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Market design for a large share of wind power
ARTICLE IN PRESS Energy Policy 38 (2010) 3131–3134
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Editorial
Market design for a large share of wind power
The EU has agreed the Renewables Directive that mandates that 20% of EU energy (not just electricity) be generated from renewables (not just zero-carbon) sources by 2020. As it is challenging to deliver renewable energy in transport fuels, and may be difficult to increase the share of heat sufficiently, the main burden will be placed on the electricity supply industry. For example, although the UK has only a 15% target for renewable energy, this might require 40% of electricity generated to come from renewable sources. The renewable electricity technology with both adequate potential and the lowest cost is on-shore wind (energy from waste is often cheaper but in limited supply, while hydro resources have limited remaining potential in most European countries). Wind power is intermittent and has a modest load factor, and so the share of additional renewable generation capacity will need to be considerably larger than its share of generation. Thus the SKM report commissioned by the UK Government anticipates that to deliver 150 TWh from renewable electricity supply (RES), 38 GW of wind capacity plus 16 GW other RES and 56 GW non-renewable will be required to meet a peak load of 63 GW. The requirements for back-up power imply that fossil generation will have a load factor of only 31%, while the RES, much of which has a zero variable cost, will be more than sufficient to meet demand in many hours per year, causing the price in those hours to fall to zero. Faced with more volatile spot prices and low load factors for back-up generation, there are concerns that without substantial reforms, security of supply will be compromised. 1. The challenge for market design Liberalised wholesale electricity markets in many European countries are not well-suited to meet the challenge of a large share of wind power, while providing security of supply in the short and long term (see for example, Hiroux and Saguan, this issue). The papers in this special issue address the various challenges posed by the substantial increase in wind power contemplated, as well as providing examples in various countries of the approaches adopted and experience to date (Rivier, this issue; MacGill, this issue; Sioshansi and Hurlbut, this issue). Ideally the wholesale markets should foster competition and entry (to deliver efficiency and support innovation), allow RD&D to be supported while not compromising international competitiveness, and provide incentives for timely, efficient and adequate investment in generation and transmission, while delivering electricity to consumers at an acceptable price. Even within a single country these requirements are challenging, but at the EU level they are considerably more so. The ideal would be the European electricity market operates as an integrated whole with efficient dispatch, and 0301-4215/$ - see front matter & 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2009.07.037
that cost-effective interconnections are built as required to ensure that intermittency can be shared and overall costs reduced, and that the RES is built where it has a comparative advantage and traded to those countries to allow them to meet their renewable targets (Roques et al., this issue; Boccard, this issue). None of these requirements can currently be guaranteed. One way to examine the efficiency of a market design is to ask whether it suffers from avoidable market failures, that is, flaws that could be corrected or offset at lower cost than the harm they do. The market will be working well once it delivers the best feasible outcome for the system. Market failures can be identified by testing to see whether the market gives the right incentives to ensure that wind farms are built in the right place at the right time, operated to deliver least system cost, and only built and run when economically justified. In an efficiently designed liberalised market, the prices that wind farm investors face should signal these requirements, and if the prices are wrong, then the outcomes are likely to be inefficient and more costly (Hiroux and Saguan, this issue; Vandezande et al., this issue; Weigt et al., this issue). To assess whether the price signals are working, we can work backwards from the final situation in which the wind farm is up and running and producing income. The prices and income should determine its value to the electricity system and more widely to mitigating climate change. Given forecasts of prices, income and costs at various locations and dates, developers can decide whether to enter the market, and if so where and when to locate. Our task is to see how well each decision aligns private and social costs and benefits. On-shore wind farms typically have high availability, but their output depends on the local wind speed, which varies over quite short time scales. Weber (this issue) shows that the day-ahead prediction error in Germany is more than 20% of average wind production, but can be reduced by considering shorter time scales. Demand also suffers from prediction errors, initially larger than the error in wind’s contribution at modest wind deployment, but eventually swamped by that error. The System Operator (SO) needs reserves to balance supply and demand, and part of this cost can be attributed to the contribution each wind farm makes to the total. Costs can be reduced with better forecasting, and better procurement and management of reserves. A well-designed system would incentivise the SO to procure and control the plants that have the lowest cost of supplying balancing services, some of which may be outside the SO’s control area. Cross-border co-operation can thus lower costs, as can better utilisation of interconnectors, and adequate interconnection capacity. The first set of questions about market design would be whether the SO is incentivised to minimise the costs of procuring
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Editorial / Energy Policy 38 (2010) 3131–3134
