The effect of wind on chp


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The effect of wind on chp

  1. 1. The Effect of Wind Generation on Combined Heat and Power by Eamon Keane The thesis is submitted to University College Dublin in part fulfillment of the requirements for the degree: ME in Energy Systems School of Electrical, Electronic & Mechanical Engineering Supervisor: Prof. Mark O‘Malley August 2010
  2. 2. Acknowledgements I would like to thank my supervisor, Prof. Mark O‘Malley, for his assistance in getting me started on this project and arranging meetings. My supervisor at Dalkia, Patrice Berthaud, was a great help in providing the financial model for the CHP plant. This report would not have been possible without the kind assistance and data provided by Kevin Hagan and Aoife Crowe from the Commission for Energy Regulation. I also extend my gratitude to Pádraig Fleming from Bord Gáis for providing data and guidance. John Fingleton and Michael Peters from Fingleton White gave me good perspective on CHP during the project. Any mistakes are my own. Many thanks go to Veolia Environment and the Ireland Fund of France for their generous sponsorship of this effort. I would like to thank Dr. David Timoney for co-ordinating the collaboration with Dalkia and for organising the energy systems course. I extend thanks to the French ambassador, Yvon Roe D‘Albert, for hosting an unexpected sponsorship ceremony at the French Embassy. Disclaimer Whilst this report strives to ensure as high a level of accuracy as possible, I make no warranties or representations of any kind with respect to: (a) the content of this document (including without limitation its quality, accuracy, and completeness), or (b) the content of any other document [or website] referred to [or accessed by means of a hypertext link through this document]. Use of this document and the information it contains is solely at the user's own risk. I accept no liability for any loss or damage arising from use of this document or reliance on the information it contains. Where other work is cited, readers should refer to the disclaimers attached to that work. ii
  3. 3. Abstract Combined Heat and Power (CHP) offers substantial savings on carbon emissions. A megawatt of CHP can reduce CO2 emissions by the same amount as 1.5MW of wind. However the large scale introduction of wind into the electricity sector can reduce CHP‘s profits. Policy makers should be aware that by supporting wind, there may be an increase in CO2 if a marginal CHP unit shuts down. This report uses modelled 2020 electricity price data from a previous study to examine the effect of wind on CHP profitability. The cost-effectiveness of a 1MWe CHP was tested with different natural gas and electricity price assumptions. The effect wind has on electricity prices was found to be strongly dependent on the natural gas price and the generation margin. As a share of all-island generation, 30% wind was found to reduce daytime electricity prices by an average of 21% compared with a 10% wind penetration under a conceivable 2020 gas and carbon price assumption. It was found that a 30% wind penetration reduces the gross savings of a CHP plant by 19% compared with a 10% wind penetration. This decrease could be offset by regulators levying capacity charges on CHP based on the volume of electricity they import, and not the capacity of their grid connection. iii
  4. 4. Table of Contents Acknowledgements.................................................................................. ii Abstract.............................................................................................. iii List of Figures ........................................................................................ v 1. Introduction .................................................................................... 1 2. CHP Background ................................................................................ 2 2.1. CHP Targets ............................................................................... 2 2.2. Spark Spread .............................................................................. 4 2.3. CHP Barriers ............................................................................... 6 3. Single Electricity Market ...................................................................... 9 3.1. Capacity Payments ...................................................................... 10 3.2. System Marginal Price .................................................................. 11 4. Effect of Wind on Electricity Market ....................................................... 14 4.1. CER Market Impact Study............................................................... 15 4.2. CER Wind Data ........................................................................... 27 4.3. Wind Variability.......................................................................... 28 5. Natural Gas .................................................................................... 30 5.1. Shale Gas ................................................................................. 30 5.2. European Natural Gas Prospects ...................................................... 36 6. Effect of Wind on Natural Gas .............................................................. 38 7. Financial Analysis ............................................................................. 45 7.1. Effect of REFiT ........................................................................... 47 7.2. Effect of Wind on Startup Costs ....................................................... 50 7.3. Effect of Wind on Capacity Payments ................................................ 50 7.4. Financial Savings ........................................................................ 51 7.5. Payback Period .......................................................................... 57 7.6. Carbon Emissions With and Without CHP ............................................ 58 8. Conclusion ..................................................................................... 59 References .......................................................................................... 60 iv
  5. 5. List of Figures Figure 2.1: CHP capacity 1991-2008 including and excluding Aughinish Alumina [10] .... 2 Figure 2.2: CHP deployment scenarios and government 2010 and 2020 targets [10] ..... 3 Figure 2.3: CHP installed capacity by sector (2008) [10] ...................................... 3 Figure 2.4: Industrial sector spark spread 2004 to 2008 [10] ................................. 5 Figure 2.5: Effect of spark gap on payback period of a 1MWe industrial CHP [10] ........ 5 Figure 2.6: Treatment of CHPs when applying for grid connection [13] .................... 7 Figure 3.1: Breakdown of SMP between uplift and revenue [15] ............................. 9 Figure 3.2: Decision paper example of capacity payment breakdown [16] ................ 10 Figure 3.3: Annual capacity payment pot 2007-2011 [17, 19] ............................... 11 Figure 3.4: Merit order for the start of 2008 [20] ............................................. 12 Figure 3.5: Half hourly SMP data February 2008 to May 2010 in €/MWh ................... 13 Figure 3.6: Average SMP February 2008 to May 2010 ......................................... 13 Figure 4.1: Merit order effect at different times of the day [22] ........................... 14 Figure 4.2: Effect of a carbon price on the differential between coal and gas ........... 15 Figure 4.3: Additions to 2005 generation portfolio in AIGS by 2020 [34] .................. 16 Figure 4.4: All-island peak demand extrapolated out to 2030 from 2010 GAR ............ 17 Figure 4.5: Actual wind generation vs. forecast ............................................... 18 Figure 4.6: Wind Forecast Error Duration Curve ............................................... 19 Figure 4.7: Correlation of natural gas price with SMP (assumes €30/tCO2)................ 19 Figure 4.8: Average SMP in Central Scenario vs. Low Growth Scenario .................... 21 Figure 4.9: Surplus capacity 2010-2016 [38] ................................................... 22 Figure 4.10: Simple spark spread for CER scenarios .......................................... 23 Figure 4.11: Lowest 90% Price Duration Curve for selected 2020 portfolios............... 24 Figure 4.12: Top 10% Price Duration Curve for selected 2020 portfolios .................. 24 Figure 4.13: Annual average SMP by half hour of day for Low Fuel Scenario.............. 25 Figure 4.14: Annual average prices by half hour for Low Demand Scenario ............... 26 Figure 4.15: Difference in SMP by hour of day (P1-P5 Low Fuel Scenario)................. 26 Figure 4.16: Half hourly wind generation data for Portfolio 5 .............................. 27 Figure 4.17: Wind generation in CER study ..................................................... 28 Figure 4.18: 10 year monthly wind output variation [42] .................................... 29 Figure 5.1: Shale gas production 2000-2009 [44] .............................................. 30 Figure 5.2: Potential future shale gas production [45] ....................................... 31 Figure 5.3: Shale production rate and breakeven US production prices [45] .............. 32 Figure 5.4: Annual average US wellhead natural gas prices [49] ............................ 33 Figure 5.5: Shale gas exploration sites in Europe [51] ........................................ 35 v
  6. 6. Figure 5.6: Shale gas land footprint [52] ....................................................... 35 Figure 5.7: Shale gas in context of global natural gas production [44] .................... 36 Figure 5.8: EU supply and demand projection to 2030 ....................................... 36 Figure 5.9: Historical and projected natural gas prices ...................................... 37 Figure 6.1: Annual Irish gas demand projections by sector .................................. 38 Figure 6.2: Objective function for a CEGN [56]................................................ 39 Figure 6.3: CCGT power output for high wind, low wind and base case scenarios [56] . 40 Figure 6.4: Linepack for low wind, high wind and base case scenarios [56] .............. 40 Figure 6.5: Wind Generation on 7th January 2010 [40] ....................................... 41 Figure 6.6: 1 in 50 winter peak day demand and max supply capacity [58] ............... 42 Figure 6.7: Daily total Irish gas demand in 2010 and 2030 [59] ............................. 43 Figure 6.8: Daily gas flows in the Irish market in 2020 from various sources [59] ........ 43 Figure 6.9: Flow flex duration curve for Ireland (mscm) [59] ............................... 44 Figure 7.1: Site electrical and natural gas demand with and without CHP ................ 45 Figure 7.2: Average SMP by category............................................................ 47 Figure 7.3: Average revenue received by wind generators in 2020 ......................... 49 Figure 7.4: Component energy cost comparison with and without CHP.................... 52 Figure 7.5: Savings and percentage savings with and without CHP ......................... 53 Figure 7.6: Sensitivity analysis Portfolio 5 Low Fuel .......................................... 55 Figure 7.7: Savings vs. Natural Gas Prices ...................................................... 56 Figure 7.8: Correlation of savings with wind penetration .................................... 57 Figure 7.9: All-island 2009 fuel mix ............................................................. 58 vi
