Nash Equilibrium Estimation and Analysis in Joint Peer-to-Peer Electricity and Carbon Emission Auction Market With Microgrid Prosumers

Ziqing Zhu, Ka Wing Chan*, Siqi Bu, Bin Zhou, Shiwei Xia

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

2 Citations (Scopus)

Abstract

The joint Peer-to-Peer (P2P) electricity market (EM) and carbon emission auction market (CEAM) among prosumer microgrids (MGs) in the distribution network is a promising paradigm to facilitate the participation of distributed energy resources (DERs) and incentivize the decarbonization. In this market, MGs will modify their bidding strategies to be adaptive to other rival MGs' for profit maximization. Such modification will converge to the Nash Equilibrium Point (NEP), where each MG cannot obtain more profits by modifying its strategy subject to the fixed strategy of other rival MGs. In this paper, the NEP under such a joint market paradigm is investigated, in which MGs will trade electricity in the EM and purchase carbon emission quotas (CEQs) in the CEAM. In addition, MGs must adjust their bidding strategies considering penalties due to deviations between day-ahead (DA) scheduling and real-time (RT) procurement caused by uncertainties of net load, as well as the price fluctuation in the CEAM. The NEP is estimated by a novel Multi-agent Deep Deterministic Policy Gradient (MADDPG) algorithm, and the risk mitigation is achieved by incorporating the conditional value-at-risk (CVaR) constraint. The computational performance and effectiveness of risk mitigation of this proposed algorithm, and the obtained NEP in the joint EM and CEAM, are analyzed in the case studies.

Original languageEnglish
Pages (from-to)5768-5780
Number of pages13
JournalIEEE Transactions on Power Systems
Volume38
Issue number6
Early online date30 Nov 2022
DOIs
Publication statusPublished - Nov 2023

Scopus Subject Areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

User-Defined Keywords

  • Bidding strategy
  • conditional value-at-risk
  • microgrid
  • multi-agent reinforcement learning
  • P2P energy trading

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