Energy Storage Management Using Artificial Intelligence to Maximize Polish Energy Market Profits

Konrad Świrski () and Piotr Błach
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Konrad Świrski: Faculty of Power and Aeronautical Engineering, Institute of Heat Engineering, Warsaw University of Technology, 00-661 Warsaw, Poland
Piotr Błach: Faculty of Power and Aeronautical Engineering, Doctoral School, Warsaw University of Technology, 00-661 Warsaw, Poland

Energies, 2024, vol. 17, issue 19, 1-17

Abstract: Along with the growing renewable energy sources sector, energy storage will be necessary to stabilize the operation of weather-dependent sources and form the basis of a modern energy system. This article presents the possibilities of using energy storage in the energy market (day-ahead market and balancing market) in the current market conditions in Poland after reforming the balancing market in June 2024. The current state of the markets is characterized by high price volatility, which can ensure the high profitability of storage operations. However, very flexible and self-adaptive algorithms for charging and discharging are required, taking advantage of market price spreads. This study aimed to see if, through a solution based on ChatGPT 4o, energy storage operations can be planned by taking maximum advantage of the existing price spreads in the market. Previous analyses in this area have focused on complex models that predicted prices in the markets and planned the plant’s operation on this basis. In this case, the simple model used (charging and discharging based on historical prices) resulted in profits of EUR 90/MWh, while in the second case, when holidays, weather, and demand forecasts were taken into account, the profit was EUR 150–180/MWh, which exceeds the current Levelized Cost of Electricity of storage estimated at around EUR 100/MWh. These analyses indicated that modern genAI tools are appropriate for further study, especially as the technology dramatically increases its capabilities.

Keywords: energy storage; optimization; ChatGPT; BESS operation scheduling; artificial intelligence; energy market; balancing market (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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