Coordinated Optimization of Mixed Microgrid Multi-agent Game Considering Multi-energy Coupled Shared Energy Storage

被引：0
|
作者：
Lin M. [1 ]
Liu J. [1 ]
Tang Z. [1 ]
Zeng P. [1 ]
Jiang B. [1 ]
Ma G. [1 ]
机构：
[1] School of Automation, Hangzhou Dianzi University, Hangzhou
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2024年 / 48卷 / 04期
基金：
中国国家自然科学基金;
关键词：
combined cooling; heating and power; integrated energy system; mixed game; pricing mechanism; shared energy storage;
D O I：
10.7500/AEPS20230920003
中图分类号：
学科分类号：
摘要：
Shared energy storage with cooling, heating and power coupling can more significantly improve the utilization efficiency and accommodation level of multi-energy sources in microgrids. However, the multi-energy coupled shared energy storage further increases the difficulty of coordinated operation among multi-microgrids. Therefore, a coordinated optimization model of mixed microgrid multi-agent game is proposed considering multi-energy coupled shared energy storage. First, the model is carried out around the multi-energy coupled shared energy storage, and the operation model of the components is constructed from three dimensions of cooling, heating and power. Then, a two-layer coordinated scheduling model of mixed microgrid game is proposed considering the collaboration of multi-energy coupled shared energy storage. The upper layer is an intraday electricity price optimization model for microgrid operators based on Stackelberg game, which aims at maximizing the microgrid operators’ intraday revenue, and provides a responsive electricity price for the lower collaborative operation with the comprehensive consideration of the upper and lower constraints on the electricity price and the constraints on the average of the purchase and sale prices. The lower layer is a cooperative operation strategy model based on cooperation game, which takes the cooperation cost of producers and sellers and shared energy storage as the goal, comprehensively considers the upper and lower limits of unit output constraints and power balance constraints, and provides feedback cooperation cost for the upper layer to guide the optimization and adjustment of electricity price. Then, the change rate of electricity price is used as the convergence criterion, and the bifurcation method is used to deal with the constraints of electricity price to accelerate the model solution in the mixed game model solution. Finally, the effectiveness of the proposed method is verified by using a microgrid system with the multi-agent coordinated optimization. © 2024 Automation of Electric Power Systems Press. All rights reserved.
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页码：132 / 141
页数：9
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