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考虑蓄电池储能的配电网动态网络重构
引用本文:谢亮,李远卓,崔慧军,宋庆昌,罗欣,张志伟.考虑蓄电池储能的配电网动态网络重构[J].电力需求侧管理,2021,23(3):70-74.
作者姓名:谢亮  李远卓  崔慧军  宋庆昌  罗欣  张志伟
作者单位:国网冀北电力有限公司,北京 100085;北京清软创新科技股份有限公司,北京 100085
基金项目:国网冀北电力有限公司科技项目(52010119006U)
摘    要:随着分布式能源渗透率逐渐增高,用户侧负荷波动变大,电网很难一直保持其原有的较佳运行方式;而蓄电池储能技术的日渐成熟使其应用越来越广泛,削峰填谷的效益也被认可.考虑到蓄电池储能在配电网中的应用,会对未来配电网络进行动态重构.具体以网络损耗最小为目标函数,考虑网络拓扑约束、节点电压约束、支路电流约束和潮流平衡约束等,建立上层优化模型进行网络重构;以收益最大为目标函数,考虑储能充放电功率约束等,建立下层优化模型制定充放电策略;通过嵌入fmincon函数的遗传算法对双层优化模型进行求解;最后以修改的IEEE33节点系统作为算例,验证了所提方法的有效性.

关 键 词:电网结构  分布式能源  蓄电池储能  网络重构
收稿时间:2020/12/15 0:00:00
修稿时间:2021/2/17 0:00:00

Dynamic network reconfiguration of distribution network considering battery energy storage
XIE Liang,LI Yuanzhuo,CUI Huijun,SONG Qingchang,LUO Xin,ZHANG Zhiwe.Dynamic network reconfiguration of distribution network considering battery energy storage[J].Power Demand Side Management,2021,23(3):70-74.
Authors:XIE Liang  LI Yuanzhuo  CUI Huijun  SONG Qingchang  LUO Xin  ZHANG Zhiwe
Affiliation:State Grid North Hebei Electric Power Co., Ltd., Beijing 100085, China;Beijing Tsingsoft Technology Co., Ltd., Beijing 100085, China
Abstract:As the penetration rate of distributed energy increases gradually and the load fluctuation on the user side becomes larger, it is difficult for the power grid to maintain its original better operation mode all the time. With the maturity of battery energy storage technology, the application is extensive, and the benefits of peak cutting and valley filling are also recognized. The application of battery energy storage in distribution network is considered, and the future distribution network is dynamically reconstructed. Taking the minimum network loss as the objective function and considering the network topology constraint, node voltage constraint, branch current constraint and power flow balance constraint, an upper optimization model is established for network reconfiguration. Taking the maximum profit as the objective function and considering the energy storage charge and discharge power constraints, the lower optimization model is established to formulate the charge and discharge strategy. The two-layer optimization model is solved by the genetic algorithm embedded with fmincon function. Then a mathematical model is established. Genetic algorithm is solved by embedding graphminspantree function. Finally,a modified IEEE-33 node system is used as an example to verify the effectiveness of the proposed method.
Keywords:
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