  7. 7. 1. Introduction Utilising the exhaust heat from a power plant affords substantial carbon savings compared to generating that heat separately. The penetration of CHP in Ireland has been far below the technical potential for a variety of reasons discussed in section 2. There is uncertainty as to what effect wind may have on the viability of CHP. The two components which affect CHP are the price of electricity and the price of natural gas. This report looks at how each may be affected by high levels of wind. Section 2 discusses the current status of CHP in Ireland. Section 3 describes the important aspects of the Single Electricity Market. Section 4 looks at how wind may affect the electricity sector using data from the Commission for Energy Regulation‘s (CER) ―Impacts of High Levels of Wind Penetration on the Single Electricity Market in 2020‖. Section 5 looks at the most salient aspects of the natural gas market in 2020. Section 6 examines any possible effects high wind levels may have on natural gas supply in Ireland. Finally, section 7 brings together insights from previous chapters and with a financial model of a typical CHP plant explores what impact varying levels of wind may have on CHP. 1
  8. 8. 2. CHP Background 2.1. CHP Targets A range of reports on Combined Heat and Power (CHP) in Ireland have been published by the Sustainable Energy Authority of Ireland (SEAI) [1-11]. A 2001 report outlined the government‘s targets for CHP under the National Climate Change Strategy [1]. This target was for a tripling of the then installed 122MWe to 377MWe installed by 2010, which would abate 0.25MtCO2. This target was to exclude the large 310MWe plant proposed for Aughinish Alumina, which was included in the Business as Usual (BAU) scenario. At the end of 2008, 282MWe of CHP was considered to be operational [11]. However this includes the 160MWe addition to Aughinish Alumina [10] which means that the adjusted installed figure is approximately 122MWe; almost the same figure as when the National Climate Change Strategy target was set. This is shown in Figure 2.1 [10], however the estimate of installed capacity was lowered slightly in SEAI‘s 2010 report [11]. Figure 2.1: CHP capacity 1991-2008 including and excluding Aughinish Alumina [10] 2
  9. 9. In 2007, the government‘s Energy White Paper [12] set targets for CHP deployment by 2010 and 2020. The 2010 target was set at a total installed CHP capacity of 400MWe, with the provisional 2020 target described as ―at least‖ 800MWe pending further analysis. Figure 2.2 [10] shows these targets and SEAI‘s future deployment scenarios. Figure 2.2: CHP deployment scenarios and government 2010 and 2020 targets [10] From Figure 2.2 it can be seen that the 2010 target will likely be missed, and SEAI does not envisage the provisional 2020 target being met. The CHP installed capacity in Ireland is predominantly made up of industrial units. The total number of installed CHP units was 167 in 2008, and industrial CHPs number 31 [10]. While industrial CHPs only made up 19% of the number of units, they comprised 80% of the capacity. This is shown in Figure 2.3 [10]. CHP Installed Capacity by Sector (2008) Other Services 2% 18% Industry 80% Figure 2.3: CHP installed capacity by sector (2008) [10] 3
  10. 10. It is clear that the majority of future potential capacity growth lies in the industrial sector due to the much higher average capacity per unit compared to the services and other sectors [3, 10]. This report will deal principally with industrial CHPs. 2.2. Spark Spread The spark spread is the difference between what an industrial facility pays for a unit of electricity and what it pays for a unit natural gas. Equation 1 shows the equation for the spark spread. Equation 1: Spark Spread SEAI prefer to use the term ‗spark gap‘. Equation 2 shows the equation for spark gap. Equation 2: Spark Gap Both the spark gap and spark spread measure the same values. Figure 2.4 shows the spark gap for industrial CHPs from 2004 to 2008. Since CHPs can last up to 25 years, the spark gap volatility shown in Figure 2.4 is an issue which affects the investment decision. 4
  11. 11. Figure 2.4: Industrial sector spark spread 2004 to 2008 [10] It can be seen that the spark gap has narrowed significantly in recent years. This has the effect of extending the payback period. This effect is shown in Figure 2.5. Spark Gap = 3 Spark Gap = 4 Spark Gap = 5 Figure 2.5: Effect of spark gap on payback period of a 1MWe industrial CHP [10] 5
  12. 12. Figure 2.5 shows a spark gap of 3, 4 and 5. Taking an electricity price of €0.10/kWh, the difference between a spark gap of 3 and 4 is sufficient to reduce the payback period from over 5 years to under 4 years. 2.3. CHP Barriers There are a range of barriers to wider industrial CHP deployment. These are explained in detail in [10]. The barriers that most pertain to industrial CHPs are outlined below. 2.3.1. Suitability of Thermal Loads CHP economics typically dictate that a unit should be operational for at least 4,000 hours per year and the CHP site should have balanced electrical and thermal loads [10]. While Ireland has some large heating loads, much of them are not suitable for CHP. The cement industry consumes 19% of industrial energy but there is a mismatch between the thermal demand of the site and the heat output from the CHP [10]. CHP is also not suitable for the cement industry because of the grade of heat required to fire the kilns (1000°C). The mining industry consumes most of its energy as electricity, and thus has a low heat to power ratio, which means that the potential for CHP deployment is limited. 2.3.2. Grid Connection Another barrier to CHP is regulatory. If a CHP intends to export to the grid, they must secure a grid connection. The criteria for obtaining a grid connection were amended by the Commission for Energy Regulation (CER) in July 2009 [13]. The Group Processing Approach (GPA) of the CER is the process by which renewable generators receive offers for connection to either the transmission or distribution system. The current iteration is Gate 3, and applications are processed in batches based on time of application. This can lead to significant delays for generators who have just joined the queue. However based on the discretion of the CER, applications can be expedited if they meet the public interest criteria. The public interest criteria can be summarised as [13]:  Diversity of Fuel Mix  Predictability and power system support  Environmental benefits  Experimental/Research The CER‘s amendment allows for the bypass of the GPA if the CHP is less than 5MWe and is found not to interact electrically with other applications in Gate 3. Alternatively if the CHP is greater than 5MWe then it may only be expedited if it can demonstrate to the CER that it meets the public interest criteria. In addition if the CHP is seeking a 6
  13. 13. connection of less than 500kWe then if it meets the public interest criteria it can bypass the GPA [10]. This is summarised in Figure 2.6 [13]. Figure 2.6: Treatment of CHPs when applying for grid connection [13] 2.3.3. PSO levy Another potential barrier to CHP adoption is the Public Service Obligation (PSO) levy. The PSO levy is used to recompense ESB Public Electricity Supply (PES) for its obligation to purchase peat generated electricity. It is also used to guarantee minimum electricity to certain renewable generators, principally wind generation. The PSO is calculated based on a site‘s Maximum Import Capacity (MIC). While a CHP might have a reduced electricity import requirement the majority of the time, it will typically require a full import connection for the period when the CHP unit is not available. Industrial sites with CHPs under current rules thus have to pay the full PSO, irrespective of how often they import electricity. 2.3.4. Use of System Charges Transmission Use of System (TUoS) charges and Distribution Use of System (DUoS) charges are levied on generators and consumers that are connected to the transmission and distribution systems respectively [10]. For a CHP which does not export, the charges are based on the MIC and the charges remain the same [10]. 7
  14. 14. 2.3.5. Industrial Energy Reduction Programmes Large energy users may prioritise relatively capital light measures such as efficiency improvements over capital intensive measures such as CHP [10]. However this can be thought of as a positive barrier, because Energy Service Companies (ESCos) can combine an energy efficiency programme with a reduced size CHP. 2.3.6. Corporate Investment Criteria SEAI found that while the typical payback for a CHP would be 5 years, some corporate investors will only consider capital projects with payback periods in the order of three to four years [10]. SEAI‘s survey found that the investment was still not seen as economically viable when assessed against corporate investment criteria, even when the investment was financed by ESCos. 2.3.7. Management & Operation CHP is typically not a core activity within industry and therefore CHPs require external expertise to run the plant. However ESCos can address this lack of expertise. These barriers are summarised in Table 1. Area Barrier Status Economic Economic Viability Remaining Barrier Corporate Investment Criteria New Barrier Grant Assistance Partial/New Barrier Fuel Prices Spark Gap Partial Barrier Availability of Heat Loads Cement Manufacture Remaining Barrier Mining Remaining Barrier Dairies Partial Barrier Electricity Market Grid Connection Partial Barrier Public service Obligation Levy Partial/Removed Barrier Use of System Charge Partial Barrier Support Prices for Sale of Partial Barrier Electricity Competition with Energy Industrial Energy Reduction New Barrier Saving Programmes Programmes Other Management & Operation New Barrier Interaction with Authorities Partial Barrier Table 1: Barriers to industrial CHPs [10] 8
