand-side investments and returns at the distribution level, nor encourage a cohesive policy and business framework for including distributed generation-demand response measures in utility asset valuations. Advanced Asset Management (AAM) practices on the other hand, will achieve “ Least- Cost Utility Asset Management” , by optimising the activities and programs required to meet optimum (agreed) service standards at minimised lifecycle costs. The objective is to look at the lowest long-term cost (rather than short-term savings) when making AAM decisions. AAM requires the development of management tactics based on collection, analysis and dissemination of key information on asset condition, performance, lifecycle costs, risk costs and treatment options. Selecting appropriate AAM requirements and standards for utility asset valuations will depend upon the following criteria: (1) Costs and benefits to the utility; (2) Size, condition and complexity of the assets; (3) Risk associated with failures; (4) Skills and resources available to the utility; (5) Customer expectations; and, Industrial Research Page 72 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources (6) Legislative requirements. Legislative requirements is the most important criterion, defining the parameters affecting the scope of all the other criteria. Appropriate legislative requirements will benefit utility accountability, service management, risk management and financial efficiency. These benefits are summarised in table one below. Table 1: Benefits from Better Legislation of Infrastructure Asset Management Practices A. Improved stewardship and accountability 1. Demonstrating to owners, customers by: and stakeholders that services are being delivered effectively and efficiently. 2. Providing the basis for evaluating and balancing service / price / quality trade- offs. 3. Improving accountability for use of resources through published performance and financial measures. 4. Providing the ability to benchmark results against similar organisations. B. Improved communication and relationships 1. Improving understanding of service with service users by: requirements and options. 2. Formal consultation / agreement with users on the service levels. 3. More holistic approach to asset management within the organisation, through multi-disciplinary management teams. 4. Improved customer satisfaction and organisation image. C. Improved risk management by: 1. Assessing probability and consequences of asset failure. 2. Addressing continuity of service. 3. Addressing the inter-relationships between networks (the chain is only as good as its weakest link) and risk management strategies. 4. Influencing decisions on non-asset solutions through demand management. D. Improved financial efficiency by: 1. Improved decision-making based on costs and benefits of alternatives. 2. Justification of all costs of owning / operating assets over the lifecycle of the assets. A formal approach to the management of infrastructure assets is necessary to provide services in the most cost-effective and technically efficient manner, and to demonstrate this to customers, investors and other stakeholders. The key to achieving major change in the emerging deregulated electricity market, is to develop a cohesive policy and business framework for including distributed generation-demand response measures in utility asset Industrial Research Page 73 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources valuations. The goal of appropriately legislated utility infrastructure asset management is to meet a required level of service in the most cost-effective way through the creation, acquisition, maintenance, operation rehabilitation and disposal of assets to provide for present and future customers (see figure one). A S S E T M A N A G E M E N T P L A N N IN G A U D IT R E V IE W D IS P O S AL / P L AN N IN G R A T IO N A L IZ A T IO N S T R A T E G IE S M IN IM IS E D C R E AT IO N / R E P L AC E M E N T L IF E C Y C L E A C Q U IS IT IO N COSTS R E H AB IL IT A T IO N F IN AN C IA L / R E N E W AL M AN AGEMENT C O N D IT IO N S & P E R F O R M AN C E O P E R AT IO N S M O N IT O R IN G M A IN T E N AN C E L ife - c yc le a ss et m a n a g em e n t m ea n s co n s id e rin g a ll m an ag em en t as s em ea co anag en o p tio n s a n d strateg ies as p a rt o f th e as s et life - c yc le, fro m an s tra teg se t le , p la n n in g to d is p o sa l. isp s a A S S E T M A N A G E M E N T P L A N N IN G A sse t P lan n in g : inv o lv e s c onfirm ing th e se rv ic e tha t is req uired fro m th e cu s to m er a nd ens uring th at th e ss et la n olv es co nfirm in g the s ervice th at re quire d from the c us an d en s urin g that the prop os ed as se t is the m o st effectiv e s olu tion to m e et th e cu s tom er ’ s n ee d. p ro po se d a ss et ost effe ctiv so lu tio n ee t the c us e r ne ed. A sse t C rea tio n / A c q u isitio n : is the p rov isio n of, o r im