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Renewable Generation, Support Policies and the Merit Order Effect: A Comprehensive Overview and the Case of Wind Power in Portugal

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Electricity Markets with Increasing Levels of Renewable Generation: Structure, Operation, Agent-based Simulation, and Emerging Designs

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 144))

Abstract

The growth of wind power generation over the past decade has surpassed all expectations. The cost of the wind energy support policy was, however, quite significant and to a large extent has led to somewhat intensive debates. The merit order effect (MOE) is an important aspect to be considered in all debates, albeit sometimes oversimplified or even ignored. Accordingly, the central goal of this chapter is to analyze and quantify the reduction in the Portuguese day-ahead market prices achieved by wind power as a result of the MOE in the first half of 2016. The results generated by an agent-based simulation tool, called MATREM, indicate a price reduction of about 17 €/MWh for the entire study period. The (total) financial volume of the MOE reached the considerable value of 391.055 million €. Especially noteworthy is the net cost of the wind energy support policy, which takes into account the feed-in tariff, the market value of the wind electricity, and the financial volume of the MOE. This cost reached the value of \(-8.248\) million € in January 2016, a negative value, indicating that a net profit has occurred in the month. The (total) net cost was 69.011 million € during the study period. Although considerable, this cost should be interpreted carefully, since it did not take into account the interaction of wind generation with the climate policy and the EU emission trading system (i.e., the carbon price effect on the electricity market).

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Notes

  1. 1.

    The cumulative wind power capacity is based on data provided by Global Wind Energy Council (GWEC). The reason for choosing GWEC’s figures is to present the evolution of the installed capacity using a consistent database based on a standardized methodology for different countries.

  2. 2.

    Feed-in policies guarantee to renewable generators a specified payment over a fixed period. Numerous options exist for defining the level of financial incentive, notably feed-in tariffs (the payment is structured as a guaranteed minimum price) and feed-in premiums (the payment typically involves the addition of a price premium to the spot price, capped at a maximum amount).

  3. 3.

    The European Commission State Aid guidelines, issued in 2014, instructed EU countries to begin using tendering to allocate support to new renewable energy projects in 2015–2016, and also required a shift to renewable energy tenders for the majority of projects in 2017.

  4. 4.

    Decree-Law no. 189/88, enacted on 27 May, 1988.

  5. 5.

    Decree-Law no. 313/95, of 24 December, 1995.

  6. 6.

    Decree-Law no. 168/99, passed on 18 May, 1999.

  7. 7.

    Decree-Law no. 33-A/2005, passed on 16 February, 2005.

  8. 8.

    Statement by the IMF, the ECB and the EC on the first review mission to Portugal. Press release 11/307 (August 12, 2011).

  9. 9.

    Decree-Law no. 51/2012, enacted on 20 May, 2012. It allowed for the installation of 20% more power than the power stated in the grid connection allowance, in return for a discount on the FIT.

  10. 10.

    Decree-Law 215-B/2012, enacted on 8 October, 2012.

  11. 11.

    Decree-Law 35/2013, enacted on 28 February, 2013.

  12. 12.

    Council of Ministers Resolution 20/2013, of 10 April, 2013.

  13. 13.

    Both the price effect and the volume effect are often associated with the direct effect of variable generation on spot market prices. Additionally, an indirect effect is sometimes mentioned in the literature, related to the climate policy and the EU emission trading system (ETS). The rationale for this effect is as follows. Increasing levels of renewable generation reduces the demand for electricity generated by fossil fired power plants, thereby reducing the demand on the emissions trading market (or carbon market). This, in turn, leads to a reduction of the market price of allowances (or carbon price), as long as the supply curve has a positive slope, thus creating savings for the different entities that take part in the ETS, which may ultimately be reflected in the cost of electricity production (see, e.g., [12, 23]).

  14. 14.

    A spot market is a market where delivery is immediate [24]. Typically, notation is somewhat abused, and a day-ahead market is often considered a spot market.

  15. 15.

    The supply curve is also referred to as the merit order curve.

  16. 16.

    The demand curve may simply be a vertical line defined by considering the value of the load forecast, since the demand for electricity may be, and typically is, considered (highly) inelastic and set according to a forecast of the load.

  17. 17.

    A list of generators in ascending order of marginal cost is known as a merit order [26].

  18. 18.

    Notice that large hydropower stations are not mentioned explicitly, since hydro bids are normally considered strategic, depending on precipitation and the level of water in reservoirs.

  19. 19.

    Notice that this particular way to interpret the influence of RES on market prices, the so-called “negative” demand impact, is mentioned in this section for reasons associated with completeness, but is not illustrated in Fig. 9.3.

  20. 20.

    The volume effect is also known as the financial volume of the merit order effect.