  15. 15. 3. Single Electricity Market The Single Electricity Market (SEM) is a mandatory pool where the vast majority of electricity is bought and sold at a single price. All units with a maximum export capacity over 10MW (the de-minimis threshold) are obliged to sell their electricity to the pool. Those units under 10MW can decide whether to sell to the SEM or not [14]. Generating units under this threshold – principally CHPs – can arrange a private contract with an electricity supplier which will treat the electricity exported as a ‗negative demand‘. This ‗negative demand‘ will reduce the volume of electricity the supplier must purchase from the SEM to meet its customers‘ demand. For this to be attractive to the electricity supplier, it would be priced at a discount to the SEM price. The SMP has two components: the shadow price and uplift. The shadow price is determined by the most expensive generating unit during a given half hour. However the shadow price does not account for the generators‘ start-up and no-load costs. The SMP is increased, by way of an uplift, to reflect these costs. While there are a number of ways the uplift could be applied, after consultation, the regulators decided on an approach which minimises distortion of the SMP [15]. The breakdown of the SMP from [15] is shown in Figure 3.1. Figure 3.1: Breakdown of SMP between uplift and revenue [15] In the example shown in [15], the time-weighted average SMP was €62.30/MWh of which the uplift accounted for €8.21/MWh or 13%. 9
  16. 16. 3.1. Capacity Payments Generators also receive capacity payments which are designed to recover the capital costs of power plants. Capacity payments are allocated across the year to incentivise the provision of generation to meet a certain security standard (8 hours Loss of Load Probability (LOLP) per year). The regulator determines the capacity payment pot each year based on projected demand. As demand is higher in winter, the capacity payments are higher than in summer. Figure 3.2 shows an example breakdown of a hypothetical €100m capacity payment pot [16]. Figure 3.2: Decision paper example of capacity payment breakdown [16] The capacity payment pot is determined by the product of the annual projected peak demand and the annual cost of a peaking plant. Equation 3: Capacity payment pot calculation The cost of peaking plant is determined based on a forecast of the cost of a Best New Entrant (BNE). In 2009, it was determined that for 2010 the cheapest peaking plant would be a distillate fuelled plant in Northern Ireland [17]. This type of plant was determined to have a levelised cost per kW of €85.58. The projected revenues from ancillary services were €4.84/kW. Subtracting the latter from the former resulted in a BNE cost per kW of €80.74 for 2010. 10
  17. 17. Based on a model of projected demand and based on modelling to ensure 8 hours LOLP, a projected capacity requirement of 6,826MW was assumed. This resulted in a capacity payment pot of €551m. The capacity payment pot has decreased from 2009 due to both a reduction in the BNE price (9% higher in 2008, when the pot was determined) and reduced projected demand (8% higher in 2008) [18]. For 2011, it has been determined that a capacity requirement of 6,922MW is required and that the BNE price is €78.73/kW [19]. Figure 3.3: Annual capacity payment pot 2007-2011 [17, 19] 3.2. System Marginal Price The System Marginal Price (SMP) is set for every half hour of the day by the variable costs of the most expensive unit which is generating during that time plus an uplift. The variable costs of a unit are dominated by the cost of fuel, and so the SMP has a strong correlation with fuel prices. Electricity generating units are turned on until demand is met starting with the cheapest units and extending to the more expensive units. This is called the merit order. The merit order for the start of 2008 is shown in Figure 3.4. Renewable generation (principally wind) has almost no variable costs and so the wind is used when it is blowing. Coal is next cheapest, followed by peat and then baseload gas, mid-merit gas and peaking plant. 11
  18. 18. 250 200 150 C ost (€ /MWh) 100 peak 50 mid-merit gas baseload gas peat coal renewables 0 0 1000 2000 3000 4000 5000 6000 7000 8000 C a pa city (MW) Figure 3.4: Merit order for the start of 2008 [20] The SMP varies across the day based on the units required to meet the load. The SMP data for February 2008 to May 2010 are shown in Figure 3.5. Data was obtained from the Single Electricity Market Operator (SEMO) website [21]. The periods above €200/MWh are relatively few, and they typically represent times when Ireland has purchased electricity at short notice from Britain across the interconnector. Figure 3.6 zooms in on the area where the majority of data points lie. The five day moving average is shown as the continuous white line. It can be seen that there is a lot of volatility in the average SMP both monthly and yearly. This can be explained to a large extent by natural gas price movements and changes in demand. 12
  19. 19. Figure 3.5: Half hourly SMP data February 2008 to May 2010 in €/MWh Figure 3.6: Average SMP February 2008 to May 2010 13
  20. 20. 4. Effect of Wind on Electricity Market Wind tends to reduce the SMP by shifting the supply curve to the right. This is called the merit order effect (MOE) and is illustrated in Figure 4.1. Wind has very low variable costs, and so is always in merit when it blows. The demand curve for electricity is relatively inflexible, so the reduction in cost doesn‘t tend to increase consumption. Figure 4.1 shows the MOE effect to be greatest at peak electrical demand because it displaces expensive peaking plant. The effect is lower during the day, and lowest at night because wind may only be displacing the relatively inexpensive coal and nuclear. Figure 4.1: Merit order effect at different times of the day [22] Various studies have attempted to quantify the MOE of wind on electricity prices [23- 27]. The studies use different assumptions and look at different countries with different market structures, so the results may not be comparable. The qualitative results may be more meaningful. In [25] it was found that the MOE in 2006 for Germany was 16% lower when the carbon price was increased from €0 to €40 per tonne CO2. This is explained by the fact that in Germany at times of low demand coal sets the SMP, while at high demand gas sets the SMP. A carbon price increases the cost of coal more than the cost of gas generation. This decreases the shoulder between the lower cost coal and higher cost gas. When 14
  21. 21. wind generation causes coal to set the SMP, where otherwise it would be gas, this then decreases the MOE. This is illustrated in Figure 4.2. Figure 4.2: Effect of a carbon price on the differential between coal and gas Looking at Figure 4.2, it can be observed that if the price of coal increases but the gas price does not, the shoulder will decrease and vice versa. Delarue et al. [28] showed that for the Belgian system the MOE was relatively insensitive to wind forecast error. Munksgaard [29] concluded that taking account of the MOE, Danish consumer prices were €0.05—0.06/kWh more expensive than they would be without wind. This was attributed to the feed-in tariff structure paid to wind developers. 4.1. CER Market Impact Study The CER conducted a study which calculated the SMP in 2020 for various penetrations of wind [30]. The study varied the natural gas and carbon price and computed the SMPs in 2020. The five generation portfolios studied were similar to those used in the All Island Grid Study (AIGS) [31-36], with a few small changes. These changes were the inclusion of 190MW of CHP, the addition of the Huntstown Power station, a higher maximum load (10,407MW in the central scenario vs 9600MW in the AIGS), some different assumptions about which plants were retired by 2020, and slightly different plant characteristics. 15
  22. 22. Apart from these, the substantive changes to the 2005 Irish portfolio were the same as assumed in the AIGS Workstream 2B [34]. These additions are shown in Figure 4.3 for the five respective portfolios. Figure 4.3: Additions to 2005 generation portfolio in AIGS by 2020 [34] Portfolio 1 is useful as a benchmark, since the proportion of wind is similar to that presently on the Irish system (1.4GW) [37]. Portfolio 2 is similar to Portfolio 1 but has an extra 2GW of wind. Portfolios 3 and 4 are characterised by a high reliance on OCGTs and coal, respectively. Portfolio 5 is similar to Portfolio 2 but has 2GW extra wind. Portfolio 5‘s installed capacity of 6GW of wind in 2020 may now overshoot Ireland‘s 40% renewable electricity target, with Eirgrid‘s latest Generation Adequacy Report (GAR) projecting that 40% renewables may translate to 4.6GW wind [38]. The principal difference between Portfolios 1, 2 and 5 is the level of wind penetration and for the purposes of this report this shall be the main consideration. Figure 4.4 shows the all-island peaks extrapolated out beyond 2016 from the 2010 GAR. It is clear that the AIGS [34] and the CER study [30] have projected 2020 demand far ahead of the likely level. Electricity demand may rise at a faster pace after 2020 if there is a concerted transfer of transport and thermal loads to electricity (e.g. electric vehicles and electric heating). A scaled down Portfolio 5 offers perhaps the best post- recession view of the generation mix in 2020. The exact numerical results are not as 16
  23. 23. important as the relative values of the portfolios, and for this the AIGS and CER‘s study are very useful. Figure 4.4: All-island peak demand extrapolated out to 2030 from 2010 GAR The CER study computed the average SMP across the year using a market simulation software model called PLEXOS. This model has been successfully calibrated for the current SEM and is accurate. The CER took an annual wind profile and dispatched the generation units assuming perfect foresight. In reality the wind forecast will be revised, and the decision to dispatch units will be based on probabilistic methods. This is an important caveat when interpreting the results. In 2020, if, instead of committing units based on a day ahead schedule at present, a rolling unit commitment schedule is implemented, the costs are projected to be similar [39]. Based on the AIGS portfolios, the cost increase comparing perfect wind forecast and that accounting for wind‘s uncertain output has been estimated as 1.2% [39]. The results presented here should thus be broadly comparable to what can be expected under real circumstances. To illustrate, Figure 4.5 shows the relative accuracy of wind forecast and wind output for the first 8 months of 2010 by 15 minute interval [40]. 17
