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Journal articles on the topic 'Electricity markets'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 June 2025

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1

Borenstein, Severin, James Bushnell, Edward Kahn, and Steven Stoft. "Market power in California electricity markets." Utilities Policy 5, no. 3-4 (1995): 219–36. http://dx.doi.org/10.1016/0957-1787(96)00005-7.

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2

Green, R. "Electricity and Markets." Oxford Review of Economic Policy 21, no. 1 (2005): 67–87. http://dx.doi.org/10.1093/oxrep/gri004.

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3

Bauer, Douglas C. "US Electricity Markets." Energy Exploration & Exploitation 4, no. 2-3 (1986): 177–90. http://dx.doi.org/10.1177/014459878600400210.

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Current US electricity markets are showing improvement, reflecting improvement in the economy as a whole. However, we do have several concerns for the future. The risks which accompany new power plant construction have led the industry, as well as others, to seek out new alternatives. Canadian imports, cogeneration, and improved bulk power markets all have a role to play in future utility planning. But, I believe we must still retain the option of new central station generation. Current attempts in the US to remove capital formation incentives through tax reform, to prohibit construction work in progress in the rate base, and to exclude surplus capacity from cost recovery are examples of public policy decisions which we believe would be counterproductive to providing low cost, reliable power to consumers. Rather, we believe public policy should focus on providing the utility industry with the opportunities to make the best long-term economic decisions on behalf of its customers.

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4

Bishop, Simon, and Ciara McSorley. "Regulating Electricity Markets." Electricity Journal 14, no. 10 (2001): 81–86. http://dx.doi.org/10.1016/s1040-6190(01)00258-5.

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5

Ruff, Larry E. "Competitive Electricity Markets." Electricity Journal 12, no. 9 (1999): 20–35. http://dx.doi.org/10.1016/s1040-6190(99)00079-2.

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6

Barreda-Tarrazona, Iván, Aurora García-Gallego, Marina Pavan, and Gerardo Sabater-Grande. "Demand response in experimental electricity markets." Revista Internacional de Sociología 70, Extra_1 (2012): 127–65. http://dx.doi.org/10.3989/ris.2011.10.30.

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7

Rahimi, A. F., and A. Y. Sheffrin. "Effective market monitoring in deregulated electricity markets." IEEE Transactions on Power Systems 18, no. 2 (2003): 486–93. http://dx.doi.org/10.1109/tpwrs.2003.810680.

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8

Wang, P., Y. Xiao, and Y. Ding. "Nodal Market Power Assessment in Electricity Markets." IEEE Transactions on Power Systems 19, no. 3 (2004): 1373–79. http://dx.doi.org/10.1109/tpwrs.2004.831695.

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9

Doorman, Gerard, and Bjørn Nygreen. "Market Price Calculations in Restructured Electricity Markets." Annals of Operations Research 124, no. 1-4 (2003): 49–67. http://dx.doi.org/10.1023/b:anor.0000004762.31449.33.

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10

Mjelde, James W., and David A. Bessler. "Market integration among electricity markets and their major fuel source markets." Energy Economics 31, no. 3 (2009): 482–91. http://dx.doi.org/10.1016/j.eneco.2009.02.002.

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11

Penya, Yoseba K., and Nicholas R. Jennings. "Optimal combinatorial electricity markets." Web Intelligence and Agent Systems: An International Journal 6, no. 2 (2008): 123–35. http://dx.doi.org/10.3233/wia-2008-0133.

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12

Mays, Jacob. "Quasi-Stochastic Electricity Markets." INFORMS Journal on Optimization 3, no. 4 (2021): 350–72. http://dx.doi.org/10.1287/ijoo.2021.0051.

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With wind and solar becoming major contributors to electricity production in many systems, wholesale market operators have become increasingly aware of the need to address uncertainty when forming prices. Although implementing theoretically ideal stochastic market clearing to address uncertainty may be impossible, the use of operating reserve demand curves allows market designers to inject an element of stochasticity into deterministic market clearing formulations. The construction of these curves, which alter the procurement of reserves and therefore the pricing of both reserves and energy, relies on contentious administrative parameters that lack strong theoretical justification. This paper proposes instead to link their construction to outcomes that would be expected in efficient stochastic markets. The analysis considers the potential of these “quasi-stochastic” market clearing approaches to improve efficiency relative to the deterministic status quo as well as ways in which they are unable to fully replicate the stochastic ideal. Further, the paper argues that efficiently managing uncertainty entails a reexamination of the discriminatory uplift payments and enhanced pricing schemes currently employed to address nonconvexity.

