Guoqiang Sun, Jinghong Sun, Sheng Chen, et al. Market-Based Coordination of Regional Electric and Natural Gas Systems: Peer-to-Peer Energy Trading Model[J]. CSEE Journal of Power and Energy Systems, 2025, 11(3): 1370-1379.
DOI:
Guoqiang Sun, Jinghong Sun, Sheng Chen, et al. Market-Based Coordination of Regional Electric and Natural Gas Systems: Peer-to-Peer Energy Trading Model[J]. CSEE Journal of Power and Energy Systems, 2025, 11(3): 1370-1379.DOI: 10.17775/CSEEJPES.2021.03970.
Market-Based Coordination of Regional Electric and Natural Gas Systems: Peer-to-Peer Energy Trading Model
摘要
Abstract
The increasing penetration of distributed energy sources in electric power and natural gas distribution networks in recent years has led to a shift from the conventional centralized trading mode to decentralized trading. However
current decentralized energy trading developments
such as those based on the peer-to-peer (P2P) paradigm
have mainly focused on electric power grids
while regional integrated energy systems coupled by multiple energy sources are currently restricted to centralized scheduling generally. This present work addresses this issue by developing a P2P market transaction model for regional integrated energy systems. First
the privacy of each participating agent is preserved by conducting a trade matching process between producers and consumers
and the P2P trading strategy is constructed. Then
the feasibility of the obtained trading strategy is confirmed by the operation center of the regional integrated energy system by conducting collaborative optimization scheduling based on the P2P energy trading results. Moreover
the locational marginal price is employed to calculate the P2P network usage charge. This charge is then equally distributed between producers and consumers to incentivize balanced utilization of the distribution network and ensure equitable benefits from P2P transactions. The effectiveness and superiority of the proposed P2P trading framework are demonstrated based on numerical results obtained for realistic regional integrated electric and natural gas systems.
Real-Time Dispatch via Expert Knowledge Driven Deep Reinforcement Learning
Multi-Objective Optimization for Assessing Tradeoffs Between Energy Sharing and Ancillary Services in Local Energy Communities
Pricing Strategy for Regional Integrated Energy System Considering Privacy Based on Deep Reinforcement Learning
Distributionally Robust Optimization Based Restoration for Integrated Electricity-Gas Systems Incorporating Wind Power and Flexible Reserve Capacity Allocations
Multiple Renewable Short-Circuit Ratio for Assessing Weak System Strength with Inverter-Based Resources
Related Author
Liangcheng Cheng
Jiahao Yan
Jianguo Yao
Shengchun Yang
Wenbo Mao
Kedong Zhu
Pierluigi Siano
Mohammad Dolatabadi
Related Institution
China Electric Power Research Institute
Department of Management and Innovation Systems, University of Salerno, Fisciano
National University of Science and Technology Politehnica Bucharest, Bucharest
School of Engineering, Massachusetts Institute of Technology (MIT)
Department of Electrical, Electronic, and Information Engineering, University of Bologna