  24. 24. Figure 4.5: Actual wind generation vs. forecast Figure 4.6 presents the data in Figure 4.5 in a different manner. The error forecast duration curve represents the percentage of time the (absolute) error was below a certain threshold. For January to August 2010, 80% of the time the error has been within 100MW and 50% of the time the error has been within 50MW. A small percentage of the time the forecast is off by a large margin. 18
  25. 25. Figure 4.6: Wind Forecast Error Duration Curve The CER used three different price scenarios and computed the SMP for each. The study was performed in July 2008 when gas prices were very high and since then the price has reduced considerably. The low price scenario is closest to the current price. Figure 4.7 shows the results of the study, and also shows the 2010 price. Figure 4.7: Correlation of natural gas price with SMP (assumes €30/tCO2) 19
  26. 26. Figure 4.7 shows a high degree of correlation between the natural gas price and the SMP for the respective portfolios. The lowest SMP can be seen in the high coal Portfolio 4. The next cheapest is the 6GW wind Portfolio 5. Third cheapest is Portfolio 2, which adds 4GW wind and extra CCGTs. Next is Portfolio 1, which represents business as usual with 2GW of wind. The most expensive is Portfolio 3, which relies heavily on inefficient OCGTs to balance 4GW of wind, so this is not surprising. The CER‘s result that Portfolio 5 and Portfolio 2 result in lower average SMPs than Portfolio 1 is evidence that increased wind lowers the SMP. The higher the wind penetration the lower the SMP. The effect is greater at higher natural gas prices as the divergence of the lines in Figure 4.7 shows. The three scenarios also varied the carbon price from €15/tCO2 in the low fuel scenario to €30/tCO2 in the central scenario and €45/tCO2 in the high fuel scenario. In the low fuel scenario this resulted in the carbon price making up half the price of coal generation. As a result, coal was utilised much less in the low fuel than in the central and high scenarios when the carbon price was a proportionately lower share of coal‘s fuel costs. The cost of carbon was only approximately 10% of the cost of natural gas in the low fuel scenario. Figure 4.7 shows results for a peak load of 10.4GW; however the CER also performed a low electrical demand growth sensitivity of 2.7% against 3.5% in the other scenarios. This resulted in a peak demand of 9.4GW. Each portfolio in the low growth sensitivity contained 1GW of generation (10% of peak) over and above what was required by system security standards. The impact of this on the SMP is shown in Figure 4.8. 20
  27. 27. Figure 4.8: Average SMP in Central Scenario vs. Low Growth Scenario Figure 4.8 shows for each portfolio the average SMP reduces for a given portfolio if there is lower demand. The most significant reduction is in Portfolio 1 where the €20/MWh reduction occurs because expensive peaking generation is used less because of the high generation margin (installed plant capacity above the annual peak). Portfolios 2 and 5 show lower reductions due to the higher wind capacity. A large reduction occurs in Portfolio 3 due to the reliance on OCGTs, and the high coal Portfolio 4 shows a small reduction. Figure 4.8 shows that a high generation margin leads to a lower average SMP. Currently, and at least until 2016, Eirgrid projects a rising surplus capacity on the system [38]. This is shown in Figure 4.9. 21
  28. 28. Figure 4.9: Surplus capacity 2010-2016 [38] A surplus capacity can be expected to dampen average SMPs due to the reduced use of peaking plant. The large generation margin is markedly different from pre-recession GARs where deficits were forecasted as recently as the 2008-2014 GAR [41]. Eirgrid‘s median scenario is based on an average GDP growth of 5% between 2011 and 2016, while the low scenario is based on 4.2% GDP growth. These scenarios respectively envisage electricity demand growth of 2.2% and 1.9%. To the extent these figures are optimistic the surplus capacity could be larger. Taking the low demand scenario, a surplus capacity in 2016 of approximately 1550MW would exist. The low demand scenario envisages the all island peak as 6,939MW in 2016. This equates to a substantial 22% surplus margin, which based on Figure 4.8 will act to keep the SMP lower than if a normal margin existed. Figure 4.10 uses the same data as Figure 4.7 to generate spark spreads. The simple spark spread is the difference between the time weighted average SMP and the natural gas price. The real spark spread is the difference between the metered electricity price (€/MWh) less the natural gas price. In each case the spark spreads of Portfolios 2 and 5 are below the low wind Portfolio 1. The smallest difference between P2 & P5 and P1 is €7/MWh for the low fuel price scenario, with the largest difference being €25/MWh for the high fuel price scenario. 22
  29. 29. Figure 4.10: Simple spark spread for CER scenarios An instructive way to view the price data is through a price duration curve. A price duration curve shows the proportion of the year that the SMP is below a given threshold. The highest level of variation is in the top 10% of prices which typically represent imports and peaking generation. For clarity, Figure 4.11 and Figure 4.12 respectively show the price duration curve for the lowest 90% and highest 10% separately. Portfolios 1, 2 and 5 are considered from here on, as Portfolios 3&4 are highly unlikely future generation mixes. The low fuel (LF) and low demand (LD) scenarios were selected for comparison. The actual SMPs for 2008 and 2009 are shown for contrast. 23
  30. 30. Figure 4.11: Lowest 90% Price Duration Curve for selected 2020 portfolios Figure 4.12: Top 10% Price Duration Curve for selected 2020 portfolios 24
  31. 31. Another useful way to view the price data is by time of day. Figure 4.13 shows the three selected low fuel price scenarios and Figure 4.14 shows the three low demand scenarios. For CHP units which export or import electricity at night, the effect of increased wind on the system will be negligible. However during the day, wind will adversely affect CHP units by lowering the SMP. For example Figure 4.15 shows the difference in SMP between Portfolio 1 and Portfolio 5 for the low fuel scenario. The average difference between 9am and 10pm is €24/MWh. As a percentage of Portfolio 1 daytime prices, this is a substantial 21% reduction. For the low demand scenario, the corresponding reduction is €19/MWh and 14%. Thus for a system with a higher generation margin the merit order effect is lower. Figure 4.13: Annual average SMP by half hour of day for Low Fuel Scenario 25
  32. 32. Figure 4.14: Annual average prices by half hour for Low Demand Scenario Figure 4.15: Difference in SMP by hour of day (P1-P5 Low Fuel Scenario) 26
  33. 33. 4.2. CER Wind Data The wind data used in the CER study was based on wind capacity factors assigned to different regions of Ireland by Eirgrid. This data for Portfolio 5 is shown in Figure 4.16. Figure 4.16: Half hourly wind generation data for Portfolio 5 As Figure 4.16 shows, there is substantial variation in the 48 hour wind data. However it can be seen that the moving average never reaches zero and only once touches 5,000MW while 6,000MW is the installed capacity. All the portfolios use the same wind data, and so the output follows the same pattern regardless of installed capacity. Figure 4.17 shows the wind output for the five scenarios. 27
  34. 34. Figure 4.17: Wind generation in CER study From Figure 4.17, it can be seen that the average wind output for January was 3,000MW for P5, or a capacity factor of 50%. This contrasts with the average August output of 1,000MW, or a capacity factor of 17%. The annual capacity factor for the wind data used was 32%. 4.3. Wind Variability Wind is variable over hourly, daily, monthly and yearly timescales. Figure 4.16 and Figure 4.17 show the variation in the CER study wind output data on a half hourly, daily and monthly timescales. The CER used a specific pattern of wind data for their study, it is worth comparing this to historical patterns. Wind data going back 10 years is held by the Electricity Research Centre. Figure 4.18 shows the monthly variation in wind data for the last ten years. The monthly average capacity factor and max and min for each month are shown. The capacity factor for the CER‘s study is also overlaid. The largest historical variation in the wind data is for the month of February, where over the last ten years the capacity factor has ranged from 30% to 57.5%. The variation is lowest in July, ranging from 15% to 23%. The CER study‘s wind data tracks the average reasonably closely. 28
  35. 35. Figure 4.18: 10 year monthly wind output variation [42] 29
  36. 36. 5. Natural Gas The cost of natural gas is a key determinant of CHP economics. Shale gas is the most dynamic element of the natural gas market and is the most uncertain element of future natural gas supply. The current shale gas situation is described, followed by a brief discussion of the outlook for European natural gas. 5.1. Shale Gas The large scale commercialisation of shale gas is a recent phenomenon, although the first gas well in America was actually from a shale formation in Fredonia, New York in 1821 [43]. The principal reasons behind this expansion are [43]:  Advances in horizontal drilling  Advances in hydraulic fracturing  The rapid increase in natural gas prices as a result of significant supply and demand pressures Figure 5.1 shows the expansion of shale gas from 2000 to 2009 [44]. US Shale Gas Production 2000-2009 3 12% 2.5 Marcellus Haynesville Woodford 10% Trillion Cubic Feet per Year As % of US 2009 Demand 2 Fayetteville Barnett Antrim 8% 1.5 6% 1 4% 0.5 2% 0 0% 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Figure 5.1: Shale gas production 2000-2009 [44] There are a range of different shale ‗plays‟ in the US. The most important of these is the Barnett shale, which accounts for over half of the current production. Future increases will depend on the other plays, with the most promising being the Marcellus and Haynesville shales. 30
