为提高带有风电、光伏发电和储能单元的多区域互联经济调度问题求解精度,加强各区域的隐私保护能力,提出一种分布式多策略粒子群优化算法。首先,为提高粒子群优化算法的全局搜索能力,利用竞争机制将种群分区,根据分区使用多策略方案,增强算法多样性。其次,构建带有风电、光伏发电和储能单元的多区域互联经济调度模型,各区域之间通过边界联络线传输信息,然后提出一种分布式方法应用于多策略粒子群优化算法中,各子算法分别求解与各区域相关的子问题。最后,将IEEE39节点系统分为两区域与三区域互联系统,并在修改后的IEEE39节点系统上验证算法性能,通过与其他3种优化算法进行对比分析,实验结果验证了基于竞争分区的粒子群优化算法的有效性和分布式方法的可行性。
Abstract
This paper proposes a distributed multi-strategy particle swarm optimization algorithm to improve the solution accuracy of solving the multi-area interconnected economic dispatch problem involving wind power, photovoltaic power generation, and energy storage units. Additionally, the proposed approach enhances the privacy protection capability among areas. Firstly, to improve the global search ability of the particle swarm optimization algorithm, the population is partitioned using a competition mechanism. Then, different strategies are applied according to the partition results to enhance the algorithm diversity. Secondly, a multi-area interconnected economic dispatch model with wind power, photovoltaic power generation and energy storage units is constructed, where information is transmitted between areas through tie-lines, and then a distributed method is proposed to be applied in a multi-strategy particle swarm optimization algorithm, where each sub-algorithm solves the sub-problems related to each area separately. Finally, the IEEE39-bus system is divided into two-area and three-area interconnected systems, and the performance of the algorithm is verified on the modified IEEE 39-bus system, and the experimental results verify the effectiveness of the particle swarm optimization algorithm based on competitive partitioning and the feasibility of the distributed approach by comparing and analyzing the algorithms with the other three optimization algorithms.
关键词
互联电力系统 /
调度 /
粒子群优化 /
分布式优化 /
竞争机制 /
联络线约束处理
Key words
electric power system interconnection /
scheduling /
particle swarm optimization /
distributed optimization /
competitive mechanism /
tie line constraint handling
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基金
国家自然科学基金（62273133）; 河南省自然科学基金杰出青年基金（242300421053）; 河南省科技攻关项目（242102210036）; 河南省高校基本科研业务费项目（NSFRF240608）
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