Abstract
In this letter, the evolutionary game theory (EGT) with replication dynamic equations (RDEs) is adopted to explicitly determine the factors affecting energy providers (EPs) willingness of using the market power to uplift the price in the bidding procedure, which could be simulated using the win-or-learn-fast policy hill climbing (WoLF-PHC) algorithm as a multi-agent reinforcement learning (MARL) method. Firstly, empirical and numerical connections between WoLF-PHC and RDEs is proved. Then, by formulating RDEs of the bidding procedure, three factors affecting the bidding strategy preference are revealed, including the load demand, severity of congestion, and the price cap. Finally, the impact of these factors on the converged bidding price is demonstrated in case studies, by simulating the bidding procedure driven by WoLF-PHC.
Original language | English |
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Pages (from-to) | 5975-5978 |
Number of pages | 4 |
Journal | IEEE Transactions on Power Systems |
Volume | 36 |
Issue number | 6 |
DOIs | |
Publication status | Published - 1 Nov 2021 |
Keywords
- bidding strategy
- evolutionary game theory
- Games
- Generators
- Heuristic algorithms
- market power
- multi-agent reinforcement learning
- Power system dynamics
- Reinforcement learning
- Stability criteria
- Stakeholders
- Market power
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering