Analyzing trade in continuous intra-day electricity market: An agent-based modeling approach

Adaptive learning; Agent-based modeling; Intra-day electricity markets; Renewable energy sources; Autonomous agents; Computational methods; Electric industry; Natural resources; Power markets; Renewable energy resources; Simulation platform; Thermoelectric power plants; Agent based modeling frameworks; Agent-based model; Comparative analysis; Degree of uncertainty; Information asymmetry; Renewable energy source; Thermal power plants; Trading strategies; Consumer behavior

Résumé :

[en] In recent years, the vast penetration of renewable energy sources has introduced a large degree of uncertainty into the power system, thus leading to increased trading activity in the continuous intra-day electricity market. In this paper, we propose an agent-based modeling framework to analyze the behavior and the interactions between renewable energy sources, consumers and thermal power plants in the European Continuous Intra-day (CID) market. Additionally, we propose a novel adaptive trading strategy that can be used by the agents that participate in CID market. The agents learn how to adapt their behavior according to the arrival of new information and how to react to changing market conditions by updating their willingness to trade. A comparative analysis was performed to study the behavior of agents when they adopt the proposed strategy as opposed to other benchmark strategies. The effects of unexpected outages and information asymmetry on the market evolution and the market liquidity were also investigated. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Disciplines :

Energie

Auteur, co-auteur :

Shinde, P.; Division of Electric Power and Energy Systems, KTH Royal Institute of Technology, Stockholm, 11428, Sweden

Boukas, Ioannis ; Université de Liège - ULiège > Montefiore Institute of Electrical Engineering and Computer Science

Radu, David-Constantin ; Université de Liège - ULiège > Université de Liège - ULiège

de Villena, M.M.; Department of Electrical Engineering and Computer Science, University of Liège, Liège, 4000, Belgium

Amelin, M.; Division of Electric Power and Energy Systems, KTH Royal Institute of Technology, Stockholm, 11428, Sweden

Langue du document :

Anglais

Titre :

Analyzing trade in continuous intra-day electricity market: An agent-based modeling approach

Date de publication/diffusion :

2021

Titre du périodique :

Energies

ISSN :

1996-1073

Maison d'édition :

MDPI

Volume/Tome :

Fascicule/Saison :

Peer reviewed :

Peer reviewed vérifié par ORBi

Subventionnement (détails) :

This research was funded by the Swedish Energy Agency.Acknowledgments: The authors are thankful to the Swedish Energy Agency for the financial support to carry out this research work.

Disponible sur ORBi :

depuis le 25 novembre 2022

Statistiques

Nombre de vues

123 (dont 2 ULiège)

Nombre de téléchargements

80 (dont 3 ULiège)