  37. 37. In June 2010, MIT released an interim report entitled The Future of Natural Gas [45]. This was a multi-disciplinary study of the future supply and demand of natural gas, with a focus on shale gas. The report includes sensitivities to account for the uncertain nature of natural gas resources. Figure 5.2 show their projections of shale gas out to 2030. Figure 5.2: Potential future shale gas production [45] Figure 5.2 is in bcf/day (billion cubic feet). Converting that to tcf/yr, MIT predict shale production rising from 2.8tcf in 2009 to 9.8tcf in 2020 and 10.6tcf in 2030. The key reason why the potential production rate reaches a steady state is because of high decline rates. Production from shale gas wells drop off precipitously within a couple of years. Figure 5.3 shows the drop off in production from certain plays and additionally the breakeven gas price for the different categories of US gas [45]. From Figure 5.3, it can be seen that the production from wells in the Haynesville drop off approximately 75% within a year while Marcellus and Barnett wells drop off by approximately 70%. Shale gas has thus been described as a „treadmill‟ where production rates are limited by the number of rigs drilling for gas. Although there is uncertainty as to the cost of shale gas, Figure 5.3 shows the MIT study‘s cost curves for shale and the other types of US natural gas. 31
  38. 38. Figure 5.3: Shale production rate and breakeven US production prices [45] The MIT study assumes there is 500tcf of shale gas available below $8/MMBtu (a MMBtu is equivalent to 10 therms or 0.96mcf). Five hundred tcf is enough for 25 years of US gas supply. Years of supply is a flawed metric, however, since based on Figure 5.2, the maximum sustainable rate of production would be in the region of 10tcf. So US shale could potentially supply approximately 40% of US 2009 demand for 50 years at under $8/MMBtu. However others dispute the assumed shale cost curve shown in Figure 5.3 [46-48]. The disagreement centres on whether companies are including the ‗full cycle‘ costs. In March John Dizard wrote in the Financial Times [46]: “Industry advocates forward consultants‟ studies talking of “learning curves” and $5 gas as far as the eye can see. Not many neutrals. So I worked people in the energy service industry, and gas producers to try and refute Ben Dell‟s numbers. I couldn‟t. My industry sources‟ numbers all converged close to $8 per mcf. They do not believe the producers are covering their all-in costs.” The costs quoted by the shale companies are thought to exclude land costs, seismic surveys and operating costs [46]. They also rely on optimistic production forecasts, with residual production lasting up to 40 years. Since shale is a recent phenomenon, these forecasts cannot be proved one way or the other. 32
  39. 39. Figure 5.4 shows the inflation adjusted average US natural gas price from 1982 to 2009 [49]. Annual Average US Wellhead Natural Gas Prices (US$2009/MMBtu) 9 9 8 8 7 7 6 6 5 5 4 4 3 3 2 2 1 1 0 0 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 Figure 5.4: Annual average US wellhead natural gas prices [49] Natural gas prices have average $4.50/MMBtu for the first half of 2010 [49]. If the true cost of shale gas was closer to $8/MMBtu than $4/MMBtu, then shale producers would be losing money. However there is an explanation as to why producers would continue to drill even if their true costs are above current market prices. In [50], Mark Rowland, Chief Financial Officer (CFO) of a major shale gas company, Chesapeake, states: ―I believe at least half and probably 2/3 or 3/4 of our gas drilling is what I would call involuntary. It's being incentivized by something other than the gas price.” Two reasons why companies would continue to drill are:  They have hedged at a higher natural gas price, and so are not exposed to the current uneconomic price  They acquired expensive leases prior to 2009 which will expire unless they drill Much of the current drilling may thus be ‗involuntary‘. Shale gas is being overproduced due to factors other than the gas price. As hedges and leases expire, drilling could be expected to decline. With the high decline rates from shale gas wells, the price of natural gas should converge to a price that incentivises enough shale gas production to meet demand. Whether that price is the $4/MMBtu that the MIT study suggests, or closer to $8/MMBtu, remains to be seen. 33
  40. 40. Shale gas exploration in Europe is still in the preliminary stages. Figure 5.5 shows the sites where oil and gas companies are currently exploring. Exxon Mobil has found the results of drilling in Hungary disappointing and has withdrawn from that area [51]. European geology is more complex and fragmented than America‘s which may make developing shale more challenging. Potential additional barriers to shale include Europe‘s higher population density, stricter environmental regulation and limited numbers of drilling rigs [51]. The fracturing of shale requires significant quantities of water. Figure 5.6 shows a typical shale gas drilling site. The footprint of a shale gas operation will be more significant if shale resources lie under a built up area. The pools of water must be remediated as they contain chemicals. The US Energy Information Administration (EIA) made a forecast for future shale gas production in March 2010 [44]. Figure 5.7 shows this projection in the context of global natural gas production. As can be seen, in 2009 shale gas only produced 1.7% of global natural gas production. The EIA shows global production of shale reaching approximately 10 tcf (0.3 tcm) by 2030, or 6.4% of the projected global total. This 10 tcf is close to the 10.6 tcf the MIT study [45] considered to be the maximum sustainable output of US shale gas. While shale gas production outside of the US is likely to prove more challenging, if European (or Indian/Chinese) shale proves feasible, then this total may rise. If the rest of the world was to match the US shale output from the MIT study, then the total would rise to 21.2tcf or 13.5% of the projected total. If EU production of shale developed quickly it could have a significant dampening effect on EU natural gas prices. 34
  41. 41. Figure 5.5: Shale gas exploration sites in Europe [51] Figure 5.6: Shale gas land footprint [52] 35
  42. 42. Figure 5.7: Shale gas in context of global natural gas production [44] 5.2. European Natural Gas Prospects A projection of EU gas production and imports out to 2030 from the IEA‘s World Energy Outlook 2009 Reference Scenario is shown in Figure 5.8 [53]. The IEA project European gas production to continue declining while from 2015 demand is expected to pick up. The result is that by 2020 EU imports increase from 312bcm in 2007 to 425bcm in 2020 and 428bcm in 2030. The IEA makes a conservative estimate of 15bcm of EU unconventional production (coal bed methane and shale gas) by 2030. This is the largest uncertainty of future EU production. UK gas supply is projected to decline from 73bcm in 2008 to 44bcm in 2015 and under 20bcm by 2030. Figure 5.8: EU supply and demand projection to 2030 36
  43. 43. No one can predict the future gas price, however the IEA take a nuanced approach and their forecast is useful for comparison. Figure 5.9 shows the historical gas price and the IEA‘s projection for European gas prices to 2020. The IEA still think, on balance, that EU prices will retain a link to oil. The rise shown is linked to the IEA‘s projected increase of oil prices to $100/bbl in 2008 prices by 2020. Also shown are the CER‘s assumed gas prices in 2020. It can be seen that the central and high fuel prices are well above what is currently expected. This is because the CER study was carried out during the run up of oil and gas prices in 2008. Figure 5.9: Historical and projected natural gas prices 37
  44. 44. 6. Effect of Wind on Natural Gas Wind has the potential to affect the natural gas grid, which, as a gas consumer, could impact CHP. Looking at Figure 4.16 it is clear that the output of wind can vary from high output to low output. Flexible power plants, principally natural gas fired, must counteract these fluctuations. The changing electrical output places a demand on the natural gas grid. The question then arises as to whether the natural gas grid can supply sufficient quantities of natural gas to these power plants. A large part (65% in the 2009 gas year) of Ireland‘s natural gas demand comes from power generation. Figure 6.1 shows this and also shows projections out to 2019 estimated in the CER‘s Joint Capacity Statement [54]. Very modest growth is expected principally due to the weakened economy, energy efficiency, and increasing renewable penetration. Figure 6.1: Annual Irish gas demand projections by sector Few studies have been undertaken on the effect of wind on natural gas. Of those, the approach generally taken is to model a Combined Electricity and Gas Network (CEGN) [55, 56]. A CEGN takes account of the physical constraints on both the electricity and natural gas networks. Qadran et al. [56] describe a CEGN which seeks to minimise total system costs over a time horizon of two days. Figure 6.2 shows the cost function which they seek to minimise. 38
  45. 45. Figure 6.2: Objective function for a CEGN [56] The technical limitations of both networks are constraints on the least cost optimisation model. The authors selected a high wind Great British system which approximated the Gone Green scenario laid out by National Grid as a likely outcome by 2020 if EU targets are to be met [57]. Qadran et al. assume 25GW of wind installed in Great Britain by 2020, although the Gone Green scenario lists 29.4GW. Based on January 2005 weather, the wind output in January 2020 in Britain was determined. The fluctuations are balanced principally by CCGTs ramping up and down. A two day period of low wind output and a two day period of high wind output were selected to test the effects on the gas grid. Figure 6.3 shows the CCGT output over the assumed days. The CCGT power output is shown for the low wind case, the high wind case, and for 2009 as a reference case. At hours 42-44 in the low wind case, peak electrical gas demand coincides with non-electrical gas peak demand. The pressure in the gas pipeline was found to be insufficient to supply all the CCGTs, and for those few hours the interconnector with France was utilised. 39
  46. 46. Figure 6.3: CCGT power output for high wind, low wind and base case scenarios [56] Figure 6.4: Linepack for low wind, high wind and base case scenarios [56] The fundamental difference between electricity and gas networks is that while electrical energy in a wire travels at close the speed of light, gas in a pipeline flows orders of magnitude slower at approximately 10m/s or 36km/hr. Hence gas injected at Moffat, Shannon, or Kinsale will take several hours to reach Dublin. Deliverability from the gas grid relies on maintaining high pressure in the gas pipeline. Gas withdrawals are 40
