Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources




       Decentralised Capaci...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources




Contents


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Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources




Glossary

AC               ...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources




Abstract

This report descr...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources




Terms of Reference

The New...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources




Acknowledgements:


This re...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources




Summary

S1. New Zealand ne...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources




Introduction

1.    For the...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources



      supply power (measured...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources



      technology combination...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources




Scenarios Examined

10.   T...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources



         B. 550kW Combined D...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources




Distributed Generation Oper...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources



      Meridian Energy’ s maj...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources



      capacity monitoring ar...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources



      are summarised in figu...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources




MCLM Operating Scenario


E...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources



      of the results from us...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources



      periods over the past ...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources



34.   Since AC is charged on...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources




IPP Operating Scenario


En...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources



Table 7: Winter Peak Period ...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources



      network is $500.05/yea...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources




Other Potential Benefits Av...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources




Resource Inputs

45.   Wind...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources




Main Results of the Scenari...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources



      option dependent on CP...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources



          49. The table 13 r...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources



Table 14: Equivalent Earning...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources



Table 15: Summary of Annual ...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources



                       contr...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources




55.                        ...
Decentralised Capacity-Support for Distribution Networks from Distributed Energy Resources




Comparing the MCLM and IPP ...
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
The Economics of Grid-Connected Hybrid Distributed Generation
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

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The Economics of Grid-Connected Hybrid Distributed Generation

  1. 1. 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
  2. 2. 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
  3. 3. 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
  4. 4. 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
  5. 5. 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
  6. 6. 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
  7. 7. 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
  8. 8. 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
  9. 9. 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
  10. 10. 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
  11. 11. 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
  12. 12. 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
  13. 13. 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
  14. 14. 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
  15. 15. 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
  16. 16. 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
  17. 17. 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
  18. 18. 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
  19. 19. 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
  20. 20. 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
  21. 21. 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
  22. 22. 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
  23. 23. 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
  24. 24. 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
  25. 25. 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
  26. 26. 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
  27. 27. 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
  28. 28. 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
  29. 29. 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
  30. 30. 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
  31. 31. 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
  32. 32. 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
  33. 33. 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

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