and managing reserves over varying time scales (from the very short run up to contracts that might run for several years in order to encourage peaking plant to be built and offered). As the SO function is a natural monopoly, it is subject to regulation, and the question then resolves into whether the national regulation is well-designed, and whether efficient cross-border transactions are encouraged by the various regulators involved. Better forecasting can be encouraged by rewarding wind farms for more accurate forecasts (effectively by charging them for the higher cost of shorter term deviations from announced output). Given such incentives one would expect the market to offer this forecasting service by collecting weather forecasts and aggregating all wind farm data, as in Portugal or Spain (see for example, Rivier, this issue). Because wind power is intermittent, it is rarely efficient to build enough transmission capacity to handle maximum wind output (and indeed, the system maximum demand is unlikely to change much as the capacity of wind power increases) (Weigt et al., this issue). Wind can displace conventional generation, but the cost of doing so will vary by location. Consequently, the value of electricity at each node will typically differ, and can be computed if bids and offers are honest under a nodal pricing system (locational marginal pricing, or LMP, as practised in PJM and discussed by Lewis, this issue and Green, 2008). Efficient nodal pricing allows storage hydro behind the same transmission constraints to balance fluctuating wind power in a decentralised manner, which other market designs and transmission access regimes can deter. Thus the British transmission access regime offers firm capacity up to declared net capacity of the generator but only when that capacity is available or has been built, in exchange for an annual charge, but effectively zero variable cost, undermining efficient scale, timing and operating decisions for wind farms. ¨ Matevosyan and Soder (2007) shows that coordinating wind and hydro power in Sweden can be valuable, but may not happen, presumably because of the cost of bilateral contracting. These obstacles can be overcome by the hydro company taking over the wind company, usually extracting all the rent. Knowing that, wind companies might avoid investing, or the hydro company may itself invest in wind, although it may not be the most competent company to do so. The same need for coordinating dispatch is also true of conventional generation, but with the proviso that the cost of rebalancing the generation mix in each transmission-constrained zone will be reduced if conventional plant is given timely forecasts of its likely demand. Again, the SO is the logical focus of delivering updates to the optimal generation schedule in the light of changing weather forecasts and plant availability. Optimists might argue that intra-day balancing markets combined with nodal pricing might be sufficient to allow selfdispatch to deliver efficient outcomes, but Weber points out that most such markets are not adequately liquid to give one much confidence in their efficiency. That is a powerful argument for central dispatch, as in PJM, and would amount to a major change in market design in many countries, certainly in Britain, which abandoned that model with the ending of the Electricity Pool in 2001. Suppose the system is reformed to deliver efficient location pricing, so that each wind farm receives the correct value of its electricity at each moment and place, then what market failures remain? The first is that wind has zero CO2 emissions, and will only be rewarded for this if fossil generation pays the correct price for CO2. The EU Emissions Trading System or ETS was created to price CO2, but its volatility and periodic crashes gives little confidence that it is correctly pricing carbon. The market failure, not specific to wind, but of greater importance to zero-carbon power than other generation, is the failure of the EU CO2
Allowance (EUA) price to properly reflect the social cost of CO2 (Grubb and Newbery, 2008). This can be addressed either by reforming the ETS, which is likely to be difficult, or by offering effectively a special carbon price for RES, either through a feed-in tariff or by a premium payment above the nodal electricity price. The choice between these is discussed further below. The second market failure specific to RES, and the reason for the EU Renewables Directive, is that RES is not valued just to the extent of carbon saved, but to the extent that each investment creates a learning spill-over that is not fully captured by the operator (or the wind turbine supplier). Neuhoff (2008) has shown how (under ideal conditions) the value of this spill-over might be computed, but in practice rather rough and ready calculations are likely. In theory one asks what the global benefit of one more MW of wind capacity installed now would be in terms of reducing the cost of building and operating all future wind turbines, discounted to the present, provided at some future date the technology will be mature and competitive against future low-carbon alternatives. That calculation is difficult for many reasons. First, the future is unknown, and specifically we do not know the social cost of present and future CO2 emissions and the nature of the future low-carbon alternatives. Second, there is an option value in finding out what these technologies can deliver (cost and performance) as they may (but not necessarily will) be needed in some future states of the world. Third, there are delicate moral arguments about valuing future global benefits that also affect the discount rate to use, as Stern (2006) discusses. Different views of these can make a very large difference to the answer (Hope and Newbery, 2008). Finally, the impact on future wind power costs may depend on the confidence which the supply chain has in future levels of demand, constraints in that supply chain and on the rate of deployment (which may be affected by local planning considerations), as Neuhoff (2008) demonstrates. Possibly because these calculations are hard, the European Commission prefers to set a target for each country as a way of avoiding the question, encouraging solidarity and ensuring fair and equitable burden sharing. Given that target, one could back out the implied benefit as the marginal extra cost of meeting these targets efficiently. The third market failure, again not specific to RES, but likely to be more acute in their case, is the policy risk of the support system and its impact on the cost of finance. This will be more severe the more payments for the spill-over benefits are delayed, the more unpredictable they are, and the more vulnerable they are to policy changes. At one extreme a legally enforceable contract guaranteeing an up-front subsidy or the tariff paid per MWh over a specified period would be the most secure and least risky, while an annually and politically determined premium would be the most risky. Feed-in tariffs as found in Germany are lowest risk, the British Renewable Obligation Certificates, whose value is determined in a market of varying size that can be influenced by political decisions, is at the higher end of risk and hence cost (Redpoint et al., 2008). Several points can be made about the premium to offer for wind power, and renewables more generally. First, it should be technology-specific, as learning rates and spill-over benefits vary by technology. On-shore wind is considerably more mature than off-shore wind, which has to operate in a far more hostile environment, even if it benefits from better wind conditions and less visual intrusion. Learning rates for solar photovoltaics appear higher than for wind—IEA (2000) suggests that PV costs might fall by 35% for each doubling of cumulative output compared to 18% for wind. The EU Renewables Directive does not require this, but several countries have recognised this by tailoring support to the technology.