Voir plus de statistiques

citations Scopus^®

citations Scopus^®
sans auto-citations

OpenCitations

citations OpenAlex

Bibliographie

ENTSOE. Power Facts Europe 2019. Available online: https://eepublicdownloads.blob.core.windows.net/public-cdn-container/clean-documents/Publications/ENTSO-E%20general%20publications/ENTSO-E_PowerFacts_2019.pdf (accessed on 8 March 2021).
Neuhoff, K.; Ritter, N.; Salah-Abou-El-Enien, A.; Vassilopoulos, P. Intraday Markets for Power: Discretizing the Continuous Trading? 2016. Available online: https://ssrn.com/abstract=2723902 (accessed on 25 June 2021).
Ciferri, D.; D’Errico, M.C.; Polinori, P. Integration and convergence in European electricity markets. Econ. Politica 2020, 37, 463–492, doi:10.1007/s40888-019-00163-7.
Pogosjan, D.; Winberg, J. Förändringar av Marknadsdesign och deras påverkan på balanshållningen i det Svenska Kraft-systemet: En kartläggning och analys av de Balansansvarigas arbetsgång. 2013. Available online: https://www.svk.se/siteassets/5.jobba-har/dokument-exjobb/forandringar-av-marknadsdesign-och-deras-paverkan-balanshallningen.pdf?_t_ id=qWowEhUA1rBRRNtzXdqH-w==&_t_uuid=-UEFAhWSR7eX49e5YldH_Q&_t_q=sf6-gas&_t_tags=language:sv,siteid: 40c776fe-7e5c-4838-841c-63d91e5a03c9,andquerymatch&_t_hit.id=SVK_WebUI_Models_Media_OfficeDocument/_ffc3ccaf-a4 bd-441d-a665-a05a3e9b59d8&_t_hit.pos=259(accessed on 15 February 2021).
Dobschinski, J.; De Pascalis, E.; Wessel, A.; von Bremen, L.; Lange, B.; Rohrig, K.; Saint-Drenan, Y.M.; Fraunhofer, I.W.E.S.; ForWind, O.; Germany, E. The potential of advanced shortest-term forecasts and dynamic prediction intervals for reducing the wind power induced reserve requirements. In Proceedings of the Scientific Proceedings of the European Wind Power Conference, Warsaw (PL), Warsaw, Poland, 20–23 April 2010; pp. 177–182.
Holttinen, H. Handling of wind power forecast errors in the nordic power market. In Proceedings of the Probabilistic Methods Applied to Power Systems, Stockholm, Sweden, 11–15 June 2006.
Le, H.L.; Ilea, V.; Bovo, C. Integrated European intra-day electricity market: Rules, modeling and analysis. Appl. Energy 2019, 238, 258–273.
Schittekatte, T.; Reif, V.; Meeus, L. The EU Electricity Network Codes (2019 ed.). Available online: https://fsrglobalforum.eu/wp-content/uploads/2019/03/FSR_2019_EU_Electricity_Network_Codes.pdf (accessed on 21 February 2021).
Nordic TSOs. Short-Term Markets. Available online: https://www.svk.se/siteassets/press-och-nyheter/nyheter/tsos-discussion-paper-for-consultation.pdf (accessed on 4 December 2020).
Coulter, K. 5 Trends Reshaping Today’s Global Power Market. Available online: https://www.renewableenergyworld.com/2020/01/14/5-trends-reshaping-todays-global-power-market/#gref(accessed on 12 March 2021).
Nord Pool. Bots Disguised as Electricity Traders Are Placing Orders. Available online: https://careers.nordpoolgroup.com/blog/posts/25998-bots-disguised-as-electricity-traders-are-placing-orders(accessed on 5 April 2021).
NordPool. Nord Pool Key Statistic July 2019. Available online: https://www.nordpoolgroup.com/message-center-container/newsroom/\exchange-message-list/2019/q3/nord-pool-key-statistics---july-2019/(accessed on 10 December 2020).
Shinde, P.; Amelin, M. A literature review of intraday electricity markets and prices. In Proceedings of the 2019 IEEE Milan PowerTech, Milan, Italy, 23–27 June 2019; pp. 1–6.
Skajaa, A.; Edlund, K.; Morales, J.M. Intraday trading of wind energy. IEEE Trans. Power Syst. 2015, 30, 3181–3189. doi:10.1109/TPWRS.2014.2377219.
Boukas, I.; Ernst, D.; Théate, T.; Bolland, A.; Huynen, A.; Buchwald, M.; Wynants, C.; Cornélusse, B. A deep reinforcement learning framework for continuous intraday market bidding. arXiv 2020, arXiv:2004.05940.
Richard Scharff and Mikael Amelin. Trading behaviour on the continuous intraday market elbas. Energy Policy 2016, 88, 544–557.
Alexander von Selasinsky. The Integration of Renewable Energy Sources in Continuos Intraday Markets for Electricity. Ph.D. Thesis, TU Dresden, Dresden, Germany, 2016.
Vytelingum, P. The Structure and Behaviour of the Continuous Double Auction. Ph.D. Thesis, University of Southampton, Southampton, UK, 2006.