prov em ent to, a n as s et w h ere the o utlay c an re as on ably ss et reatio cq is itio th e prov ision or p rov en t an a s se t e re th e ou tla y ca n reas o nab ly be ex pe cte d to prov ide b ene fits be yon d the yea r o f o utla y. A v a lu e m a na gem en t ap proac h m ay b e a dopte d to b e e xp ec ted id e be nefits b eyond th e ye ar of outla lue an ag em e nt a pp ro ac a y be ado pted prod uc e the e c ono m ic and c reativ e s olutio ns . p ro du ce ec o nom an d re ativ o lu tions F in a n cial M an ag e m ent : re quires th e re co gn ition o f all co s ts as s oc ia te d w ith a ss e t ow n e rs hip , inc lu din g an c ia l a g em e nt require s the recog nitio n of a ll c os a s so ciated as s et o w ersh ip, in clud ing c re ation / ac qu isitio n, ope rations, m ainten an ce , reh abilita tio n, re new a ls , d ep re cia tion a nd disp os al, an d s upp orts rea tion a c quisition , o peration s, aintena nc e, re ha bilitation, rene w ls, de prec iatio n an d dis po sa l, a nd up po rts c os t-e ffec tiv e de cis io n - m aking. o st - effec d ecision aking . A sse t O p eratio n s an d M ain ten an c e : func tion s relate to the da y - to - d ay run nin g and upk e ep o f a ss ets, a nd the ss et e ra tio an a n ce fun c tions re la te th e d ay da y ru nn ing an d u pk eep of as se ts, an d as s oc iate d c osts are partic ularly s ign ifican t for dyna m ic / sh ort- liv ed a ss ets. a s so ciated co sts p artic ula rly sig nific ant d yn am s ho rt e d as s A sse t C o n d itio n / P erfo rm a n ce : w h ere as s et pe rform an ce relate s to the ability o f th e as s et to m e et targ et ss et e rfo an c e he re a ss e t p erfo rm a nc e re la tes th e a bility of the a ss ee t targe t lev e ls of s erv ic e, an d as s et co nd itio n refle cts the ph ys ic all s ta te of the as s et. M o nitoring a ss e t c on dition a nd le v els o f e rv a nd a ss e t c on dition reflec ts th e phys ica of se t. onito rin g as s et o nditio n an d pe rfo rm a nc e throug ho ut th e as s et life - c yc le is im po rtan t in o rd er to id entify u nde r-perform ing a ss e ts or th os e, p erform an ce th ro ugh out the a s set cyc p orta nt order iden tify un der- pe rfo rm in g as s ets o r thos w hic h are a bo ut to fail. hich ab out fa il. A sse t R eh a b ilitatio n / R e p lac em e nt : is th e s ignific an t u pgrading or re plac em ent of an as set or as s et ss et e h ab ilita tio ep la ce m ent the ig nifica nt upg ra din g rep la ce m en t o f a n a ss et a s set c om p on ent to re store the as set to its requ ire d fun ction all co nd itio n a nd p erform a nc e. A s s et m an ag ers ne ed to o m po ne nt res tore th e a ss et re quired fu nc tio na c on dit ion an d perform anc e . se t a na gers n ee d be ab le to iden tify the op tim um lon g - te rm s olutio n throu gh a form all de cis io n - m ak ing p ro ce s s. b e a ble id entify optim lo ng term so lu tion th ro ugh fo rm a d ec is ion a king proc es s . A sse t D isp o s al / R atio n alisatio n : is a n op tion w he n an as se t is no lon ge r required, bec o m es une c o no m ic all to ss et is p sa l a tio a lis atio an o ptio n h en a n s et n o lo ng er re quired, b ec om e s u nec n om a m a intain or re ha bilitate. It prov ide s the op po rtu nity to rev iew th e c onfigura tio ns , type a nd lo ca tion o f a ss ets, a nd ain tain reh ab ilita te. id es th e opp ortunity ie w the onfiguration s, typ e an d loc ation of as se ts, an d the s e rv ice de liv ery p ro ce s se s relev an t to the activ ity. th e erv ic e d eliv proc es s es rele v a nt th e a ctivity. A sse t M a n ag em en t A u d it / R eview : inv o lv e s c a rrying ou t re gu la r inte rn all an d ind epe nd - ss et an e m e view es arryin g o ut reg ular interna a nd indep en d en t a ud its to en sure a c o ntinu ous as s et m an age m en t im p rov em en t c yc le , a nd to ac hiev e / e nt au dits e ns ure ontin uou s a s set a nag em ent prov ent an d a c hie v m a intain best in du stry prac tice . ain tain b est ind us try p ra c tic e. Figure 1: Least-Cost Utility Asset Management Industrial Research Page 74 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources The key is to develop a cohesive legislative framework for including distributed generation-demand response measures in utility asset valuations. Infrastructure asset management and valuation issues influence DG investments, through the creation, acquisition, maintenance, operation, rehabilitation and disposal of assets to meet a required level of service. These issues include: adopting lifecycle costing (see figure one), developing cost-effective management strategies for the long-term, providing a defined level of service and monitoring performance, managing risks. C. Utility-Driven, Customer Response Framework Utilities develop the necessary tools to provide a consistent and thorough assessment of DG load management and capacity-support benefits as part of