  47. 47. typically balanced with gas injections over a 24 hour period. If too much gas is withdrawn during the day, and high-deliverability storage located close to the load centre can‘t inject fast enough, then the pressure in the pipeline will drop. The pressure drop is proportional to the drop in the amount of gas or ‗linepack‘ in the gas pipeline. Figure 6.4 shows the drop that occurred between hours 36 and 44. An intra-day study of the variation of linepack with wind output has not been performed for Ireland. However the CER‘s Joint Gas Capacity Statement 2010 stress tests the gas grid for a 1 in 50 severe winter [58]. Additionally the high demand of this past winter was a real world test. A record gas demand was set on 7th January 2010. The wind generation on this day was very low as is shown in Figure 6.5. Wind output averaged just 10% of rated capacity and output was only 5% during the peak demand period of 5.00-6.00pm when electrical demand peaked at an all-time record 4,950MW. Figure 6.5: Wind Generation on 7th January 2010 [40] The electrical peak coincided with a 20.7 Degree Day (DD) at Dublin Airport, which was just short of the 21DD for a 1 in 50 winter. The industrial/commercial demand was not at its previous record due to the recession. The record combined demand of Ireland, Northern Ireland and the Isle of Man was 30.2 mscm/day. The gas grid successfully met this without the need to resort to curtailment. If the deliverability was not there, then the power sector may have had its gas supply curtailed [58]. 41
  48. 48. The Gas Capacity Statement models the future 1 in 50 day requirements of the grid for each year until 2019. Figure 6.6 shows the components of demand and the ability to meet the demand based on whether Ireland has no storage (Moffat only), whether there is storage at Inch (Moffat + Inch), the addition of Corrib (Moffat + Inch + Corrib), and the addition of storage at Larne (Moffat + Inch + Corrib + Larne). As is evidenced, with the addition of Corrib in 2013, the maximum gas supply is comfortably above a 1 in 50 winter peak day requirement. The situation is further improved if the Larne storage facility comes online in 2016. The addition of Corrib is highly likely; however the commercial decision for Larne has yet to be taken. The Shannon LNG terminal would further improve the maximum supply capacity. Shannon LNG has planning permission however a commercial decision has yet to be taken. Figure 6.6: 1 in 50 winter peak day demand and max supply capacity [58] A recent Poyry report investigated the issue of gas intermittency in Great Britain and Ireland in 2030, assuming high levels of wind [59]. Eight GW of wind were assumed to be installed by 2030, which is substantially above the 4.6GW which will represent the 40% renewable target in 2020. Figure 6.7 shows the results of Poyry‘s modelling. Substantially higher variation is visible compared to the present situation. 42
  49. 49. Figure 6.7: Daily total Irish gas demand in 2010 and 2030 [59] The study also modelled how the flows from Corrib, Inch, Shannon, Larne and Moffat would look in 2020. Figure 6.8: Daily gas flows in the Irish market in 2020 from various sources [59] The study suggests that the provision of all these sources could result in an oversupply of flexibility to the market. This would be favourable from a security of supply point of view but the returns to the operators of these facilities may not be sufficient. Poyry neglected to study the intra-day effects in their study as Qadran et al did. They approximated this, however, by measuring the flow-flex. Flow-flex is defined as the ratio of the average gas flow rate over the first 16 hours of the day to the average across the full day. A high flow flex means more erratic gas demand. From Figure 6.9, 43
  50. 50. the green 2019 line shows approximately 140 days of higher flow-flex than 2009. Poyry did not perform a hydraulic study of the Irish gas grid which would be required to determine whether there would be periods of low pressure. However Poyry stress that changes are likely to be gradual and that the System Operator will have time to adapt, for instance by moving to shorter balancing periods. Figure 6.9: Flow flex duration curve for Ireland (mscm) [59] An intra-day study with a two hour time step such as in Qadran et al would clear up any uncertainty, however based on Figure 6.6, the gas grid appears robust. Any disturbances would only be for a very small number of hours and could be addressed by operator vigilance. All power plants are required to hold reserve fuel oil which could be employed during periods of high gas grid stress. 44
  51. 51. 7. Financial Analysis A financial model of a representative CHP plant, compiled by Dalkia, was used in Excel to determine the effect of different levels of wind on the overall savings a CHP accrues. The input data was the SMP for the selected scenarios from the CER study. The modelled CHP plant was sufficient to meet the baseload thermal demand of a typical pharmaceutical plant. The plant has a usual day time (8am-11pm) demand of 1.9MWe which falls off at night to 1.3MWe. The thermal demand on the site ranges from 1.5MWth to 1.9MWth. The CHP has been sized to provide a constant thermal baseload of 1.2MWth and 1MWe. The availability of the CHP is 92%. All electricity is imported and backup boilers employed for the 8% CHP downtime. The site has a heat load factor of 99%, meaning 1% of the heat energy produced by the CHP is not put to a useful purpose. The result of the CHP is to increase the site‘s natural gas requirements by 67% but to reduce the electrical requirements by 45%. Figure 7.1 shows the monthly volumes of electricity and natural gas required with and without CHP. Figure 7.1: Site electrical and natural gas demand with and without CHP 45
  52. 52. The savings made by the reduced electricity imports must pay for the increased gas use and capital costs. As Figure 4.13-14 show, wind will principally affect the daytime electricity price by reducing the SMP. The site‘s electrical demand was broken up into five categories:  Winter Peak (Jan, Feb, Nov, Dec 5-7pm)  Winter Day (Jan, Feb, Nov, Dec, 8am-11pm excl. 5-7pm)  Summer Day (Mar-Nov 8am-11pm)  Weekend day (all weekends 8am-11pm)  Night (all nights 11pm-8am) The half hourly SMP data was arranged into these five bins for each of the categories and the average price in each category was determined for the scenarios from the CER study. The results are shown in Figure 7.2. The same trend observed in Figure 4.13 is evident. The largest difference between 2GW, 4GW and 6GW of wind is seen on the winter peak with a reduction of approximately €20/MWh per additional GW for the low fuel scenario. The next largest change is in the summer day (which included the 5-7pm peak) where in the low fuel scenario the average SMP drops from €110/MWh in Portfolio 1 to €94 in Portfolio 2 and €84/MWh in the high wind Portfolio 5. Figure 7.2 replicates what Figure 4.15 showed - that there is no difference in the night time price regardless of wind penetration. The same categorisation is shown for the low demand, 2008 and 2009 data for comparison. 46
  53. 53. Figure 7.2: Average SMP by category The Excel model allowed for an input of SMP prices and natural gas prices to determine the total energy bill with and without CHP. It includes the various components of the final price including:  DUoS  TUoS  Electricity Tax  PSO levy  Gas commodity cost  Gas transmission charges  Gas distribution charges The most direct impact wind could have on these charges is via the Renewable Energy Feed-in Tariff (REFiT). 7.1. Effect of REFiT REFiT was introduced in 2006 to facilitate the expansion of renewable energy to meet the 2010 renewable targets. It currently provides support for 1,075 MW of wind 47
  54. 54. generation [60], or 74% of the 1,459MW installed as of July 19th 2010 [61]. REFiT guarantees an SEM payment of €57/MWh indexed to any Consumer Price Index (CPI) increases since 2005. The CPI for 2005 was 2.5%, hence the REFiT for 2006 was €58.43. The CPI for 2006 was 4% which resulted in a 2007 REFiT of €60.67 (58.43 * 1.04). CPI for 2007 was 4.9% which put the 2008 REFiT at €63.74 [62]. The 2008 CPI was 4.1% which increased REFiT to €66.35. The 2009 CPI was negative so REFiT did not increase. Inflation forecasts vary but 1-2% is the general expectation for the coming years. At 2% out to 2020, the REFiT rate would jump to €79/MWh (66.35*1.01^9). At 1%, it would be €72.50/MWh. While originally REFiT was restricted to 1,450MW of installed wind and projects operational by 2010 [63], the government has made it clear that it will consider projects which are not yet operational [62]. It is also seeking EU approval for an expansion of the 1,450MW cap on installed wind [63]. It is unclear to what extent REFiT support will be extended or how much of the prospective 4.6GW of wind by 2020 will be covered by REFiT. The average SMP for 2008 was €80/MWh, while the average for 2009 was €43/MWh. Although REFiT facilitates other renewables as well as wind, wind makes up by far the biggest segment. Wind power generation in 2008 was approximately 2,410 GWh and 2,955 GWh in 2009 [64]. In 2008, if wind received the average SMP, no REFiT charge would occur. In 2009 if wind received the average SMP, a subsidy of €23/MWh would be required. This would result in a charge of €68m (2955*1000*23). In 2020, the 40% renewable target would result in approximately 12,000 GWh of wind if Eirgrid‘s projection of 4.6GW is correct and a 30% capacity factor is assumed (4.6*0.3*24*365). If the current quota of wind with REFiT is assumed then 1,450MW, generating 3,800 GWh (1.45*0.3*24*365), would be eligible for guaranteed generation payment in 2020. The average revenue received per MWh by wind was computed from the CER data. Figure 7.3 shows the results. The high wind Portfolio 5 under the low natural gas price scenario receives an average €62/MWh. The medium wind Portfolio 2 receives €68/MWh and Portfolio 1 wind receives €78/MWh. The increasing wind tends to cannibalise its own revenue. The lower SMP offsets, to an extent, any increase wind may impose on system costs. The low demand, central scenario, and high fuel scenario would not require REFiT as their gas price assumption of €47/MWh (low demand and central) and €70/MWh (high) generate high enough electricity prices to clear the REFiT hurdle. 48