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13

Taylor, W. "Competition in Electricity Markets." IEEE Power Engineering Review 16, no. 7 (1996): 11. http://dx.doi.org/10.1109/mper.1996.512035.

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14

Salerian, John, Tendai Gregan, and Ann Stevens. "Pricing in Electricity Markets." Journal of Policy Modeling 22, no. 7 (2000): 859–93. http://dx.doi.org/10.1016/s0161-8938(98)00033-7.

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15

BILAS, RICHARD A., KENNETH L. LAY, GORDON R. SMITH, MICHAL C. MOORE, and ROBERT J. MICHAELS. "POWER MARKETS: RESTRUCTURING ELECTRICITY." Contemporary Economic Policy 17, no. 1 (1999): 1–19. http://dx.doi.org/10.1111/j.1465-7287.1999.tb00659.x.

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16

Mozdawar, Seyed Alireza, Asghar Akbari Foroud, and Meysam Amirahmadi. "Interdependent electricity markets design: Market power and gaming." International Journal of Electrical Power & Energy Systems 136 (March 2022): 107641. http://dx.doi.org/10.1016/j.ijepes.2021.107641.

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17

Wachi, Tsunehisa, Suguru Fukutome, Luonan Chen, and Yoshinori Makino. "Decomposition of Market Clearing Price in Electricity Markets." IEEJ Transactions on Power and Energy 126, no. 3 (2006): 297–307. http://dx.doi.org/10.1541/ieejpes.126.297.

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18

Adelowo, Jacqueline, and Moritz Bohland. "Redesigning automated market power mitigation in electricity markets." International Journal of Industrial Organization 97 (December 2024): 103108. http://dx.doi.org/10.1016/j.ijindorg.2024.103108.

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19

Ausubel, Lawrence M., and Peter Cramton. "Using forward markets to improve electricity market design." Utilities Policy 18, no. 4 (2010): 195–200. http://dx.doi.org/10.1016/j.jup.2010.05.004.

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20

Budhraja, Vikram S. "Harmonizing Electricity Markets with the Physics of Electricity." Electricity Journal 16, no. 3 (2003): 51–58. http://dx.doi.org/10.1016/s1040-6190(03)00028-9.

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21

Bojnec, Štefan. "Electricity Markets, Electricity Prices and Green Energy Transition." Energies 16, no. 2 (2023): 873. http://dx.doi.org/10.3390/en16020873.

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22

Liu, Donglan, Xin Liu, Kun Guo, Qiang Ji, and Yingxian Chang. "Spillover Effects among Electricity Prices, Traditional Energy Prices and Carbon Market under Climate Risk." International Journal of Environmental Research and Public Health 20, no. 2 (2023): 1116. http://dx.doi.org/10.3390/ijerph20021116.

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With the increase in global geopolitical risks and the frequent occurrence of extreme climate in recent years, the electricity prices in Europe have shown large fluctuations. Electricity price has an important impact on the cost of production and living, while electricity demand will also affect other energy markets. A double-layer system based on the spillover effects from a systematic perspective is constructed in this paper to explore the connectedness between different electricity markets and other related energy markets in Europe, considering the impact of climate risks. The results show that there are certain spillover effects among electricity markets in different countries, with a temporary upward trend in the beginning of the Russia–Ukraine conflict, and the electricity markets in the UK and Germany have a more important role in Europe. There are two-way spillover effects between the electricity market and fossil fuel markets, carbon market and carbon emission. Since 2022, the electricity market is affected by gas prices, while it has a certain impact on carbon emissions. The heating degree day (HDD) has significant spillover effects on the electricity market and other energy markets, while the spillover effects of the cooling degree day (CDD) are relatively small.

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23

Yang, Yang, Minglei Bao, Yi Ding, Yonghua Song, Zhenzhi Lin, and Changzheng Shao. "Review of Information Disclosure in Different Electricity Markets." Energies 11, no. 12 (2018): 3424. http://dx.doi.org/10.3390/en11123424.

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Electricity markets have been established in many countries of the world. Electricity and services are traded in the competitive environment of electricity markets, which generates a large amount of information during the operation process. To maintain transparency and foster competition of electricity markets, timely and precise information regarding the operation of electricity market should be disclosed to the market participants through a centralized and authorized information disclosure mechanism. However, the information disclosure mechanism varies greatly in electricity markets because of different market models and transaction methods. This paper reviews information disclosure mechanisms of several typical electricity markets with the poolco model, bilateral contract model, and hybrid model. The disclosed information and clearing models in these markets are summarized to provide an overview of the present information disclosure mechanisms in typical deregulated power systems worldwide. Moreover, the various experiences for establishing an efficient information disclosure mechanism is summarized and discussed.

