Optimization Method for Cluster-Grid Cooperative Interaction for Large-scale Distributed Renewable Energy Integration

被引：0
作者：
Chen, Tingwei [1 ]
Zeng, Jun [1 ]
Zhang, Xuan [1 ,3 ]
Zhao, Ziyu [1 ]
Huang, Xiangmin [1 ]
Liu, Junfeng [2 ]
机构：
[1] School of Electric Power Engineering, South China University of Technology, Guangzhou
[2] School of Automation Science and Engineering, South China University of Technology, Guangzhou
[3] Beijing Branch of China Southern Power Grid Company Limited, Beijing
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2024年 / 48卷 / 15期
基金：
中国国家自然科学基金;
关键词：
adjustable power range; all-scenario feasibility; cluster-grid cooperative interaction; distributed optimization; potential game;
D O I：
10.7500/AEPS20231016004
中图分类号：
学科分类号：
摘要：
The stable operation of the power grid with large-scale distributed renewable energy integration is an important research direction for optimizing the operation of the power grid under the new situation of low-carbon transformation. In response to the multi-agent characteristics and randomness issues brought about by the participation of the clusters formed by distributed renewable energy aggregation in optimizing power grid operation, an optimization method for cluster-grid cooperative interaction for large-scale distributed renewable energy integration is proposed. First, by constructing a high-dimensional inscribed rectangle in the constraint space of flexible resources, an adjustable power range is formed to represent the flexible adjustment range. By constructing a flexible constraint of adjustable power domain to fully cover the fluctuation range of renewable energy clusters, the optimization results are feasible in all scenarios within the random fluctuation range of renewable energy clusters. Then, based on the self-interested and autonomous characteristics of multi-agents in power grid operation, an optimization model of potential game cluster-grid cooperative interaction is constructed to meet the requirements of parallel optimization for homogeneous individuals and serial optimization for non-homogeneous individuals. A distributed optimization method and its process suitable for the model are proposed. Finally, simulation cases show that the proposed method effectively achieves multi-agent collaborative interaction optimization and ensures the feasibility of the optimization results in all scenarios. This work is supported by National Natural Science Foundation of China (No. 62173148, No. 52377186) and Guangdong Provincial Natural Science Foundation of China (No. 2022A1515010150, No. 2023A1515010184). © 2024 Automation of Electric Power Systems Press. All rights reserved.
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页码：73 / 83
页数：10
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