ARTICLE IN PRESS Editorial / Energy Policy 38 (2010) 3131–3134
Next, this support for RD&D is support for the public good of knowledge that might help save the planet, and is not just a benefit enjoyed by electricity consumers. Logically, then, it should be funded out of general revenue (or the proceeds from auctioning EUAs, for example). The only case for burdening electricity consumers is that the current tax system under-charges them—in the UK electricity is relatively subsidised by the difference between the standard rate of VAT, usually 1712%, and the reduced energy rate of 5%. To summarise, I would argue that the premium to give over and above the nodal value of electricity is the difference between the social cost of carbon and the EUA price, per MWh, plus the learning benefit to ascribe to the particular technology. The shortfall on the carbon price could be made up by a Contract for Difference (CfD) on the carbon price that pays C-E per MWh, where C is the social cost of carbon and E is the EUA price, both embodied in the marginal unit of generation. The learning benefit will be largely created by successfully commissioning the wind turbine or park, although one would want to reward high availability and higher output per unit of installed capacity. That would suggest a payment per MW of capacity installed and a continuing payment per MWh generated. Political or regulatory uncertainty could be reduced by making the first capital grant contingent upon successful commissioning, with a contracted premium payment per MWh delivered for a specified period. The resulting revenue would thus be a combination of a subsidy against the capital cost, a predictable premium (carbon plus the learning benefit) per MWh, and the nodal price, which will be volatile, as discussed below, and for which a contract would reduce risk. Here a conventional CfD would not be suitable as that would be for firm delivery at specified hours, something wind farms cannot guarantee. Instead the contract would need to be for an annual average price per MWh at that node, about which there should not be so much uncertainty. The TSO would be the natural counter-party to the nodal price risk as it is in the best place to manage that risk and to offer a long-term contract. The fourth possible market failure is that the locational signals, even with nodal pricing, may not be correct. Transmission is lumpy, and nodal prices reflect short-run marginal costs, not the long-run marginal costs (LRMC) needed to guide decisions on where to locate (Newbery, 2005b; Brunekreeft et al., 2005). This problem can be handled by offering a long-term CfD on the nodal price (effectively a Transmission Constraint Contract, TCC, as discussed by e.g. Brunekreeft et al., 2005) combined with a lumpsum or periodic payment for connection or location network tariff, calculated as the shortfall between the long-run marginal cost of grid reinforcement costs and the revenues from the TCC (equal to expected revenue from selling at the nodal price). Even here, there are potential problems, as the LRMC may fall short of the average cost of the required investment, so that it is necessary to check that the overall investment has a social benefit greater than the social cost (Weigt et al., this issue). This problem is particularly likely where it would make sense to build a high capacity transmission link but only if sufficiently many wind farms were to be built as well, but there is uncertainty about how many will be built. Targets, such as those in the Renewables Directive, might be quite helpful in inducing the required coordination rather than leaving everything to decentralised price-guided market decisions. Longer-term regulatory commitments to fund such investments may also help. Finally, large-scale wind will have considerable impacts on wholesale prices, as there will be many hours when wind output could exceed total demand, leading to a considerable increase in volatility and greater risks of market power if the conventional generation is concentrated (Green and Vasilakos, this issue; Neuhoff and Twomey, this issue). A large part of the reform of
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electricity markets and system operation will be directed to mitigating such adverse effects. Greater volatility may also disadvantage non-renewable lowcarbon generation such as nuclear power (or clean coal with carbon capture and storage, CCS), unless they are part of the portfolio of incumbent diversified companies. Again, contracts are the logical solution (and one that also addresses concerns over market power), but the liquidity of the necessary contract and reference markets may be low. Liquidity can be enhanced by moving to a gross pool, through which all power is traded spot, and on whose prices contracts can be written to address temporal volatility. Nodal prices, however, create spatial variations that need additional hedging contracts (such as TCCs), and again these are likely to be traded on illiquid markets. The logical provider of such spatial hedges would be the System Operator (acting as a Market Operator), but again this raises concerns over governance arrangements for this natural monopoly. Even if these can be addressed, lower load factors for conventional plant and persistent higher volatility may deter new investment, as very longterm contracts are unlikely in fully unbundled and liberalised markets. Underwriting the CO2 price over a sufficiently long period, such as 15–20 years (by a floor or CfD on the carbon price) would remove much of the regulatory risk, and ought to be sufficient for nuclear power (although perhaps not for CCS, which may justify additional learning benefit support). Whether or not capacity payments are needed as well to some extent depends on whether the SO can offer longer-term contracts for availability (Newbery, 2005a), and will in any case be more important for peaking power than base-load nuclear power. To conclude, large-scale expansion of wind power poses significant challenges to any market design and regulatory approach that is not guided by the principles of aligning prices with social costs and benefits and reducing unnecessary risks—and to that extent should be welcomed as precipitating reforms that are needed in any case. The worry is that costly fixes that fail to address underlying flaws will be politically simpler than intelligent reform. This issue will have amply fulfilled its social function if by pointing out the risks and opportunities that wind power creates it encourages intelligent reform. Research Director, Electricity Policy Research Group. Research support by the ESRC to the Electricity Policy Research Group, EPRG, under the programme Towards a Sustainable Energy Economy is gratefully acknowledged.