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24

Krajcar, Slavko, Perica Ilak, and Ivan Rajšl. "Hydropower plant simultaneous biding in electricity market and ancillary services markets." Journal of Energy - Energija 64, no. 1-4 (2022): 29–51. http://dx.doi.org/10.37798/2015641-4142.

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In a traditional environment, hydropower plant owners seek for minimum cost while in today deregulated environment goal function is profit maximization. Besides electricity only market, power producers can offer their services also in ancillary services markets. By doing so, it is possible to increase expected profit. This paper focuses on simultaneous hydropower plant biding in electricity and ancillary services markets, and purpose is to examine and verify effects of the proposed method on expected profit of hydropower plan owner. A mathematical model based on mixed integer programming approach is used. Head effect is also take into account with price-wise linear performance curves. Prices from real electricity markets and ancillary markets are used, and real hydropower system Lokve-Bayer in Croatia, with focus on hydropower plant Vinodol, is modelled. Obtained results show that there is a notable improvement in expected profit of hydropower plant if presented market bidding approach is used. It is also shown that hydropower plant Vinodol is capable for simultaneous bidding in different power markets.

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25

Olson, Mark, Stephen Rassenti, Mary Rigdon, and Vernon Smith. "Market Design and Human Trading Behavior in Electricity Markets." IIE Transactions 35, no. 9 (2003): 833–49. http://dx.doi.org/10.1080/07408170304406.

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26

Morales, J. M., S. Pineda, A. J. Conejo, and M. Carrion. "Scenario Reduction for Futures Market Trading in Electricity Markets." IEEE Transactions on Power Systems 24, no. 2 (2009): 878–88. http://dx.doi.org/10.1109/tpwrs.2009.2016072.

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27

Anderson, E. J., and T. D. H. Cau. "Implicit collusion and individual market power in electricity markets." European Journal of Operational Research 211, no. 2 (2011): 403–14. http://dx.doi.org/10.1016/j.ejor.2010.12.016.

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28

Bunn, Derek W., Isaac Dyner, and Erik R. Larsen. "Modelling latent market power across gas and electricity markets." System Dynamics Review 13, no. 4 (1997): 271–88. http://dx.doi.org/10.1002/(sici)1099-1727(199724)13:4<271::aid-sdr131>3.0.co;2-3.

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29

WU, Felix. "Recent Trends of Electricity Markets." Journal of the Institute of Electrical Engineers of Japan 118, no. 3 (1998): 169–72. http://dx.doi.org/10.1541/ieejjournal.118.169.

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30

Green, R. "Markets for Electricity in Europe." Oxford Review of Economic Policy 17, no. 3 (2001): 329–45. http://dx.doi.org/10.1093/oxrep/17.3.329.

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31

Lock, Reinier. "Surveillance of Competitive Electricity Markets." Electricity Journal 11, no. 2 (1998): 17–27. http://dx.doi.org/10.1016/s1040-6190(98)00003-7.

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32

Conejo, Antonio J., and Francisco J. Prieto. "Mathematical programming and electricity markets." Top 9, no. 1 (2001): 1–22. http://dx.doi.org/10.1007/bf02579062.

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33

Daglish, Toby. "Consumer governance in electricity markets." Energy Economics 56 (May 2016): 326–37. http://dx.doi.org/10.1016/j.eneco.2016.03.018.

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34

Liu, Min, and Felix F. Wu. "Portfolio optimization in electricity markets." Electric Power Systems Research 77, no. 8 (2007): 1000–1009. http://dx.doi.org/10.1016/j.epsr.2006.08.025.

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35

Mayer, Klaus, and Stefan Trück. "Electricity markets around the world." Journal of Commodity Markets 9 (March 2018): 77–100. http://dx.doi.org/10.1016/j.jcomm.2018.02.001.

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36

Joskow, Paul, and Jean Tirole. "Reliability and competitive electricity markets." RAND Journal of Economics 38, no. 1 (2007): 60–84. http://dx.doi.org/10.1111/j.1756-2171.2007.tb00044.x.

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37

Belyaev, Lev. "Electricity markets [The Business Scene]." IEEE Power and Energy Magazine 5, no. 3 (2007): 16–26. http://dx.doi.org/10.1109/mpae.2007.365809.

