TY - JOUR
T1 - Analysis of strategic interactions among distributed virtual alliances in electricity and carbon emission auction markets using risk-averse multi-agent reinforcement learning
AU - Zhu, Ziqing
AU - Chan, Ka Wing
AU - Bu, Siqi
AU - Or, Siu Wing
AU - Xia, Shiwei
N1 - Funding Information:
This work is jointly supported by the Research Grants Council of the HKSAR Government (Grant No. R5020-18 ), the Innovation and Technology Commission of the HKSAR Government to the Hong Kong Branch of National Rail Transit Electrification and Automation Engineering Technology Research Center (Grant No. K-BBY1 ), The Hong Kong Polytechnic University and the National Natural Science Foundation of China ( 52077075 ).
Publisher Copyright:
© 2023
PY - 2023/9
Y1 - 2023/9
N2 - The incorporation of carbon emission auction market (CEAM) and ancillary service market (ASM) is an emerging trading paradigm in active distribution network (ADN). Such regime not only promotes the elimination of carbon emission, but also facilitates the secure operation of power network, especially considering the participation of distributed virtual alliances (DVAs) consisting of renewable distributed generators (RDGs) with uncertain output. In this research, a bi-level bidding and market clearing dynamic programming model is developed for in-depth analysis of market participants’ bidding strategies and market equilibrium. This model allows DVAs to modify their bidding strategies in the energy market (EM), ASM and CEAM based on the market clearing results and uncertainty of RDG output. Also, a new Meta-Learning based Win-or-Learn-Fast (MLWoLF-PHC) algorithm, which not only enables the fully distributed bidding strategy modification, but also performs well considering uncertainty as a risk-averse method, is proposed to solve this model. Its computational performance, the market equilibrium analysis, and the impact of CEAM on the converged market clearing price of EM and ASM would be thoroughly investigated and examined in the case studies.
AB - The incorporation of carbon emission auction market (CEAM) and ancillary service market (ASM) is an emerging trading paradigm in active distribution network (ADN). Such regime not only promotes the elimination of carbon emission, but also facilitates the secure operation of power network, especially considering the participation of distributed virtual alliances (DVAs) consisting of renewable distributed generators (RDGs) with uncertain output. In this research, a bi-level bidding and market clearing dynamic programming model is developed for in-depth analysis of market participants’ bidding strategies and market equilibrium. This model allows DVAs to modify their bidding strategies in the energy market (EM), ASM and CEAM based on the market clearing results and uncertainty of RDG output. Also, a new Meta-Learning based Win-or-Learn-Fast (MLWoLF-PHC) algorithm, which not only enables the fully distributed bidding strategy modification, but also performs well considering uncertainty as a risk-averse method, is proposed to solve this model. Its computational performance, the market equilibrium analysis, and the impact of CEAM on the converged market clearing price of EM and ASM would be thoroughly investigated and examined in the case studies.
KW - Ancillary service market
KW - Carbon emission auction market
KW - Distributed network market
KW - Distributed virtual alliances
KW - Multi-agent reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85163398682&partnerID=8YFLogxK
U2 - 10.1016/j.rser.2023.113466
DO - 10.1016/j.rser.2023.113466
M3 - Journal article
AN - SCOPUS:85163398682
SN - 1364-0321
VL - 183
JO - Renewable and Sustainable Energy Reviews
JF - Renewable and Sustainable Energy Reviews
M1 - 113466
ER -