References Boccard, N. Economic properties of wind power—an European assessment. Energy Policy, this issue, doi:10.1016/j.enpol.2009.07.033. Brunekreeft, G., Neuhoff, K., Newbery, D., 2005. Electricity transmission: an overview of the current debate. Utilities Policy 13 (2), 73–94 June. Green, R.J., 2008. Electricity wholesale markets: designs now and in a low-carbon future. The Energy Journal 2008 Special Issue: The Future of Electricity: Papers in Honor of David Newbery (pp. 95–124), 95–124. Green, R.J., Vasilakos, N. Market behaviour with large amounts of intermittent generation. Energy Policy, this issue, doi:10.1016/j.enpol.2009.07.038. Grubb, M.G., Newbery, D.M., 2008. Pricing carbon for electricity generation: national and international dimensions. In: Grubb, M., Jamasb, T., Pollitt, M. (Eds.), Delivering a Low Carbon Electricity System: Technologies, Economics and Policy. CUP, pp. 278–313 ch. 11. Hiroux, C., Saguan, M. Large-scale wind power in European electricity markets: time for revisiting support schemes and market designs. Energy Policy, this issue, doi:10.1016/j.enpol.2009.07.030. Hope, C., Newbery, D.M., 2008. Calculating the social cost of carbon. In: Grubb, M., Jamasb, T., Pollitt, M. (Eds.), Delivering a Low Carbon Electricity System: Technologies, Economics and Policy. CUP, pp. 31–63 ch. 2. International Energy Agency (IEA), 2000. Experience Curves for Technology. IEA, Paris. Lewis, G. Estimating the value of wind energy using locational marginal price. Energy Policy, this issue, doi:10.1016/j.enpol.2009.07.045.
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MacGill, I. Electricity market design for facilitating the integration of wind energy: experience and prospects with the Australian National Electricity Market. Energy Policy, this issue, doi:10.1016/j.enpol.2009.07.047. ¨ Matevosyan, J., Soder, L., 2007. Short-term hydropower planning coordinated with wind power in areas with congestion problems. Wind Energy Journal 10 (3), 195–208 (John Weiley & Sons, Ltd; May 2007). Neuhoff, K., 2008. Learning by doing with constrained growth rates: an application to energy technology policy. The Energy Journal 2008 Special Issue: The Future of Electricity: Papers in Honor of David Newbery (pp. 165–182), 165–182. Neuhoff, K., Twomey, P. Wind power and market power in competitive markets. Energy Policy, this issue, doi:10.1016/j.enpol.2009.07.031. Newbery, D.M., 2005a. Electricity liberalisation in Britain: the quest for a satisfactory wholesale market design. In: Newbery, D., (Ed.), Energy Journal, Special Issue on European Electricity Liberalisation, 43–70. Newbery, D.M., 2005b. Integrating electricity transmission systems. Utilities Policy 13 (2), 69–72 June. Redpoint, et al., 2008. Implementation of EU 2020 renewable target in the UK electricity sector: renewable support schemes—a report for the department for business, enterprise and regulatory reform, Redpoint consultants, Cambridge University, Trilemma UK, London. Rivier, J. Electricity market participation of wind farms: the success story of the Spanish pragmatism. Energy Policy, this issue, doi:10.1016/j.enpol.2009.07.032.
Roques, F., Saguan, M., Hiroux, C. Optimal wind power deployment in Europe—a portfolio approach. Energy Policy, this issue, doi:10.1016/j.enpol.2009.07.048. Sioshansi, R., Hurlbut, D. Market protocols in ERCOT and their effect on wind generation. Energy Policy, this issue, doi:10.1016/j.enpol.2009.07.046. Stern, N.H., 2006. The Economics of Climate Change. CUP, Cambridge. Vandezande, L., Meeus, L., Belmans, R., Saguan, M., Glachant, J.-M. Wellfunctioning balancing markets as a prerequisite for wind power integration. Energy Policy, this issue, doi:10.1016/j.enpol.2009.07.034. Weber, Ch. Adequate intraday market design to enable the integration of wind energy into the European power systems. Energy Policy, this issue, doi:10.1016/ j.enpol.2009.07.040. Weigt, H., Jeske, T., Leuthold, F., Hirschhausen. Take the long way down—integration of large-scale offshore wind in the North Sea through HVDC-lines. Energy Policy, this issue, doi:10.1016/j.enpol.2009.07.041.