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38

van Welie, Gordon. "Keeping Wholesale Electricity Markets Competitive." IEEE Power and Energy Magazine 16, no. 2 (2018): 62–65. http://dx.doi.org/10.1109/mpe.2018.2811813.

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39

Basterfield, David, Thomas Bundt, and Kevin Nordt. "Risk management in electricity markets." Managerial Finance 36, no. 6 (2010): 525–33. http://dx.doi.org/10.1108/03074351011043008.

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40

Defeuilley, Christophe. "Retail competition in electricity markets." Energy Policy 37, no. 2 (2009): 377–86. http://dx.doi.org/10.1016/j.enpol.2008.07.025.

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41

Garcia, Alfredo, and Ennio Stacchetti. "Investment dynamics in electricity markets." Economic Theory 46, no. 2 (2009): 149–87. http://dx.doi.org/10.1007/s00199-009-0508-3.

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42

Reneses, Javier, Julian Barquín, Javier García-González, and Efraim Centeno. "Water value in electricity markets." International Transactions on Electrical Energy Systems 26, no. 3 (2015): 655–70. http://dx.doi.org/10.1002/etep.2106.

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43

Drom, Rick. "Electricity Capacity Markets: A Primer." Natural Gas & Electricity 30, no. 10 (2014): 1–8. http://dx.doi.org/10.1002/gas.21758.

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44

Arci, Francesco, Jane Reilly, Pengfei Li, Kevin Curran, and Ammar Belatreche. "Forecasting Short-term Wholesale Prices on the Irish Single Electricity Market." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (2018): 4060–78. https://doi.org/10.11591/ijece.v8i6.pp4060-4078.

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Electricity markets are different from other markets as electricity generation cannot be easily stored in substantial amounts and to avoid blackouts, the generation of electricity must be balanced with customer demand for it on a second-by-second basis. Customers tend to rely on electricity for day-to-day living and cannot replace it easily so when electricity prices increase, customer demand generally does not reduce significantly in the short-term. As electricity generation and customer demand must be matched perfectly second-by-second, and because generation cannot be stored to a considerable extent, cost bids from generators must be balanced with demand estimates in advance of real-time. This paper outlines a a forecasting algorithm built on artificial neural networks to predict short-term wholesale prices on the Irish Single Electricity Market so that market participants can make more informed trading decisions. Research studies have demonstrated that an adaptive or self-adaptive approach to forecasting would appear more suited to the task of predicting energy demands in territory such as Ireland. We have identified the features that such a model demands and outline it here.

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45

Lin, Jie, Wei Yu, and Xinyu Yang. "Towards Multistep Electricity Prices in Smart Grid Electricity Markets." IEEE Transactions on Parallel and Distributed Systems 27, no. 1 (2016): 286–302. http://dx.doi.org/10.1109/tpds.2015.2388479.

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46

Gilmore, Joel, Ben Vanderwaal, Ian Rose, and Jenny Riesz. "Integration of solar generation into electricity markets: an Australian National Electricity Market case study." IET Renewable Power Generation 9, no. 1 (2015): 46–56. http://dx.doi.org/10.1049/iet-rpg.2014.0108.

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47

Denysiuk, S., H. Bielokha, and K. Ushchapovsky. "PARTICIPATION OF AGGREGATORS IN ELECTRICITY MARKETS OF LOCAL ELECTRICITY SYSTEM." POLISH JOURNAL OF SCIENCE, no. 82 (January 20, 2025): 56–61. https://doi.org/10.5281/zenodo.14707937.

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The local electricity system is a decentralized network. To achieve effective interaction between suppliers and consumers in the operation of local systems in electricity markets, a load aggregator is needed. The goal of the aggregator is to maximize its profit in the day-ahead energy market. The article examines the features of the participation of load aggregators in day-ahead electricity markets and presents an analysis of the aggregator's operation with forecasted load schedules when applying different demand response programs. The daily load schedule and 4 load change strategies (Load Curtailment, Load Shifting, Flexible Load Shape, Energy Arbitrage, Utilizing Onsite Generation, Utilizing ES System) for a local electricity system consisting of a centralized grid, an energy storage system, and a solar generator are considered. An analysis of the impact of different load change strategies on the total daily cost of electricity showed that all strategies will lead to a decrease in cost. The best results were shown by Load Curtailment strategies and Energy Arbitrage.

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48

Bojnec, Štefan, and Alan Križaj. "Electricity Markets during the Liberalization: The Case of a European Union Country." Energies 14, no. 14 (2021): 4317. http://dx.doi.org/10.3390/en14144317.