their regular business operations, and publicly disclose all the relevant information, in a timely and accurate manner for independent prospective DG investors / operators to respond. The “ Utility-Driven, Customer Response Framework” develops further the concepts introduced for the “ Least-Cost Utility Asset Management Framework” . Distribution costs vary significantly between utilities and between locations within utilities. Marginal costs also vary significantly by time of day and year. Where, and when, marginal distribution costs are high, there are often cost-effective opportunities for local DG to delay or eliminate the need for distribution system investments. Utilities vary significantly in the degree to which their existing data, planning processes, and analytical methods are suitable for considering DG alternatives. Few utilities have a well- developed process for considering DG. Government legislation can significantly improve this situation by taking appropriate measures to develop objectives and strategies that oblige all utilities to adopt improved costing methods. Utilities should be in a position to identify the best opportunities for implementing DG projects, and encourage / discourage (as appropriate) independent DG investments via incentives / disincentives derived from the underlying drivers influencing the value or cost to the utility (table two). Table 2: Underlying Drivers of Value / Cost Driver Description Many drivers of cost can be characterised broadly by distinctions such as Location Remote vs. Urban, Constrained vs. Unconstrained, and Mild vs. Extreme Climate. The magnitude of the growth relative to capacity sets both the timing and Load Growth the magnitude of action required, and with it scales the magnitude and timing of investment. Customer-sited generation growth will impact the load seen by the utility as well, and may become an important element to consider in load forecasting. Distribution project alternatives that have time varying load carrying Load Shape capability must correlate with the peak periods in order to provide any value, so the load factor and peak timing have an impact on net cost. Industrial Research Page 75 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Table 2: Underlying Drivers of Value / Cost (continued) Driver Description The vintage, performance, and specifications of the equipment already in Equipment place represent both opportunities and constraints for feasible solutions. Characteristics The availability, cost, maintenance and service requirements, spare parts Operational Details issues, and reliability features of new equipment alternatives determine the technical and economic capabilities for possible solutions. Available incentives, the possible methods of financing, taxes, and Financial Parameters budget constraints set or alter some costs and benefits, and may dictate some of the project priorities. Higher discount rates favour least first-cost solutions, and the net benefit or cost-benefit ratio can be very sensitive to the discount rate - slight adjustments can in many cases flip the ranking of two alternatives. If there are interactions between two projects such that two projects Synergies together are more valuable than the individual projects considered separately, then looking only at individual projects alone will miss possibly important cost savings opportunities. Direct costs can depend significantly on the attainment or non-attainment Environmental area status of the location and local permitting regulations and fees. Considerations Quality and reliability levels in the area depend not only on equipment Power Quality and (above) but also vegetation and climate. The realized customer outage Reliability costs further depends on the local customer value of service and customer demographics. Uncertainty in data, forecasting, regulatory climate, and cost estimates Uncertainty drive risk and strategic value, but also lead to risk averse behaviour by planners due to fear of being wrong (e.g. slightly overbuilding or over- forecasting as a slight overcapacity has fewer repercussions than slight under-capacity). Are there opportunities or requirements related to public relations, Intangibles goodwill, learning, political necessity, etc.? The underlying drivers of value / cost described in table two, could be used to inform prospective DG-owners / investors via appropriate information disclosure and dissemination, of the temporal-locational costs or benefits associated with any particular DG investment. In other words, sufficient information should be supplied by the utility in order for the prospective investor to make an informed choice about whether a DG pre- feasibility study for a particular site should proceed. Appropriate utility information disclosure would have to be based upon improved costing methodology for electric distribution planning [13]. The most important objectives and strategies for improved costing methodology are summarised in table three below. Industrial Research Page 76 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Table 3: Objectives and Strategies for Improved Costing Methods Objective Strategy 1. Know where costs are high. Differentiate distribution costs by location. 