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Demand Response in Electricity Markets: An Overview and a Study of the Price-Effect on the Iberian Daily Market

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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 electricity industry is undergoing a deep transformation as Europe moves towards a greener, healthier future—the growth of renewable generation has surpassed all expectations and demand response (DR) has emerged as a key element of market design. Most European countries have already opened their markets to the participation of demand response and, over the long-term, DR will probably reach its full potential as the entire range of DR programs will be made available to retail customers. To date, however, progress has been only gradual. There is currently a need to understand and quantify the major impacts and benefits of DR, to facilitate an effective implementation of DR programs. Accordingly, this chapter investigates the impact of different levels of DR on the Iberian market prices, during the period 2014–2017, and analyzes the potential benefits for market participants and retail customers. The results generated by an agent-based simulation tool, called MATREM, are striking. In the year 2017, for instance, a modest load reduction of 5% when prices rose above 80 €/MWh yielded the (very large) benefit of 76.62 million €. Also, the same decrease in load when prices exceeded 90 €/MWh provided the (still large) benefit of 39.05 million €. The chapter concludes with specific recommendations—for consideration by state institutions, system operators, electric utilities and other market participants—to foster demand response in Portugal through both incentive-based and price-based programs.

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Notes

  1. 1.

    The authors are aware of no other work to investigate the price effect of DR on the Iberian market at times of system constraints. Fernández et al. [8] analyze the economic impact of a DR program—the voluntary price for small consumers—on the Iberian market during the period from April 2014 till March 2015. However, the study considers load reductions of 1.5, 3 and 6% uniform for all hours of a 24-hour day. Also, there are various other studies that link specific levels of DR to decreases in market prices, mostly involving markets from the United States, indicating that the benefits may be quite significant (see, e.g., the three-percent solution [9]).

  2. 2.

    Notice that demand response can perform three key functions [4, 10]: energy, capacity and ancillary services. Put another way, demand response can participate on wholesale energy markets (i.e., day-ahead and real-time markets), capacity markets, and balancing and ancillary services markets. Throughout this chapter, we focus primarily on day-ahead markets and the material that follows clearly reflects this bias.

  3. 3.

    A demand response program is a mechanism for communicating prices and willingness to pay between wholesale and retail power markets, with the immediate objective of achieving load changes, particularly at times of high wholesale prices [1].

  4. 4.

    To be eligible for most demand bidding programs, customers must able to reduce load by a minimum of 100 kW. Small consumers are allowed to participate in some programs through demand response aggregators.

  5. 5.

    Chapter 2 gives a brief overview of energy markets and Chap. 8 describes the DAM supported by the agent-based system called MATREM. The interested reader is therefore referred to them for further details of market operation and electricity pricing. See also Sect. 10.3.1 for a detailed overview of the Iberian Electricity Market.

  6. 6.

    For example, customers unable to reduce load by the bid amount during the scheduled time pay the higher of the day-ahead price or the real-time price for the amount of the incomplete scheduled load reduction [19].

  7. 7.

    Orden ITC/2370/2007, BOE-A-2007-14798, enacted on 4 August, 2007.

  8. 8.

    Orden IET/2013/2013, BOE-A-2013-11461, passed on 1 November, 2013.

  9. 9.

    Notice that the natural way to account for demand response in day-ahead markets, when some customers face dynamic retail prices, is for retailers to offer price-responsive loads. For instance, a retailer that expects to serve 5000 MW in a hot summer period, armed with the information that RTP customers will reduce load by 250 MW if prices reach 80 €/kWh, may submit offers to purchase 5000 MW if prices remain low or 4750 MW if prices are expected to rise above 80 €/kWh. Such price-responsive demand offers provide the mechanism for informing the market about the extent of demand response, and typically result in lower day-ahead prices.

  10. 10.

    Generally speaking, real-time pricing means tariffed retail charges for delivered electric power that vary hour-to-hour, determined to some extent from wholesale market prices, using pre-specified methodologies.

  11. 11.

    Recall that the price of electricity varies with the time of day, day of week, or season of the year, and typically peaks when load peaks.

  12. 12.

    Two-part RTP customers who do not deviate from the baseline level pay the same amount under the two-part rate or the standard (non-RTP) rate. Accordingly, the two-part RTP rate is sometimes called “revenue neutral at the CBL” [28].

  13. 13.

    Royal Decree-Law 9/2013, BOE-A-2013-7705, of 12 July, 2013.

  14. 14.

    Royal Decree 216/2014, BOE-A-2014-3376, enacted on 28 March, 2014.

  15. 15.

    Royal Decree-Law 8/2014, BOE-A-2014-7064, passed on 5 July, 2014. Also, Law 18/2014, BOE-A-2014-10517, of 17 October, 2014.