  55. 55. Assuming REFiT stays capped at 1,450MW, and an average inflation of 2%, wind in Portfolio 5 would receive €17/MWh from REFiT (79-62). With 3,800 GWh generated per year that would amount to compensation of €65m/year (3800*1000*17). If REFiT was expanded to cover all wind generators on the system in 2020 under the same terms as current wind generators, payments would increase to €204m (12000*1000*17). Based on a PSO levy of €157m this year, the charge for industrial customers came to €14/kVA (although domestic customers may cover industrial customers‘ share this year) [60]. A €204m charge for wind may thus result in an €18/kVA charge in 2020 (14*(204/157)). This site has a 3,200kVA MIC, so this would result in an annual charge of €57,600 to subsidise wind. This will not directly affect CHP economics, as the MIC is the same with or without a CHP. Figure 7.3: Average revenue received by wind generators in 2020 The decision to index REFiT to CPI is questionable. Currently, the capital cost of Irish onshore wind is estimated at €1.05m/MW by Poyry [65]. In 2007, Eirgrid estimated the levelised cost of wind electricity (LCOE) with €1m/MW capital costs and a 34.5% capacity factor at €42.1/MWh [66]. The CER assumed capital costs for wind of €183,000 per MW per year with O&M of €61,000. This resulted in additional Portfolio 5 new wind generation (approximately 5,000MW) losing €239m under the low fuel price scenario. This is similar to the €204m calculated above; implying that the CER assumed the LCOE for wind was near €79/MWh. 49
  56. 56. The Eirgrid estimate with €1m/MW resulted in capital costs of €112,000 per year and O&M of €17,000 was assumed. The cost of capital was slightly lower in the Eirgrid study (7.32% vs. the CER‘s 8%) however the 15 year assumed lifetime was the same. Poyry‘s assumptions of €1.05m/MW installed with 10% capital costs and a 20 year lifetime led to an LCOE of approximately €72/MWh [66]. The LCOE is very sensitive to interest rates, which explains part of the difference between Eirgrid‘s and Poyry‘s numbers. Poyry also assumed substantially higher O&M than Eirgrid, at approximately €20/MWh. The actual cost range may thus lie between €42 and €72/MWh depending on capacity factor, cost of capital and maintenance. The capital cost is subject to factors such as commodity costs (e.g. steel) and international wind turbine demand. Reduced costs may provide scope for lowering the REFiT price which would reduce payouts should REFiT be extended. If REFiT is expanded its level should be based on actual costs, with the link to CPI rescinded. 7.2. Effect of Wind on Startup Costs High levels of wind will likely lead to increased cycling costs. The magnitude of these cycling costs is unclear. The following quote illustrates the lack of knowledge [67]: ―It is estimated that cycling costs can range from $300 to $500,000 per single on-off cycle, depending on the type of unit.‖ The CER included a sensitivity allowing for increased cycling costs. They approximated this by increasing the variable startup costs by 50% for all units. This manifests itself as additional uplift, a part of the SMP. Their sensitivity showed a relatively small effect – with the average SMP increasing by €1.50 for the high wind Portfolio 5 in the central scenario. 7.3. Effect of Wind on Capacity Payments The CER study used the same capacity payment methodology as currently. As Figure 4.4 showed, the CER‘s projection of required capacity of 11,400MW in 2020 is far above what is now likely. Using a cost of capital for a peaking plant of €81/kW – similar to today‘s figure – a capacity payment pot of €918m, €919m and €923m was generated for Portfolio 1, Portfolio 2 and Portfolio 5 respectively. Despite having a substantially higher installed capacity in Portfolio 2 and 5 (due to wind), the capacity requirement did not reduce. Hence wind generation is not a major factor in deciding the capacity payment pot. 50
  57. 57. If the capacity requirement was scaled back to a more likely 8,000MW in 2020 (Figure 4.4), the pot would be reduced to €650m. This is fairly close to 2010‘s €550m (Figure 3.3). 7.4. Financial Savings The model was set up with all the various tariffs which were relevant as of 2009. These included the large energy user (LEU) rebate. The LEU offers a reduction of €15/MWh per unit of electricity consumed by large energy users (the modelled plant here qualifies). The LEU penalises CHP because, by importing less electricity, a given site gets a lower rebate. The data from Figure 7.2 was inputted to the model along with the associated gas prices and the results computed. This scenario, with 2009 tariffs remaining in place, is shown in Figure 7.4. While there are many components to the annual energy costs, the majority of the costs are made up of the SMP, supplier capacity charges and gas commodity costs. The most significant costs are shown in the legend. For the low fuel Portfolio 1 in 2020, the savings with CHP amount to €425,000 or an 18.6% reduction on the no CHP case. The savings would be €509,000 but for the LEU (21% reduction). 51
  58. 58. Figure 7.4: Component energy cost comparison with and without CHP The costs with and without CHP were computed for all the scenarios in the CER‘s study. The savings and the percentage savings were recorded. It was decided to exclude the LEU from the analysis as it is unclear whether this subsidy to large energy users will still apply in 2020. The PSO rebate was additionally excluded. Figure 7.5 shows the results. Some clear trends are visible. In all cases, increasing wind leads to decreased savings for CHP. For all scenarios, there is clear differentiation between Portfolios 1, 2 and 5. The output of wind as a share of total all-island generation was computed to be 10% for P1, 20% for P2 and 30% for P5. For the low fuel scenario, the difference between 10% wind penetration and 20% wind penetration resulted in a drop in savings of €55,000/year. The increment from 20% wind to 30% wind meant further reduced savings of €42,000/year. For the other three scenarios, the same trend is observed, with the drop between 10% and 20% wind being almost equal to the further drop between 20% and 30%. Additionally, the effect of surplus generation capacity is seen in the large drop in savings, particularly between P1 central fuel and P1 low demand. The drop is 52
  59. 59. €156,000/year. The difference between P2 central fuel and P2 low demand is €116,000 while for respective P5 data points the difference is €82,000. The effect of wind is lowest when there is surplus generation capacity. The difference between 10% and 30% wind was €80,000 for the low demand scenario. For the high fuel scenario, the savings are reduced by €100,000/year for each 10% increment in wind. The percentage savings are lower due to the larger numbers involved. Figure 7.5: Savings and percentage savings with and without CHP A tornado plot was compiled to examine the sensitivity of the results to changes in each cost element. Portfolio 5 in the low fuel scenario was selected because this is the most likely scenario, although all the portfolios show similar sensitivities. Each element was independently increased and decreased by 50% and the reduction or increase in savings recorded. The origin is placed at the original savings point of €411,725/year. This is shown in Figure 7.6. The results are very sensitive to the cost of gas and daytime electricity. This is to be expected as they are the largest cost elements. If the price of gas remained the same 53
  60. 60. but the cost of daytime electricity was reduced by 50% (because of a large generation margin, for example), savings would decrease by 34% to €272,000 per year. If the price of gas increased by 50%, and the price of electricity remained the same, savings would be reduced by 38% to €263,000 per year. This is unlikely, however, because the increase in gas prices would result in a concomitant increase in electricity prices. The results are also sensitive to night time and weekend electricity prices. However these prices would not be as depressed by a large generation margin as they are principally determined by baseload generation costs. The SEM supplier capacity charge is derived from the capacity payment pot. If there is overcapacity on the system, the capacity payment pot should reduce, resulting in lower charges. Based on the forecast demand in 2020, the capacity payment pot, and supplier capacity charges, should remain close to what they are today. However if charges are increased by 50%, savings would be increased by €67,000 per year. The gas transmission capacity charge is a relatively small element. The carbon tax is based on a €15/tCO2 base level, and the range shown is €7.50- €22.50/tCO2. The carbon tax is determined by the Irish government and is outside the EU-ETS, which is also currently at €15/tCO2 [68]. The low fuel scenario also includes a level of €15/tCO2. The EU-ETS carbon price in 2020 was expected to double, however barring EU intervention, there may now be an excess of permits due to the recession. In August 2010 the first 2020 carbon futures were traded at €22.72/tCO2 [69] which illustrates current sentiment. To the extent that the Irish government increases the carbon tax above ETS levels, this will disadvantage CHP, although the sensitivity is small. The results are not sensitive to the other elements of the electricity and natural gas price. There is no change for the other elements of the electricity price based on the site‘s MIC. This is because the MIC remains the same as it is required for the small percentage of the year when the CHP is down for maintenance. A fairer way of levying the capacity charge for CHP sites would be based on the volume of electricity they import. If this was done for this site, based on the reduction of 45% of electricity imports, the TUoS and DUoS capacity charges would be reduced to €50,000 from €90,000 for the no CHP case. This would result in 10% greater savings for the scenario shown in Figure 7.6. 54
  61. 61. Substantial investment is required to upgrade transmission lines, partly to accommodate wind [70]. If this led to a doubling of the TUoS capacity charge, savings would remain the same, however the percentage savings would reduce slightly. If based on electricity volume, however, a doubling of TUoS would result in savings of €16,000. The same applies to the PSO levy. If a €204m levy was applied in 2020, this may result in a charge of €57,600. If based on electricity volume, though, it would be advantageous for CHP and would lead to augmented savings of €26,000. Figure 7.6: Sensitivity analysis Portfolio 5 Low Fuel The savings show a high correlation with the price of natural gas. Figure 7.7 plots the natural gas price against the savings for the low, central and high fuel scenarios. For each portfolio the savings increase with increasing gas price. The slope of Portfolio 1 is smaller than Portfolio 2 or 5, and the savings difference gets larger the higher the gas price. Figure 7.7 shows €/MWh on the left axis and £pence/therm on the right axis. There are 34.12 therms in a MWh, and the August 2010 conversion factor of €1=£0.82 was used to calibrate the right axis. The equations relating gas price to savings are shown and, by inputting a gas price in €/MWh, the savings can be found. Figure 7.7 shows that the effect of wind on the economics of CHP depends on the natural gas price. At the low fuel price level, increasing wind penetration from 10% to 55