2. Know when costs are high. Differentiate distribution costs by time of day and year. 3. Formalise the evaluation process. Formally compare distribution system improvements to the most promising DG alternatives at the most important locations. 4. Increase effective lead time. Consider DG alternatives as early as possible in the planning process. 5. Ensure effective buy-in. Consider the financial interests of other parties in calculating the net costs to distribution utilities. Consider mechanisms to cost-share with other parties, and reflect these in estimates of distribution company costs. 6. Get started with established costs. Consider the role of societal benefits a lower priority issue. 7. Include all costs. Consider factors that are difficult to quantify in making decisions. The objectives in table three contain the key elements for developing a distribution costing methodology [13] that will provide relevant and timely temporal-locational pricing signals for prospective DG owners to decide whether or not to invest in a particular site-specific DG project. This information could be incorporated within current information disclosure practices for lines network companies. Lines network companies for example, using the information provided in table three [13], might be regulated to: 1. Differentiate marginal distribution costs by location. This helps identify areas where DG options are most likely to be beneficial. In doing this, utilities should consider both costs for distribution system enhancements, and revenues by location. Revenues can vary due to customer mix (and resulting differences in rate level and structure) and load profile. Utilities will discover that the financial impacts of load reduction will vary from site to site based on both costs of service and marginal revenues. 2. Differentiate marginal distribution costs by time of day and year. To select an appropriate DG solution, it is particularly important to understand both when the peak loads that drive distribution improvements are occurring, and what is causing those loads. Industrial Research Page 77 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources 3. Formally compare distribution system improvements to the most promising DG alternatives at the most important locations. DG planning is a significant investment of time and money, and should be pursued where it is most likely to bear fruit. If there are questions about applicability, it is important that DG planners take the time to understand the alternatives, and conduct screening analysis to identify potentially beneficial DG applications. Where it is applied, distribution planning should be an iterative process that identifies and compares the costs of several potential options, including DG, to meet distribution system requirements. 4. Consider DG alternatives as early as possible in the planning process. Some DG alternatives have a longer lead time than typical distribution improvements, which are often planned and installed in less than two years. Efficiency programs, in particular, can take several years to reach maximum benefit. To effectively implement long lead-time programs, utilities may need to use alternative methods to their classical planning tools to “ look ahead” . For example, utilities can evaluate load trends at adjacent substations, and focus efficiency programs in areas where there are potential capacity limits several years out. While these long-range planning methods cannot predict the need for capital improvements with certainty, this type of preventative action can reduce the risk of needing “ quick solution” capital improvements. 5. Consider the financial interests of other parties in calculating the net costs to distribution utilities. Other parties, including utility customers, energy service providers, and generators, may gain financial benefits from DG implementation. Where customers are willing to co- invest in efficiency and generation, this reduces the costs of DG alternatives to the utility. Distribution companies should explore these areas of mutual financial interest, but distribution planning should reflect them only as they become practical options. 6. Consider the role of societal benefits a lower priority issue. Benefits can occur to the public at large, including economic development, less pollution, impacts on land use and visual aesthetics, etc. Many US states have in the past created regulatory and rate mechanisms to encourage utilities to pursue energy efficiency to achieve these goals. In some cases multipliers or adders have been established to reflect these values in least-cost planning. Commensurate provisions have also been made in many US states to assure that, where utilities fund initiatives that are rendered cost- effective by these adjustments against their own economic self-interest, they have mechanisms to recover costs and (in some states) achieve additional profit. 