| 引用本文: | 闫群民,董新洲,穆佳豪,马永翔.基于改进多目标粒子群算法的有源配电网储能优化配置[J].电力系统保护与控制,2022,50(10):12-19.[点击复制] |
| YAN Qunmin,DONG Xinzhou,MU Jiahao,MA Yongxiang.Optimal configuration of energy storage in an active distribution network based onimproved multi-objective particle swarm optimization[J].Power System Protection and Control,2022,50(10):12-19[点击复制] |
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| 摘要: |
| 储能系统可解决分布式电源加入配电网所产生的不良影响，而储能系统的合理配置是其有效应用的前提。以电网脆弱性衡量指标、有功网损、储能额定容量三个方面，考虑规划与运行之间的耦合性建立储能系统在有源配电网中的多目标选址定容模型。提出改进的多目标粒子群算法用于求解。该算法在种群更新过程中引入准对立学习策略以增强解的覆盖范围和收敛速度，并根据迭代次数采用自适应分裂策略分离过早聚集的粒子，从而增强粒子多样性，保证了算法跳出局部最优的能力。通过在IEEE33节点配电系统上进行分析，验证了所提模型及算法在优化分布式储能选址定容及运行策略中的合理性，并能有效改善电网的运行经济性与脆弱性，具有更强的全局寻优能力。 |
| 关键词: 储能系统 优化配置 有源配电网 电网脆弱性 多目标粒子群算法 |
| DOI：DOI: 10.19783/j.cnki.pspc.211106 |
| 投稿时间：2021-08-16修订日期：2021-11-07 |
| 基金项目:国家重点研发计划项目资助(2016YFB0900600);陕西省教育厅重点科学研究计划项目资助(20JS018) |
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| Optimal configuration of energy storage in an active distribution network based onimproved multi-objective particle swarm optimization |
| YAN Qunmin,DONG Xinzhou,MU Jiahao,MA Yongxiang |
| (1. College of Electrical Engineering, Shaanxi University of Technology, Hanzhong 723001, China;
2. Department of Electrical Engineering, Tsinghua University, Beijing 100084, China) |
| Abstract: |
| The use of energy storage systems (ESS) can guard against many hazards caused by distributed power sources joining the distribution network, and the reasonable configuration of ESS is a prerequisite for their effective application. In this paper, considering the coupling between planning and operation, a multi-objective site selection and capacity model for ESS in a distribution network with distributed generation (DG) is established from three aspects: grid vulnerability measurement indicators, active power loss, and ESS-rated capacity. A reformative multi-objective particle swarm arithmetic is devised. The arithmetic introduces a quasi-adversarial learning strategy in the population update process to enhance the coverage and convergence rate of the solution, and adopts an adaptive split strategy to separate prematurely clustered particles according to the number of iterations, thereby enhancing the diversity of particles. This has the ability to escape the local optimum while ensuring astringency. Through analysis on the IEEE-33 node power distribution system, the rationality of the proposed model and algorithm in optimizing the location and capacity and operational strategy of distributed energy storage is verified, and it can effectively improve the operating economy and vulnerability of a power grid with a stronger global search capability.
This work is supported by the National Key Research and Development Program of China (No. 2016YFB0900600). |
| Key words: energy storage system optimal configuration active distribution network grid vulnerability multi-objective particle swarm optimization |