  21. 21.

    Equation (9.1) considers that the total energy demand is traded at the spot market.

  22. 22.

    The following sources of data were used for the analysis: hourly generation from wind [27, 28] and hourly day-ahead (spot) prices published by OMIE [29].

  23. 23.

    Wind energy penetration was expressed as the ratio of wind power generation to electricity demand. Accordingly, it represents the approximate share of consumption met by real wind energy production. Albeit on an annual basis, similar definitions of wind energy penetration appear in the technical literature, as well as definitions of wind power capacity penetration (see, e.g., [30, 31]).

  24. 24.

    Chapter 8 is entirely devoted to the agent-based simulation tool and the interested reader is referred to it for further technical details of the power and derivatives exchanges, the bilateral marketplace, the user interface and the human-computer interaction paradigm, as well as the various types of software agents.

  25. 25.

    Market splitting in the daily horizon is the main mechanism to jointly manage the Portugal-Spain interconnection, following a proposal made by the Regulatory Council [44] (see also [45]).

  26. 26.

    Strictly speaking, and as depicted in Fig. 9.5, the market-clearing price results from the intersection of the so-called actual supply and demand curves.

  27. 27.

    This price differential multiplied by the traffic in the interconnection corresponds to the congestion income.

  28. 28.

    Notice that several factors other than wind generation may influence the market price (e.g., the evolution of fuel prices, the level of electricity generation with hydro sources, or maintenance activities). In fact, the month with the highest average wind power penetration rate was not the one with the lowest average real electricity price. Specifically, in February, the average penetration rate reached a maximum of 35.98% and the average market price was 26.48 €/MWh. In April, the average penetration rate was (only) 27.97%, but the average market price fell to a minimum of 23.35 €/MWh. A possible explanation for this effect can be the evolution of the reference price of natural gas during the period under consideration—that is, slightly above 15 €/MWh in January, falling to nearly 12 €/MWh in April, and raising to nearly 15 €/MWh in June [46].

  29. 29.

    Notice that a considerable volume of electricity is traded via bilateral contracts, whose price may deviate from the spot market price (e.g., the notifications of physical delivery of energy from forward contracting conducted in OMIP are, for all purposes, considered supply bids at a specific instrumental price [43]). Nevertheless, the price paid through bilateral contracts is based, to some extent, upon the spot market price, since it is the leading price indicator for all electricity trades.

  30. 30.

    A good review of work on the impact of wind power generation on spot market prices up to 2009 is presented in [12]. See [32] for a general survey of subsequent work on renewable electricity generation and power markets.

  31. 31.

    As noted earlier, there is currently a considerable literature on the impact of increasing levels of renewable generation on electricity markets. For further information about the Spanish and the Germany electricity markets, the interested reader is referred to [56,57,58] and [32, 59,60,61], respectively. See also [62] for further information on the Western Danish price area of the Nord Pool’s Elspot market, [63] for the Italian power market, and [64] for the Belgium market.

  32. 32.

    A cautionary and explanatory note is in order here. The nine selected studies—and other relevant pieces of work proposed in the literature—make use of a diverse range of models and consider different sets of simplifying assumptions. As a consequence, assessing and relating such individual research contributions to draw general conclusions is always a nontrivial (and daunting) task. Any comparative analysis should be carried out carefully and bear in mind the limitations associated with disparate research efforts. Nevertheless, the two conclusions presented here are quite general and, we believe, perfectly acceptable.

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Acknowledgements

This work was performed under the project MAN-REM (FCOMP-01-0124-FEDER-020397), supported by FEDER funds, through the program COMPETE (“Programa Operacional Temático Factores de Competividade”), and also National funds, through FCT (“Fundação para a Ciência e a Tecnologia”). The authors wish to thank Rui Castro, from INESC-ID and also the Technical University of Lisbon (IST), and João Martins and Anabela Pronto, from the NOVA University of Lisbon, for their tireless ability to read the draft and the valuable comments and helpful suggestions to improve the chapter.

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  1. LNEG–National Laboratory of Energy and Geology, Estrada do Paço do Lumiar 22, 1649-038, Lisbon, Portugal

    Fernando Lopes

  2. Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001, Lisbon, Portugal

    João Sá & João Santana

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Lopes, F., Sá, J., Santana, J. (2018). Renewable Generation, Support Policies and the Merit Order Effect: A Comprehensive Overview and the Case of Wind Power in Portugal. In: Lopes, F., Coelho, H. (eds) Electricity Markets with Increasing Levels of Renewable Generation: Structure, Operation, Agent-based Simulation, and Emerging Designs. Studies in Systems, Decision and Control, vol 144. Springer, Cham. https://doi.org/10.1007/978-3-319-74263-2_9

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