7. Consider factors that are difficult to quantify. It is neither practical nor economical to quantify everything that is important for every proposed capital investment. Progress is likely to be faster if distribution planners and their managers use a decision-making process that explicitly considers both quantifiable factors and “ intangibles” . The “ intangibles” could include political and public relations Industrial Research Page 78 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources issues, financial risks that are not formally modeled, environmental and broad economic benefits, and so on as appropriate. For most if not all utilities in New Zealand, the practices proposed in the “ Utility-Driven, Customer Response Framework” differ substantially from existing regulatory requirements and business operating practices. There are however, concrete steps that Government and utilities can take to adopt a more practical, proactive approach to optimum DG facilitation in New Zealand. “ These initial steps consist primarily of evaluating current status and developing a vision and roadmap for improving costing practices” [13]. This concept is developed further in the next section, under the “ Temporal-Locational Market-Driven” legislative Framework for facilitating DG. D. Temporal-Locational Market-Driven Framework Complete cost transparency of temporal-locational market-driven prices should be site- specific and publicly disclosed. The greater the number of sites listed, the greater the probability that prospective DG operators will identify economically viable time- and location-specific DG investment opportunities. This is necessary for sustainable DG facilitation in New Zealand. “ The key concept of de-regulation in nearly every nation is that no one company should have a monopoly on either the production, the wholesale sale, or the retail sale of electricity and electricity-related services” [14]. Under this arrangement, DG-operators should be able to competitively negotiate the supply of electricity to energy retailers or customers at temporal wholesale prices (i.e. a Time-of-Use wholesale market price) or at a pricing-schedule equivalent to the existing energy pricing contract between a typical energy retailer and the DG-operator functioning as an energy consumer (minus a reasonable administration fee of say 5-10% of the energy price). Introducing alternative arrangements outside the wholesale market for transacting DG energy, and matching regional distribution network-specific demand with network-embedded DG, could help facilitate temporal wholesale pricing of DG. The New Zealand electricity wholesale market does not support localised or regional trading of DG under the existing regulatory environment. Significant steps towards rectifying this situation can be taken however, by introducing appropriate policy mechanisms and market incentives encouraging market participants to develop innovative, reliable, and less risky methods of meeting the nation’ s demand for electricity (e.g. a reserve-market for dry-year contingencies). Policies considered or planned include: demand-side bidding and multi-settlements; demand response (participation of load management in spot markets); opening the ancillary services market to DG (e.g. outsourcing network capacity planning); resource aggregation and management; increasing market liquidity; more economically efficient transmission and distribution rate design; and, public benefits programs, including funding mechanisms, in support of investment in long-term end-use energy efficiency [15]. Industrial Research Page 79 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources T o d a y ’s T o m o r r o w ’s C e n tr a l U tility D is tr ib u te d U tilit y ? C e n t r a l G e n e r a t io n C e n t r a l G e n e r a t io n W in d R e m o te G e n se t L oads PV F u e l C e ll B a t te r y C u s to m e r C u sto m er s E ff ic ie n c y M ic r o t u r b in e 1 © 2 0 0 2 D is tr ib u te d U tility A s s o c ia te s Figure 2: The Distributed Utility [16] The distinction between the Temporal-Locational Market-Driven framework proposed in this paper and the traditional central utility structure employed in New Zealand, is exhibited in figure two above. In the left side of figure two, the central hub generation and spoke transmission and distribution has been the typical pattern adopted by utilities to generate and supply electric energy. On the right side, a schematic of the possible structure of a future utility is presented. The schematic indicates that central station power plants will be integrated with distributed generation and storage devices. In certain cases, utility owned or operated modular