Veselka, T.; Boyd, G.; Conzelmann, G.; Koritarov, V.; Macal, C.; North, M.; Schoepfle, B.; Thimmapuram, P. Simulating the Behavior of Electricity Markets with an Agent-Based Methodology: The Electric Market Complex Adaptive Systems (emcas) Model; Center for Energy, Environmental, and Economic Systems Analysis (CEEESA): Vancouver, Canada, 2002. Available online: https://ceeesa.es.anl.gov/pubs/43943.pdf (accessed on 25 June 2021).
Charles, M.; Thimmapuram, P.; Koritarov, V.; Conzelmann, G.; Veselka, T.; North, M.; Mahalik, M.; Botterud, A.; Cirillo, R. Agent-based modeling of electric power markets In Proceedings of the IEEE Winter Simulation Conference, Savannah, GA, USA, 7–10 December 2014.
Grozev, G.; Batten, D.; Anderson, M.; Lewis, G.; Mo, J.; Katzfey, J. Nemsim: Agent-based simulator for australia’s national electricity market. In Proceedings of the SimTecT 2005 Conference Proceedings, Sydney, Australia, 9–12 May 2005.
Audouin, R.; Hermon, F.; Entriken, R. Extending a spot market multi-agent simulator to model investment decisions. In Proceedings of the 2006 IEEE PES Power Systems Conference and Exposition, Atlanta, GA, USA, 29 October–1 November 2006.
Harp, S.A.; Brignone, S.; Wollenberg, B.F.; Samad, T. Sepia. a simulator for electric power industry agents. IEEE Control Syst. Mag. 2000, 20, 53–69.
Sun, J.; Tesfatsion, L. An agent-based computational laboratory for wholesale power market design. In Proceedings of the 2007 IEEE Power Engineering Society General Meeting, Tampa, FL, USA, 24–28 June 2007; pp. 1–6.
Praça, I.; Ramos, C.; Vale, Z.; Cordeiro, M. Mascem: a multiagent system that simulates competitive electricity markets. IEEE Intell. Syst. 2003, 18, 4–60, .
Zhou, Z.; Chan, W.K.V.; Chow, J.H. Agent-based simulation of electricity markets: a survey of tools. Artif. Intell. Rev. 2007, 28, 305–342.
Aliabadi, D.E.; Kaya, M.; Şahin, G. An agent-based simulation of power generation company behavior in electricity markets under different market-clearing mechanisms. Energy Policy 2017, 100, doi:10.1016/j.enpol.2016.09.063.
Lopes, F.; Coelho, H. Electricity Markets with Increasing Levels of Renewable Generation: Structure, Operation, Agent-Based Simulation, and Emerging Designs; Springer: Berlin/Heidelberg, Germany, 2018; Volume 144.
Vermeulen, B.; Pyka, A. Agent-based Modeling for Decision Making in Economics under Uncertainty. Econ. Open-Access Open-Assess. E-J. 2016, 10, 1–33.
Aliabadi, D.E.; Kaya, M.; Sahin, G. Competition, risk and learning in electricity markets: An agent-based simulation study Appl. Energy 2017, 195, 1000-1011..
Poplavskaya, K.; Lago, J.; de Vries, L. Effect of market design on strategic bidding behavior: Model-based analysis of European electricity balancing markets. Appl. Energy 2020, 270, doi:10.1016/j.apenergy.2020.115130.
Shinde, P.; Amelin, M. Agent-Based Models in Electricity Markets: A Literature Review. In Proceedings of the 2019 IEEE Chengdu, China Innovative Smart Grid Technologies—Asia (ISGT Asia), Chengdu, China, 21–24 May 2019; pp. 1–6.
Faia, R.; Pinto, T.; Vale, Z.; Corchado, J.M. Portfolio optimization of electricity markets participation using forecasting error in risk formulation. Int. J. Electr. Power Energy Syst. 2018, 106739, doi:10.1016/j.ijepes.2020.106739.
Canelas, E.; Pinto-Varela, T.; Sawik, B. PortfolioElectricity Portfolio Optimization for Large Consumers: Iberian Electricity Market Case Study. Energies 2018, 13, 106739.
Vytelingum, P.; Cliff, D.; Jennings, N.R. Strategic bidding in continuous double auctions. Artif. Intell. 2008, 172, 1700–1729.
Luca, M.D.; Cliff, D. Human-agent auction interactions: Adaptive-aggressive agents dominate. IJCAI Int. Jt. Conf. Artif. Intell. 2011, 178–185, doi:10.5591/978-1-57735-516-8/IJCAI11-041.
NordPool. Market Opening and Closing Times. Available online: https://www.nordpoolgroup.com/trading/intraday-trading/order-types/(accessed on 10 December 2020).
EPEXSPOT. Epexspot Operational Rules. Available online: https://www.epexspot.com/document/40170/EPEX%20SPOT%20 Market%20Rules (accessed on 6 June 2020).
Cliff, D. BSE: A minimal simulation of a limit-order-book stock exchange. In Proceedings of the 30th European Modeling and Simulation Symposium, EMSS 2018, Budapest, Hungary, 17–19 September 2018; pp. 194–203.
Repository. Intraday Market Simulator. Available online: https://gitlab.uliege.be/Ioannis.Boukas/intraday-market-simulator (accessed on 15 March 2021 ).