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This paper analyzes electricity markets in Slovenia during the specific period of market deregulation and price liberalization. The drivers of electricity prices and electricity consumption are investigated. The Slovenian electricity markets are analyzed in relation with the European Energy Exchange (EEX) market. Associations between electricity prices on the one hand, and primary energy prices, variation in air temperature, daily maximum electricity power, and cross-border grid prices on the other hand, are analyzed separately for industrial and household consumers. Monthly data are used in a regression analysis during the period of Slovenia’s electricity market deregulation and price liberalization. Empirical results show that electricity prices achieved in the EEX market were significantly associated with primary energy prices. In Slovenia, the prices for daily maximum electricity power were significantly associated with electricity prices achieved on the EEX market. The increases in electricity prices for households, however, cannot be explained with developments in electricity prices on the EEX market. As the period analyzed is the stage of market deregulation and price liberalization, this can have important policy implications for the countries that still have regulated and monopolized electricity markets. Opening the electricity markets is expected to increase competition and reduce pressures for electricity price increases. However, the experiences and lessons learned among the countries following market deregulation and price liberalization are mixed. For industry, electricity prices affect cost competitiveness, while for households, electricity prices, through expenses, affect their welfare. A competitive and efficient electricity market should balance between suppliers’ and consumers’ market interests. With greening the energy markets and the development of the CO2 emission trading market, it is also important to encourage use of renewable energy sources.

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49

Fernando, lezama, Soares João, Faia Ricardo, et al. "Bidding in local electricity markets with cascading wholesale market integration." International Journal of Electrical Power & Energy Systems 131 (April 17, 2021): 107045. https://doi.org/10.1016/j.ijepes.2021.107045.

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Local electricity markets are a promising idea to foster the efficiency and use of renewable energy at the distribution level. However, as such a new concept, how these local markets will be designed and integrated into existing market structures, and make the most profit from them, is still unclear. In this work, we propose a local market mechanism in which end-users (consumers, small producers, and prosumers) trade energy between peers. Due to possible low liquidity in the local market, the mechanism assumes that end-users fulfill their energy demands through bilateral contracts with an aggregator/retailer with access to the wholesale market. The allowed bids and offers in the local market are bounded by a feed-in tariff and an aggregator tariff guaranteeing that end-users get, at most, the expected cost without considering this market. The problem is modeled as a multi-leader single-follower bi-level optimization problem, in which the upper levels define the maximization of agent profits. In contrast, the lower level maximizes the energy traded in the local market. Due to the complexity of the matter, and lack of perfect information of end-users, we advocate the use of evolutionary computation, a branch of artificial intelligence that has been successfully applied to a wide variety of optimization problems. Throughout three different case studies considering end-users with distinct characteristics, we evaluated the performance of four different algorithms and assessed the benefits that local markets can bring to market participants. Results show that the proposed market mechanism provides overall costs improvements to market players of around 30–40% regarding a baseline where no local market is considered. However, the shift to local markets in energy procurement can affect the conventional retailer/aggregator role. Therefore, innovative business models should be devised for the successful implementation of local markets in the future.

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50

Moore, Jared, and Noah Meeks. "Hourly modelling of Thermal Hydrogen electricity markets." Clean Energy 4, no. 3 (2020): 270–87. http://dx.doi.org/10.1093/ce/zkaa014.

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Abstract The hourly operation of Thermal Hydrogen electricity markets is modelled. The economic values for all applicable chemical commodities are quantified (syngas, ammonia, methanol and oxygen) and an hourly electricity model is constructed to mimic the dispatch of key technologies: bi-directional power plants, dual-fuel heating systems and plug-in fuel-cell hybrid electric vehicles. The operation of key technologies determines hourly electricity prices and an optimization model adjusts the capacity to minimize electricity prices yet allow all generators to recover costs. We examine 12 cost scenarios for renewables, nuclear and natural gas; the results demonstrate emissions-free, ‘energy-only’ electricity markets whose supply is largely dominated by renewables. The economic outcome is made possible in part by seizing the full supply-chain value from electrolysis (both hydrogen and oxygen), which allows an increased willingness to pay for (renewable) electricity. The wholesale electricity prices average $25–$45/MWh, or just slightly higher than the assumed levelized cost of renewable energy. This implies very competitive electricity prices, particularly given the lack of need for ‘scarcity’ pricing, capacity markets, dedicated electricity storage or underutilized electric transmission and distribution capacity.

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