  16. 16.

    https://www.esios.ree.es/en/pvpc (accessed on 15 May, 2017).

  17. 17.

    The instrumental sale price, or minimum price (price floor), is set at 0 €/MWh, and the instrumental purchase price, or maximum price (price cap), is set at 180.30 €/MWh [38].

  18. 18.

    Here, for the sake of clarity in exposition, the two sale curves are referred to as the “first” or “initial” curve and the “second” or “actual” curve.

  19. 19.

    A full description of the benefits of demand response in electricity markets is beyond the scope of this chapter. For further information, the interested reader is referred to [2, Sect. 3].

  20. 20.

    Figure 10.2 presents a hypothetical situation for illustrative purposes only. For the sake of clarity, the load reduction from customers participating in DR programs—that is, the demand reduction that moves consumption from \(Q^*\) to \(Q_{DR}\)—was intentionally enlarged.

  21. 21.

    A price-duration curve is a curve that shows the fraction of the number of hours of a year during which the electricity price was less than a given value. This curve appears commonly in the technical literature on energy markets (see, e.g., [45, 46]). It allows us to observe the percentage of hours where the price reached and/or exceeded a given value. To enhance readability, and also in the interests of completeness, we present this information in a table.

  22. 22.

    The reader familiar with the Iberian market (and other European markets) might find the lower bound (75 €/MWh) and the upper bound (100 €/MWh) of the price threshold somewhat arbitrary (and even rather ad hoc). Nevertheless, the lower bound is considerably higher than the average price for each year of the observation period (see Table 10.1) and, therefore, suitable to the purposes of this work. Furthermore, the decision to set the upper bound at 100 €/MWh seems to be natural, intuitive and rather elegant.

  23. 23.

    The market prices never exceeded 110 €/MWh during the observation period. The highest price (110 €/MWh) was observed on Monday 17 February 2014 at 10 p.m.

  24. 24.

    Chapter 8 is entirely devoted to the agent-based simulation tool and the interested reader is referred to it for details.

  25. 25.

    The reason for an upper bound of 60 €/MWh is as follows. The simulation-based study includes scenarios involving load reductions of 1, 3 and 5%, and price thresholds of 80, 90 and 100 €/MWh (see below). This means that the market-clearing price may drop below 80 €/MWh, depending on the scenario under consideration. However, for all scenarios, the lowest equilibrium price is always greater than 60 €/MWh.

  26. 26.

    For the purposes of this work, a demand response event is any event that results in a load reduction when the spot power price rises above a given threshold. Such an event corresponds to a single hour of operation, may occur at any time of the day, and is assumed to be related to a reduction in electricity usage by retail customers.

  27. 27.

    Market splitting in the daily horizon involves basically the segmentation of the Iberian market into two independent markets due to congestion in the Portugal-Spain interconnection, typically leading to different prices for the Portuguese and Spanish areas, yet making possible to exhaust the available capacity safely. Chapter 9 presents a brief introduction to market splitting in the daily horizon and the interested reader is, therefore, referred to it for details (see also [53, 54]).

  28. 28.

    The average monthly price reductions are computed by considering the hours of operation corresponding to demand response events only.

  29. 29.

    Strictly speaking, the left-hand dark blue curve of Fig. 10.7 is also the price-inelastic demand curve adopted for scenario B3.

  30. 30.

    Notice that the year 2017 involved the highest number of demand response events (namely, 201 DR events). All events occurred in January.

  31. 31.

    As stated in Chap. 9, a considerable volume of electricity is traded via bilateral contracts, whose price may deviate from the spot market price (see, e.g., [37]). Nevertheless, the market price is the leading price indicator for all electricity trades and, therefore, the price paid through bilateral contracts is based, to some extent, upon that price (but see, e.g., [55].

  32. 32.

    As noted above, the magnitude of the price reduction decreases gradually from 2014 to 2017. In direct contrast, the financial benefit of DR is much greater in 2017 than in 2014. The main reason for this increase is naturally related to the number of DR events considered in both years, namely 34 events in 2014 and 201 in 2017.

  33. 33.

    In this chapter, and throughout the book, the term “environmental benefit of DR” is used to denote the CO\({_2}\) savings resulting from demand response.

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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”). Hugo Algarvio was funded by FCT (PD/BD/105863/2014). The authors wish to thank João Santana, 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 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, Avenida Rovisco Pais 1, 1049-001, Lisbon, Portugal

    Hugo Algarvio

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Correspondence to Fernando Lopes .

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    Fernando Lopes

  2. University of Lisbon, Lisbon, Portugal

    Helder Coelho

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Lopes, F., Algarvio, H. (2018). Demand Response in Electricity Markets: An Overview and a Study of the Price-Effect on the Iberian Daily Market. 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_10

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