generation technologies can serve remote loads that are too expensive to connect to the utility grid. Locationally Based Marginal Costing (LBMC), is “ a method of transmission pricing in which the actual (technical) cost of power at every location in the power grid is computed using some mutually agreed upon method, and the price for power transmission between any two points in the grid is then defined as the difference in the computed local prices” [14]. These prices are determined from electricity transmission losses and transmission limits (network constraints). These physical impediments to electricity trading cause competitive prices to differ, the difference being the price of congestion [17]. As a result, if the transmission line between two locations is inadequate to handle the desired trade between those two locations, the downstream location will be forced to buy power from more expensive local generators. This will raise the local price of power relative to the remote price, which is a standard competitive result and has nothing to do with centralised computation. This standard procedure is used by Transpower to charge distribution networks for delivering capacity to the Grid Exit Points (GXPs) where electricity transmission ends, and local / regional electricity distribution begins. Locationally Based Marginal Costing is used by Orion Networks to Industrial Research Page 80 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources determine the temporal-locational value of DG capacity within its electricity distribution region [18,19]. At the moment, Orion Networks provides a single temporal-locational value for DG capacity contributions, alleviating annual electricity distribution constraints impacting its GXP locational fees to Transpower (the national transmission network operator). If the utility-driven, customer-response costing methodology was adopted universally by distribution networks throughout New Zealand, temporal-locational values for DG capacity contributions could be calculated individually for every GXP and every feeder station as well. The consequence of such a move would be to create a realistic picture of the net-worth of DG capacity contributions throughout the distribution networks. Such a move would create the “ ultimate” secondary temporal-locational energy-capacity market for facilitating DG in New Zealand. Summary The impact these different frameworks are likely to have on the New Zealand electricity market, are summarised in figure three. It is interesting to note that the pace of technological change usually precedes the pace of business innovation required to accommodate the new (technologically-enabled) opportunities realised. Furthermore, the commercial market derived from the prevailing regulatory / legislative environment is usually even slower to respond to the new business requirements identified (to make the new opportunities work). Some businesses may encounter minimal resistance to creating new markets, simply because appropriate legislation has not been developed yet and regulations do not exist: for example, the internet in its early days. Other businesses however, may encounter stiff resistance to proposed market changes, especially if entrenched market positions of market incumbents are threatened: for example, telecommunications and electricity. Conclusion According to a recently released report by the US Congressional Budget Office [20], “ If the new rules and prices are well designed, the cost of providing highly reliable electricity service to customers who desire it and the total cost of serving all customers will probably fall as distributed generation becomes more widely used.” This paper describes four progressive frameworks for facilitating this new rule- and price-making process (see figure four). Industrial Research Page 81 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Increasing Value Creation for NZ Market (Proactive) Accelerated Growth & Expansion of DG Market Increased Control Govt & Utility Constrained DG Market Govt & Utility Sustained DG Market & Containment of DG Market A B C D Customer- Least-cost Utility-driven, Temporal- driven asset customer- locational utility- management response market driven response framework framework framework framework SOE’s retain market share of NZ’s generating capacity Lines companies & IPP’s increase market share of NZ’s generating capacity Increased Risk MitigationFor Electricity Market (Reactive) Figure 3: Impact on New Zealand of Adopting Different Legislative Frameworks for Facilitating DG The United States Department of Energy, Office of Electric Transmission and Distribution goes further by stating that: “ A breakthrough is needed to eliminate the “ political log jam” and reduce the risks and uncertainties caused by today’s regulatory framework. This includes clarifying intergovernmental jurisdiction, establishing “ rules of the road” for workable competitive markets wherever