David Newbery Faculty of Economics, University of Cambridge, Sidgwick Avenue, Cambridge CB3 9DE, England E-mail address: [email protected]
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Energy Policy journal homepage: www.elsevier.com/locate/enpol
Editorial
Market design for a large share of wind power
The EU has agreed the Renewables Directive that mandates that 20% of EU energy (not just electricity) be generated from renewables (not just zero-carbon) sources by 2020. As it is challenging to deliver renewable energy in transport fuels, and may be difficult to increase the share of heat sufficiently, the main burden will be placed on the electricity supply industry. For example, although the UK has only a 15% target for renewable energy, this might require 40% of electricity generated to come from renewable sources. The renewable electricity technology with both adequate potential and the lowest cost is on-shore wind (energy from waste is often cheaper but in limited supply, while hydro resources have limited remaining potential in most European countries). Wind power is intermittent and has a modest load factor, and so the share of additional renewable generation capacity will need to be considerably larger than its share of generation. Thus the SKM report commissioned by the UK Government anticipates that to deliver 150 TWh from renewable electricity supply (RES), 38 GW of wind capacity plus 16 GW other RES and 56 GW non-renewable will be required to meet a peak load of 63 GW. The requirements for back-up power imply that fossil generation will have a load factor of only 31%, while the RES, much of which has a zero variable cost, will be more than sufficient to meet demand in many hours per year, causing the price in those hours to fall to zero. Faced with more volatile spot prices and low load factors for back-up generation, there are concerns that without substantial reforms, security of supply will be compromised. 1. The challenge for market design Liberalised wholesale electricity markets in many European countries are not well-suited to meet the challenge of a large share of wind power, while providing security of supply in the short and long term (see for example, Hiroux and Saguan, this issue). The papers in this special issue address the various challenges posed by the substantial increase in wind power contemplated, as well as providing examples in various countries of the approaches adopted and experience to date (Rivier, this issue; MacGill, this issue; Sioshansi and Hurlbut, this issue). Ideally the wholesale markets should foster competition and entry (to deliver efficiency and support innovation), allow RD&D to be supported while not compromising international competitiveness, and provide incentives for timely, efficient and adequate investment in generation and transmission, while delivering electricity to consumers at an acceptable price. Even within a single country these requirements are challenging, but at the EU level they are considerably more so. The ideal would be the European electricity market operates as an integrated whole with efficient dispatch, and 0301-4215/$ - see front matter & 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2009.07.037
that cost-effective interconnections are built as required to ensure that intermittency can be shared and overall costs reduced, and that the RES is built where it has a comparative advantage and traded to those countries to allow them to meet their renewable targets (Roques et al., this issue; Boccard, this issue). None of these requirements can currently be guaranteed. One way to examine the efficiency of a market design is to ask whether it suffers from avoidable market failures, that is, flaws that could be corrected or offset at lower cost than the harm they do. The market will be working well once it delivers the best feasible outcome for the system. Market failures can be identified by testing to see whether the market gives the right incentives to ensure that wind farms are built in the right place at the right time, operated to deliver least system cost, and only built and run when economically justified. In an efficiently designed liberalised market, the prices that wind farm investors face should signal these requirements, and if the prices are wrong, then the outcomes are likely to be inefficient and more costly (Hiroux and Saguan, this issue; Vandezande et al., this issue; Weigt et al., this issue). To assess whether the price signals are working, we can work backwards from the final situation in which the wind farm is up and running and producing income. The prices and income should determine its value to the electricity system and more widely to mitigating climate change. Given forecasts of prices, income and costs at various locations and dates, developers can decide whether to enter the market, and if so where and when to locate. Our task is to see how well each decision aligns private and social costs and benefits. On-shore wind farms typically have high availability, but their output depends on the local wind speed, which varies over quite short time scales. Weber (this issue) shows that the day-ahead prediction error in Germany is more than 20% of average wind production, but can be reduced by considering shorter time scales. Demand also suffers from prediction errors, initially larger than the error in wind’s contribution at modest wind deployment, but eventually swamped by that error. The System Operator (SO) needs reserves to balance supply and demand, and part of this cost can be attributed to the contribution each wind farm makes to the total. Costs can be reduced with better forecasting, and better procurement and management of reserves. A well-designed system would incentivise the SO to procure and control the plants that have the lowest cost of supplying balancing services, some of which may be outside the SO’s control area. Cross-border co-operation can thus lower costs, as can better utilisation of interconnectors, and adequate interconnection capacity. The first set of questions about market design would be whether the SO is incentivised to minimise the costs of procuring
