학술논문

Collaborative Optimal Dispatch of Multi-Agent Distributed Integrated Energy System Based on Game Theory
Document Type
Conference
Source
2023 IEEE 7th Conference on Energy Internet and Energy System Integration (EI2) Energy Internet and Energy System Integration (EI2), 2023 IEEE 7th Conference on. :888-894 Dec, 2023
Subject
Power, Energy and Industry Applications
Privacy
Electricity
Collaboration
Games
Pricing
Optimal scheduling
System integration
distributed integrated energy system
multi-agent collaborative optimization
game mechanics
Language
Abstract
The escalating demands of energy consumption and environmental conservation necessitate urgent advancements in energy infrastructures. In this context, Deep Integration of Multi-energy Systems (DIMS), which concentrates on the seamless integration of advanced multi-energy and information technologies, is considered the most promising strategy for resilient energy utilization in future societies. In a setting where multiple entities participate across source-network-load-storage links, achieving a balanced equilibrium that maximizes collective benefits is crucial. It therefore becomes vital to create a Distributed Integrated Energy System (DIES) operation strategy that aligns with an open energy market. To address this, we first introduce an Integrated Energy Management System (IEMS) to coordinate internal energy transactions. We propose a distributed hierarchical game architecture, taking bounded rationality into account. Under this mechanism, the upper IEMS modulates the electricity price strategy, while the lower entities achieve autonomous optimization scheduling through multi-energy collaboration and Synergy. These entities then engage in multi-agent cooperative optimization as prosumers. Furthermore, we employ the game mechanism to leverage load discrepancies, flexible role switching, and demand response, thereby effectively improving the load curve. To validate the effectiveness of our method, we employ a comparative analysis through a simulation on a DIES comprising of an IEEE 33-node network and an optimized 7-node natural gas network. This demonstrates the superiority of our proposed method.