they can be established, ensuring mechanisms for universal service and public purpose programs, and supporting a stable business climate that encourages long-term investment” [21]. In New Zealand, “ today’s regulatory framework” has been created by a de-regulated electricity industry whose market incumbents have historically been given extensive freedom to self- regulate. This is not a position that market incumbents will willingly concede. As a result therefore, any “ political log jam” that ensues from government proposals to legislate change, will most likely be a consequence of new regulations being imposed on an industry wanting to retain its independence. New regulations have been discussed in Industrial Research Page 82 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources this paper for facilitating market improvements. In order for these new regulations to work, market incumbents will have to benefit from such improvements, to prevent / lessen resistance to the changes proposed. These changes therefore, will have to be negotiated in partnership between market incumbents and new market entrants if sustainable and profitable facilitation of DG in the New Zealand market is to take place. (C) “Advanced” Utility-driven, (D) “Ultimate” customer-response Temporal-locational, Framework Market-driven Framework Emerging Energy Market D Value-adding C “extensions” to existing Increasing B Increasing market Market-efficiency structure (A) Value-creation A (B) “Progressive” (A) “Business as Usual” Least-cost utility Customer-driven, asset management utility-response Framework Framework Current Govt. Position on Facility DG Figure 4: The Consequences of Adopting Alternative Legislative Frameworks on DG Facilitation Industrial Research Page 83 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources References [1] Government Discussion Paper (GDP), “ Facilitating Distributed Generation’ , Ministry for Economic Development, September 2003. [2] GDP Paragraph 44. [3] GDP Paragraph 34. [4] The Press, “ Project defended” (Perspective, page A11), October 30th 2003. [5] GDP Paragraph 39. [6] Refer to Pupu Springs Hydro’ s submission to the Electricity Inquiry in 2000 on Energy Retailer Power Purchase Agreements, entitled: “ Submission #3: Pupu Hydro Society” , found at: http://www.electricityinquiry.govt.nz/submissions/index.html. [7] Refer to Industrial Research’ s submission to the Electricity Inquiry in 2000 on DG impacts of the existing NZ Electricity Market, entitled: “ Submission #343: IRL Electrotec Group” , found at: http://www.electricityinquiry.govt.nz/submissions/index.html. [8] GDP Paragraph 43. [9] This was the case when Christchurch City Council wanted Meridian to supply 3% of its electricity demand from Windflow’ s 500kW wind turbine on the Banks Peninsula. Only when Christchurch City Council threatened to switch energy retailers did Meridian Energy comply with their request. [10] Refer to the Orion Networks website at: http://www.oriongroup.co.nz for details, including: http://www.oriongroup.co.nz/community/envissues/DG%20Research.pdf for information regarding alternative approaches taken by lines companies for rewarding / penalizing capacity-support from customer-driven DG. [11] GDP Paragraph 33. [12] Refer to Industrial Research’ s DG website at: http://www.irl.cri.nz/electrotec/ [13] This methodology is discussed in a report of the same title found at http://www.energyfoundation.org/documents/CostMethod.pdf [14] Philipson, Lorrin. and Willis, H. Lee, “ Understanding Electric Utilities and De- Regulation” , Marcel Dekker, Inc., New York, 1999. [15] Weston, F. et al., Accommodating Distributed Resources in Wholesale Markets, The Regulatory Assistance Project, Montpelier, Vermont, September 2001. [16] Chapel, S et al., Distributed Utility Valuation Project Monograph, EPRI Report TR- 102807, Final Report, June 2000. [17] Stoft, S., Power System Economics: Designing Markets for Electricity, IEEE Press, Wiley-Interscience, New Jersey, 2002. [18] The Economics of Grid Connected Hybrid Distributed Generation, I A Sanders, A I Gardiner, Electricity Engineers Association of NZ, Christchurch, 20-21 June 2003. [19] Full 57-page report (of the EEA paper described in [2]) entitled: “ Wind-Diesel Hybrid Potential” may be downloaded from: http://www.irl.cri.nz/electrotec/downloads/IDES.html [20] Prospects for Distributed Electricity Generation, The Congress of the United States, Congressional Budget Office, September 2003. Industrial Research Page 84 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources [21] Grid 2030, A National Vision for Electricity’ s Second 100 Years: Transforming the Grid to Revolutionize Electric Power in North America, United States Department of Energy, Office of Electric Transmission and Distribution, July 2003. Industrial Research Page 85 Dr. Iain Sanders
    • Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources Industrial Research Page 86 Dr. Iain Sanders