Èlektron. model. 2021, 43(3):47-64
Based on the analysis of literature sources, the key scientific and practical tasks of improving and developing the pricing system of the current model of the electricity market in Ukraine "Competition at all levels" have been identified. The formulation of the problem of building a multi-agent simulation model of the pricing process in the electricity market as a complex dynamic system of decentralized interaction between manufacturing agents, wholesale and retail suppliers, energy traders and aggregate electricity consumers is considered.
simulation model, multiagent environment, electricity market, pricing.
- Mokhor, V.V. (2020), “On the creation of a multi-agent simulation model of pricing processes in the electricity market”, Elektronne modelyuvannya, Vol. 42, № 6, р 3-17.
- Law of Ukraine "On the electricity market" № 2019-VІІІ dated April 13, 2017, available at: https://zakon.rada.gov.ua/laws/show/2019 -19 / page2 (accessed: October 10, 2019).
- National Commission for State Regulation of Energy and Utilities. About the statement of Rules of the market. Resolution №307 of March 14, 2018, available at: https://zakon.rada.gov.ua/laws/show/v0307874-18/page#Text (accessed: October 10, 2019).
- National Commission for State Regulation of Energy and Utilities. On approval of the Rules of the day-ahead market and the intraday market. Resolution № 308 of March 14, 2018, available at: https://zakon.rada.gov.ua/laws/show/v0307874-18/page#Text (accessed: October 10, 2019).
- The National Committee, which is good for the state regulation in the spheres of energy and communal services. About the consolidated Code of the Commercial Obligation of Electricity. Resolution № 311 dated March 14, 2018, available at: https://zakon.rada.gov.ua/laws/show/v0307874-18/page#Text (accessed: October 10, 2019).
- Market Operator, URL: https://www.oree.com.ua/.
- UKRENERGO, URL: https://ua.energy/.
- Blinov, І.V. (2011), “Prior to the formation of lots of producers for auctions for the purchase and sale of electrical energy”, Pratsi Instytutu elektrodynamiky NAN Ukrayiny, Vol. 28, pp. 20-25.
- Korolev, M. (2004), “We make orders for sale…”, Professionalnyy zhurnal, 64-69.
- Amelina, A.Yu. (2004), “Choosing the Optimal Strategy for a Generating Company to Behave in the Day-Ahead Market under Market Regulation”, Vektor nauki TGU, Vol. 4, pp. 63-68.
- Mikeshina, A.A. (2010), “The strategy of behavior of a generating company in the wholesale electricity market”, Ekonomika, Statistika i Informatika, Vol. 6, pp. 190-192.
- Filatov, S.A. (2013), “Models and methods for the formation of optimal strategies for the behavior of generating companies in the wholesale electricity market”, Abstract of Cand. Sci. (Econom.) dissertation, 08.00.13, Saint Petersburg State University of Engineering and Economics, Saint Petersburg, Russia.
- Sekretarev, Yu.A. (2018), “Mathematical model of management of the functioning of a generating company in modern conditions”, Tomskogo politekhnicheskogo universiteta. Inzhiniring georesursov, Vol. 329, № 2, pp. 146-158.
- Biteryakov, Yu.F. (2005), “On the issue of price formation in competitive electricity markets”, Vestnik IGEU, 2, pp. 1-4.
- Kirilenko, O.V. (2011), “Balancing market of electricity of Ukraine and a mathematical model”, Tekhnichna elektrodynamika, Vol. 2, pp. 36–43.
- Kirilenko, O.V. (2019), “Simulation model of the market of electrical energy "in good time" with implicit consideration of festooned interconnection of energy systems, Tekhnichna elektrodynamika, 5, pp. 60-67, DOI:
- Kamchatova, E.Yu. (2016), “Risks of energy companies”, Vestnik universiteta, Vol. 11, pp. 69-74.
- Market Risk Management in Russian electricity companies. Analyticalstudy. KPMG (2012), available at: https://www.kpmg.com/RU/ru/IssuesAndInsights/ ArticlesPublications/Documents/Market-risk-management-at-Russian-power-companies-rus.pdf (accessed: October 11, 2016).
- Azevedo, F. (2006), “Forecasting electricity prices with historical statistical information using neural networks and clustering techniques”, IEEE PES Power Systems Conference and Exposition, pp. 44-50.
- Jain, A. (2009), “Short Term Load Forecasting by Clustering Technique based on Daily Average and Peak Loads”, Proceedings of 2009 IEEE Power Energy Society General Meeting (PESGM 2009), Report №: IIIT/TR/2009/205, Centre for Power Systems, International Institute of Information Technology, Hyderabad, India, July 2009.
- Аzevedo, F. (2010), “A long-term risk management tool for electricity markets using swarm intelligence”, Electric Power Systems Research, 380-389.
- Ortiz, M. (2016), “Price forecasting and validation in the Spanish electricity market using forecasts as input data”, Electrical Power and Energy Systems, № 77, pp. 123-127 (available at: www.elsevier.com/locate/ijepes).
- Chujie, T. (2018), “A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network”, Energies, № 11, p. 13, DOI:10.3390/en11123493, available at: www.mdpi.com/journal/energies.
- NEMSIM: the National Electricity Market simulator, available at: http://press-files.anu.edu.au/downloads/ press/p96431/mobile/ch11s08.html.
- Borukaev, Z.Kh. (2006), Approach to the construction of a game-theoretic model of the energy market, Elektronne modelyuvannya, Vol. 28, № 4, pp. 107-119.