  62. 62. 30% reduces savings by 19%. For the central fuel price level, the corresponding reduction is 25% and at the high fuel price the reduction is similarly 25%. Using the equations in Figure 7.7, if the fuel prices were to stay at the current level (€16/MWh) then the move from 10% wind to 20% wind would reduce savings by €51,000/year or an 11% reduction. Savings would be curtailed a further €37,000/year moving from 20% to 30% wind. Although the three scenarios had different carbon price assumptions, the high correlation of savings with natural gas price indicates that carbon prices are a secondary consideration to the natural gas price. The equations in Figure 7.7 assume the same generation margin at each natural gas price. They do not apply to the low demand scenario. Figure 7.7: Savings vs. Natural Gas Prices Figure 7.8 shows the savings in terms of the percentage of wind penetration. Some caveats are required when interpreting this, however. As shown in Figure 4.17, the wind penetration in Portfolios 2 and 5 was essentially a scalar multiple of the wind in Portfolio 1. Figure 4.18 showed that there can be substantial variation from year to year in wind output, and this will affect the results shown. From the CER data, the wind output in each half hour was added up for each portfolio. The wind output was:  Portfolio 1: 5,587GWh  Portfolio 2: 11,174GWh 56
  63. 63.  Portfolio 5: 16,759GWh Total generation for the year was approximately 57,000 GWh. Transmission line losses of 9.3% reduced the final electricity consumption to 54,000TWh. Portfolio 1 represented approximately 10% wind (5,587/57,000), with Portfolio 2 at 20% and Portfolio 5 at 30%. With 6GW installed in Portfolio 5, the capacity factor for this year was 32%. A year with a lower capacity factor may have a lower impact on the SMP, with increased savings for CHP. The most interesting aspect of Figure 7.8 is the low demand line. Despite a natural gas price that is double the low fuel price scenario, the savings are lower. A high generation margin can be expected to shift the savings line to the left (reduce savings) and to increase its slope (reduce the impact of wind on savings). This describes the movement of the red central fuel line. Figure 7.8: Correlation of savings with wind penetration 7.5. Payback Period The capital cost of CHP is variable; however SEAI report costs of €1m/MWe for large units [10]. The modelled site used a 1MWe gas turbine. The IEA reports O&M costs of on average €7/MWhe for CHP [71]. Based on annual production of 2,606MWhe from the site‘s CHP, this equates to an annual O&M estimate of €18,250. A 10% discount rate was 57
  64. 64. used to represent the opportunity cost of investment and a 15 year lifetime for the CHP was assumed. The payback period was computed for the low fuel scenario. Under these assumptions, Portfolio 1 had a discounted payback period of 5.0 years, Portfolio 2 took 5.6 years and Portfolio 5 had a payback period of 6.2 years. The deterioration in payback period is a cause for concern, given that the recent SEAI report stated the typical payback period as 5 years and that some corporations would only consider investing in projects with a 3-4 year payback [10]. 7.6. Carbon Emissions With and Without CHP The carbon emissions of the site with and without the CHP plant were computed from the Excel model. An emissions intensity of 0.55tCO2/MWh was used for grid electricity. The emission intensity for 2009 all-island electricity was 0.504tCO2/MWh [72]. Figure 7.9 shows the 2009 fuel mix. Wind accounted for 11%, which suggests the figure would be approximately 10% higher if not for wind, or about 0.55tCO2/MWh. The savings from 1MWe of CHP with a heat to power ratio of 1.2 and a 92% capacity factor were 2,136tCO2/year. Wind at a 30% capacity factor will generate 2,606MWh/year. Based on 0.55tCO2/MWh, wind saves 1,433tCO2/MW. Hence the CHP for this site saves the equivalent of 1.5MW of wind (2136/1433). Figure 7.9: All-island 2009 fuel mix 58
  65. 65. 8. Conclusion The main effect high levels of wind will have on CHP is to decrease the savings compared to a no CHP scenario. This will occur principally due to the merit order effect reducing daytime electricity prices. The merit order effect of wind is strongly dependent on the generation margin and on the natural gas price. If the generation margin increases, wind will have less effect on CHP. As the natural gas price increases, so does the impact of wind on CHP‘s savings. Wind is unlikely to significantly affect the Irish natural gas market, although if any problems arise, they can be addressed through operator vigilance. If the costs of running the gas grid increase, the sensitivity of CHP‘s savings to such an increase is low. Given the current natural gas price outlook, the low fuel scenario will offer the best guidance for the likely effect of wind in 2020. However as a high generation margin is forecast for the coming years by Eirgrid, the impact of wind on CHP may be reduced. 59
  66. 66. References In most cases the references are a direct link to the relevant pdf file. All links were tested and working as of 28th August 2010. Shortened hyperlinks were used for brevity. [1] Sustainable Energy Authority of Ireland. (2001). An Examination of the Future Potential of CHP in Ireland. Available: [2] Sustainable Energy Authority of Ireland. (2004). Combined Heat and Power in Ireland Trends and Issues 1991-2002. Available: [3] Sustainable Energy Authority of Ireland. (2006). CHP in Ireland Options for a National Policy to 2010. Available: [4] Sustainable Energy Authority of Ireland. (2006). Evaluation of Legislation and Regulation Affecting New CHP Installations in Ireland. Available: [5] Sustainable Energy Authority of Ireland. (2006). Benchmarking Report: Status of CHP in EU Member States. Available: [6] Sustainable Energy Authority of Ireland. (2006). Irish Supply Chain Capability for CHP Applications. Available: [7] Sustainable Energy Authority of Ireland. (2006). New Technologies for CHP Applications. Available: [8] Sustainable Energy Authority of Ireland. (2006). A Guide to CHP in Ireland. Available: [9] Sustainable Energy Authority of Ireland. (2006). Micro CHP Field Trial - Consultation. Available: [10] Sustainable Energy Authority of Ireland. (2009). Combined Heat and Power (CHP) Potential in Ireland. Available: [11] Sustainable Energy Authority of Ireland. (2010). Combined Heat and Power in Ireland 2010 Update. Available: [12] Department of Communications Marine and Natural Resources. (2007). Delivering a Sustainable Energy Future for Ireland. Available: [13] Commission For Energy Regulation. (2009). Treatment of Small Renewable and Low Carbon Generators. Available: [14] Poyry. (2007). Trading and Settlement Code - Helicopter Guide. Available: [15] All Island Project. (2007). SMP Uplift Parameters Decision Paper. Available: [16] All Island Project. (2006). Single Electricity Market Capacity Payment Factors Decision Paper. Available: [17] Commission For Energy Regulation. (2009). Fixed Cost of Best New Entrant 2010 Decision Paper. Available: [18] All Island Project. (2008). Annual Capacity Payment Sum, Best New Entrant Price, and Capacity Requirement 2009. Available: [19] Commission For Energy Regulation. (2010). Decision Paper on Best New Entrant Peaker for 2011. Available: [20] Economic Social Research Institute. (2009). Should Coal Replace Coal? Available: [21] Single Electricity Market Operator. (2010). Available: [22] European Wind Energy Association. (2009). The Economics of Wind. Available: [23] T. Jónsson, et al., "On the market impact of wind energy forecasts," Energy Economics, vol. 32, pp. 313-320, 2010. 60
  67. 67. [24] Poyry. (2010). Wind Energy and Electricity Prices - Exploring the merit order effect. Available: [25] F. Sensfuß, et al., "The merit-order effect: A detailed analysis of the price effect of renewable electricity generation on spot market prices in Germany," Energy Policy, vol. 36, pp. 3086-3094, 2008. [26] H. Weigt, "Germany's wind energy: The potential for fossil capacity replacement and cost saving," Applied Energy, vol. 86, pp. 1857-1863, 2009. [27] J. A. a. H. Weigt, "Electricity Markets Working Paper WP-EM-38 Combininwwg Energy Markets," 2010. [28] E. D. Delarue, et al., "The actual effect of wind power on overall electricity generation costs and CO2 emissions," Energy Conversion and Management, vol. 50, pp. 1450-1456, 2009. [29] J. Munksgaard and P. E. Morthorst, "Wind power in the Danish liberalised power market-- Policy measures, price impact and investor incentives," Energy Policy, vol. 36, pp. 3940- 3947, 2008. [30] Commission For Energy Regulation. (2009). Impact of High Levels of Wind Penetration in 2020 on the Single Electricity Market (SEM). Available: [31] Department of Communications Marine and Natural Resources. (2008). All Island Grid Study Overview. Available: [32] Department of Communications Marine and Natural Resources. (2008). All Island Grid Study Workstream 1 Renewable Energy Resource Assessment. Available: [33] Department of Communications Marine and Natural Resources. (2008). All Island Gridy Study Workstream 2A High Level Assessment of Suitable Generation Portfolios for the All- Island System in 2020. Available: [34] Risoe National Laboratory. (2007). Final Report for All Island Grid Study Work-stream 2(b): Wind Variability Management Studies. Available: [35] TNEI. (2007). Workstream 3 All-Island Grid Study Report. Available: [36] Department of Communications Marine and Natural Resources. (2008). Available: [37] Eirgrid. (2010). Available: [38] Eirgrid. (2010). Generation Adequacy Report 2010-2016. Available: [39] P. Meibom, Barth, R., Hasche, B., Brand, H., Weber, C. and O´Malley, M.J., "Stochastic optimization model to study the operational impacts of high wind penetrations in Ireland," IEEE Transactions on Power Systems, In Press, 2010. [40] Eirgrid. (2010). Wind Generation. Available: [41] Eirgrid. (2007). Generation Adequacy Report 2008-2014. Available: [42] E. Lannoye, "Electricity Research Centre, Personal Communication," ed, 2009. [43] U.S. Department of Energy. (2009). Modern Shale Gas Development in the United States: A Primer. Available: [44] U.S. Energy Information Administration. (2010). Shale Gas - A Game Changer for U.S. and Global Gas Markets? Available: [45] Massachusetts Institute of Technology. (2010). The Future of Natural Gas. Available: [46] J. Dizard, "Sleight of Hand Over Shale Gas Costs," in Financial Times, March 21 ed, 2010. [47] D. Parkinson, "A contrarian makes another call - this time natural gas," in The Globe and Mail, April 18 ed, 2010. 61