Analysis of Evolutionary Dynamics for Bidding Strategy Driven by Multi-Agent Reinforcement Learning

Ziqing Zhu, Ka Wing Chan, Siqi Bu, Siu Wing Or, Xiang Gao, Shiwei Xia

Research output: Journal article publicationJournal articleAcademic researchpeer-review

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 languageEnglish
JournalIEEE Transactions on Power Systems
DOIs
Publication statusAccepted/In press - 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

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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