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and managing reserves over varying time scales (from the very short run up to contracts that might run for several years in order to encourage peaking plant to be built and offered). As the SO function is a natural monopoly, it is subject to regulation, and the question then resolves into whether the national regulation is well-designed, and whether efficient cross-border transactions are encouraged by the various regulators involved. Better forecasting can be encouraged by rewarding wind farms for more accurate forecasts (effectively by charging them for the higher cost of shorter term deviations from announced output). Given such incentives one would expect the market to offer this forecasting service by collecting weather forecasts and aggregating all wind farm data, as in Portugal or Spain (see for example, Rivier, this issue). Because wind power is intermittent, it is rarely efficient to build enough transmission capacity to handle maximum wind output (and indeed, the system maximum demand is unlikely to change much as the capacity of wind power increases) (Weigt et al., this issue). Wind can displace conventional generation, but the cost of doing so will vary by location. Consequently, the value of electricity at each node will typically differ, and can be computed if bids and offers are honest under a nodal pricing system (locational marginal pricing, or LMP, as practised in PJM and discussed by Lewis, this issue and Green, 2008). Efficient nodal pricing allows storage hydro behind the same transmission constraints to balance fluctuating wind power in a decentralised manner, which other market designs and transmission access regimes can deter. Thus the British transmission access regime offers firm capacity up to declared net capacity of the generator but only when that capacity is available or has been built, in exchange for an annual charge, but effectively zero variable cost, undermining efficient scale, timing and operating decisions for wind farms. ¨ Matevosyan and Soder (2007) shows that coordinating wind and hydro power in Sweden can be valuable, but may not happen, presumably because of the cost of bilateral contracting. These obstacles can be overcome by the hydro company taking over the wind company, usually extracting all the rent. Knowing that, wind companies might avoid investing, or the hydro company may itself invest in wind, although it may not be the most competent company to do so. The same need for coordinating dispatch is also true of conventional generation, but with the proviso that the cost of rebalancing the generation mix in each transmission-constrained zone will be reduced if conventional plant is given timely forecasts of its likely demand. Again, the SO is the logical focus of delivering updates to the optimal generation schedule in the light of changing weather forecasts and plant availability. Optimists might argue that intra-day balancing markets combined with nodal pricing might be sufficient to allow selfdispatch to deliver efficient outcomes, but Weber points out that most such markets are not adequately liquid to give one much confidence in their efficiency. That is a powerful argument for central dispatch, as in PJM, and would amount to a major change in market design in many countries, certainly in Britain, which abandoned that model with the ending of the Electricity Pool in 2001. Suppose the system is reformed to deliver efficient location pricing, so that each wind farm receives the correct value of its electricity at each moment and place, then what market failures remain? The first is that wind has zero CO2 emissions, and will only be rewarded for this if fossil generation pays the correct price for CO2. The EU Emissions Trading System or ETS was created to price CO2, but its volatility and periodic crashes gives little confidence that it is correctly pricing carbon. The market failure, not specific to wind, but of greater importance to zero-carbon power than other generation, is the failure of the EU CO2
Allowance (EUA) price to properly reflect the social cost of CO2 (Grubb and Newbery, 2008). This can be addressed either by reforming the ETS, which is likely to be difficult, or by offering effectively a special carbon price for RES, either through a feed-in tariff or by a premium payment above the nodal electricity price. The choice between these is discussed further below. The second market failure specific to RES, and the reason for the EU Renewables Directive, is that RES is not valued just to the extent of carbon saved, but to the extent that each investment creates a learning spill-over that is not fully captured by the operator (or the wind turbine supplier). Neuhoff (2008) has shown how (under ideal conditions) the value of this spill-over might be computed, but in practice rather rough and ready calculations are likely. In theory one asks what the global benefit of one more MW of wind capacity installed now would be in terms of reducing the cost of building and operating all future wind turbines, discounted to the present, provided at some future date the technology will be mature and competitive against future low-carbon alternatives. That calculation is difficult for many reasons. First, the future is unknown, and specifically we do not know the social cost of present and future CO2 emissions and the nature of the future low-carbon alternatives. Second, there is an option value in finding out what these technologies can deliver (cost and performance) as they may (but not necessarily will) be needed in some future states of the world. Third, there are delicate moral arguments about valuing future global benefits that also affect the discount rate to use, as Stern (2006) discusses. Different views of these can make a very large difference to the answer (Hope and Newbery, 2008). Finally, the impact on future wind power costs may depend on the confidence which the supply chain has in future levels of demand, constraints in that supply chain and on the rate of deployment (which may be affected by local planning considerations), as Neuhoff (2008) demonstrates. Possibly because these calculations are hard, the European Commission prefers to set a target for each country as a way of avoiding the question, encouraging solidarity and ensuring fair and equitable burden sharing. Given that target, one could back out the implied benefit as the marginal extra cost of meeting these targets efficiently. The third market failure, again not specific to RES, but likely to be more acute in their case, is the policy risk of the support system and its impact on the cost of finance. This will be more severe the more payments for the spill-over benefits are delayed, the more unpredictable they are, and the more vulnerable they are to policy changes. At one extreme a legally enforceable contract guaranteeing an up-front subsidy or the tariff paid per MWh over a specified period would be the most secure and least risky, while an annually and politically determined premium would be the most risky. Feed-in tariffs as found in Germany are lowest risk, the British Renewable Obligation Certificates, whose value is determined in a market of varying size that can be influenced by political decisions, is at the higher end of risk and hence cost (Redpoint et al., 2008). Several points can be made about the premium to offer for wind power, and renewables more generally. First, it should be technology-specific, as learning rates and spill-over benefits vary by technology. On-shore wind is considerably more mature than off-shore wind, which has to operate in a far more hostile environment, even if it benefits from better wind conditions and less visual intrusion. Learning rates for solar photovoltaics appear higher than for wind—IEA (2000) suggests that PV costs might fall by 35% for each doubling of cumulative output compared to 18% for wind. The EU Renewables Directive does not require this, but several countries have recognised this by tailoring support to the technology.
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Next, this support for RD&D is support for the public good of knowledge that might help save the planet, and is not just a benefit enjoyed by electricity consumers. Logically, then, it should be funded out of general revenue (or the proceeds from auctioning EUAs, for example). The only case for burdening electricity consumers is that the current tax system under-charges them—in the UK electricity is relatively subsidised by the difference between the standard rate of VAT, usually 1712%, and the reduced energy rate of 5%. To summarise, I would argue that the premium to give over and above the nodal value of electricity is the difference between the social cost of carbon and the EUA price, per MWh, plus the learning benefit to ascribe to the particular technology. The shortfall on the carbon price could be made up by a Contract for Difference (CfD) on the carbon price that pays C-E per MWh, where C is the social cost of carbon and E is the EUA price, both embodied in the marginal unit of generation. The learning benefit will be largely created by successfully commissioning the wind turbine or park, although one would want to reward high availability and higher output per unit of installed capacity. That would suggest a payment per MW of capacity installed and a continuing payment per MWh generated. Political or regulatory uncertainty could be reduced by making the first capital grant contingent upon successful commissioning, with a contracted premium payment per MWh delivered for a specified period. The resulting revenue would thus be a combination of a subsidy against the capital cost, a predictable premium (carbon plus the learning benefit) per MWh, and the nodal price, which will be volatile, as discussed below, and for which a contract would reduce risk. Here a conventional CfD would not be suitable as that would be for firm delivery at specified hours, something wind farms cannot guarantee. Instead the contract would need to be for an annual average price per MWh at that node, about which there should not be so much uncertainty. The TSO would be the natural counter-party to the nodal price risk as it is in the best place to manage that risk and to offer a long-term contract. The fourth possible market failure is that the locational signals, even with nodal pricing, may not be correct. Transmission is lumpy, and nodal prices reflect short-run marginal costs, not the long-run marginal costs (LRMC) needed to guide decisions on where to locate (Newbery, 2005b; Brunekreeft et al., 2005). This problem can be handled by offering a long-term CfD on the nodal price (effectively a Transmission Constraint Contract, TCC, as discussed by e.g. Brunekreeft et al., 2005) combined with a lumpsum or periodic payment for connection or location network tariff, calculated as the shortfall between the long-run marginal cost of grid reinforcement costs and the revenues from the TCC (equal to expected revenue from selling at the nodal price). Even here, there are potential problems, as the LRMC may fall short of the average cost of the required investment, so that it is necessary to check that the overall investment has a social benefit greater than the social cost (Weigt et al., this issue). This problem is particularly likely where it would make sense to build a high capacity transmission link but only if sufficiently many wind farms were to be built as well, but there is uncertainty about how many will be built. Targets, such as those in the Renewables Directive, might be quite helpful in inducing the required coordination rather than leaving everything to decentralised price-guided market decisions. Longer-term regulatory commitments to fund such investments may also help. Finally, large-scale wind will have considerable impacts on wholesale prices, as there will be many hours when wind output could exceed total demand, leading to a considerable increase in volatility and greater risks of market power if the conventional generation is concentrated (Green and Vasilakos, this issue; Neuhoff and Twomey, this issue). A large part of the reform of
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electricity markets and system operation will be directed to mitigating such adverse effects. Greater volatility may also disadvantage non-renewable lowcarbon generation such as nuclear power (or clean coal with carbon capture and storage, CCS), unless they are part of the portfolio of incumbent diversified companies. Again, contracts are the logical solution (and one that also addresses concerns over market power), but the liquidity of the necessary contract and reference markets may be low. Liquidity can be enhanced by moving to a gross pool, through which all power is traded spot, and on whose prices contracts can be written to address temporal volatility. Nodal prices, however, create spatial variations that need additional hedging contracts (such as TCCs), and again these are likely to be traded on illiquid markets. The logical provider of such spatial hedges