引用本文: | 李诗颖,杨晓辉.基于双向动态重构与集群划分的光伏储能选址定容[J].电力系统保护与控制,2022,50(3):51-58.[点击复制] |
LI Shiying,YANG Xiaohui.Capacity and location optimization of photovoltaic and energy storage based on bidirectional dynamic reconfiguration and cluster division[J].Power System Protection and Control,2022,50(3):51-58[点击复制] |
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摘要: |
为研究网络重构与集群划分对提升配电网中分布式电源规划配置水平的可能性,提出一种动态重构与集群划分的双层划分模型,同时获得最佳重构策略和集群划分方式。通过4种方案进行DPV、ESS选址定容实验,对比DPV接入容量、年综合成本等规划指标及网络损耗、电压水平等系统运行指标。结果表明,在基于该双层划分结果的DPV、ESS选址定容方案下,DPV接入容量最大且系统年综合成本最低。经算例验证,分时段双向动态重构策略可在降低规划成本的同时大幅度拉伸DPV消纳水平的提升空间,在解决高比例分布式电源规划类问题时具有较高的参考价值。 |
关键词: 分布式电源 选址定容 光伏消纳 动态重构 集群划分 |
DOI:DOI: 10.19783/j.cnki.pspc.210385 |
投稿时间:2021-04-10修订日期:2021-09-06 |
基金项目:国家自然科学基金项目资助(61773051,61963026) |
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Capacity and location optimization of photovoltaic and energy storage based on bidirectional dynamic reconfiguration and cluster division |
LI Shiying,YANG Xiaohui |
(1. State Grid Jiangxi Nanchang Power Supply Company, Nanchang 330069, China;
2. Nanchang University, Nanchang 330031, China) |
Abstract: |
In order to study the possibility of network reconfiguration and cluster division to improve distributed generation configuration in the distribution network, a two-layer model of dynamic reconfiguration and cluster division is proposed, which can obtain the best reconfiguration strategy and cluster division at the same time. The distributed photovoltaic (DPV) and energy storage system (ESS) capacity and location optimization experiments are carried out through 4 schemes, the optimization indicators such as DPV capacity and annual overall cost and operation indicators such as network loss and voltage level are compared. The results show that under the DPV and ESS capacity and location optimization scheme based on the two-layer division result, the DPV capacity is the largest and the annual overall cost is the lowest. The examples show that the bidirectional dynamic reconfiguration strategy can greatly expand the room for improvement of DPV consumption while reducing planning costs. It has high reference value when solving high-proportion distributed power optimization problems.
This work is supported by the National Natural Science Foundation of China (No. 61773051 and No. 61963026). |
Key words: distributed generation capacity and location optimization photovoltaic consumption dynamic reconfiguration cluster division |