would be the System Operator (acting as a Market Operator), but again this raises concerns over governance arrangements for this natural monopoly. Even if these can be addressed, lower load factors for conventional plant and persistent higher volatility may deter new investment, as very longterm contracts are unlikely in fully unbundled and liberalised markets. Underwriting the CO2 price over a sufficiently long period, such as 15–20 years (by a floor or CfD on the carbon price) would remove much of the regulatory risk, and ought to be sufficient for nuclear power (although perhaps not for CCS, which may justify additional learning benefit support). Whether or not capacity payments are needed as well to some extent depends on whether the SO can offer longer-term contracts for availability (Newbery, 2005a), and will in any case be more important for peaking power than base-load nuclear power. To conclude, large-scale expansion of wind power poses significant challenges to any market design and regulatory approach that is not guided by the principles of aligning prices with social costs and benefits and reducing unnecessary risks—and to that extent should be welcomed as precipitating reforms that are needed in any case. The worry is that costly fixes that fail to address underlying flaws will be politically simpler than intelligent reform. This issue will have amply fulfilled its social function if by pointing out the risks and opportunities that wind power creates it encourages intelligent reform. Research Director, Electricity Policy Research Group. Research support by the ESRC to the Electricity Policy Research Group, EPRG, under the programme Towards a Sustainable Energy Economy is gratefully acknowledged.
References Boccard, N. Economic properties of wind power—an European assessment. Energy Policy, this issue, doi:10.1016/j.enpol.2009.07.033. Brunekreeft, G., Neuhoff, K., Newbery, D., 2005. Electricity transmission: an overview of the current debate. Utilities Policy 13 (2), 73–94 June. Green, R.J., 2008. Electricity wholesale markets: designs now and in a low-carbon future. The Energy Journal 2008 Special Issue: The Future of Electricity: Papers in Honor of David Newbery (pp. 95–124), 95–124. Green, R.J., Vasilakos, N. Market behaviour with large amounts of intermittent generation. Energy Policy, this issue, doi:10.1016/j.enpol.2009.07.038. Grubb, M.G., Newbery, D.M., 2008. Pricing carbon for electricity generation: national and international dimensions. In: Grubb, M., Jamasb, T., Pollitt, M. (Eds.), Delivering a Low Carbon Electricity System: Technologies, Economics and Policy. CUP, pp. 278–313 ch. 11. Hiroux, C., Saguan, M. Large-scale wind power in European electricity markets: time for revisiting support schemes and market designs. Energy Policy, this issue, doi:10.1016/j.enpol.2009.07.030. Hope, C., Newbery, D.M., 2008. Calculating the social cost of carbon. In: Grubb, M., Jamasb, T., Pollitt, M. (Eds.), Delivering a Low Carbon Electricity System: Technologies, Economics and Policy. CUP, pp. 31–63 ch. 2. International Energy Agency (IEA), 2000. Experience Curves for Technology. IEA, Paris. Lewis, G. Estimating the value of wind energy using locational marginal price. Energy Policy, this issue, doi:10.1016/j.enpol.2009.07.045.
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MacGill, I. Electricity market design for facilitating the integration of wind energy: experience and prospects with the Australian National Electricity Market. Energy Policy, this issue, doi:10.1016/j.enpol.2009.07.047. ¨ Matevosyan, J., Soder, L., 2007. Short-term hydropower planning coordinated with wind power in areas with congestion problems. Wind Energy Journal 10 (3), 195–208 (John Weiley & Sons, Ltd; May 2007). Neuhoff, K., 2008. Learning by doing with constrained growth rates: an application to energy technology policy. The Energy Journal 2008 Special Issue: The Future of Electricity: Papers in Honor of David Newbery (pp. 165–182), 165–182. Neuhoff, K., Twomey, P. Wind power and market power in competitive markets. Energy Policy, this issue, doi:10.1016/j.enpol.2009.07.031. Newbery, D.M., 2005a. Electricity liberalisation in Britain: the quest for a satisfactory wholesale market design. In: Newbery, D., (Ed.), Energy Journal, Special Issue on European Electricity Liberalisation, 43–70. Newbery, D.M., 2005b. Integrating electricity transmission systems. Utilities Policy 13 (2), 69–72 June. Redpoint, et al., 2008. Implementation of EU 2020 renewable target in the UK electricity sector: renewable support schemes—a report for the department for business, enterprise and regulatory reform, Redpoint consultants, Cambridge University, Trilemma UK, London. Rivier, J. Electricity market participation of wind farms: the success story of the Spanish pragmatism. Energy Policy, this issue, doi:10.1016/j.enpol.2009.07.032.
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David Newbery Faculty of Economics, University of Cambridge, Sidgwick Avenue, Cambridge CB3 9DE, England E-mail address: [email protected]