靳攀润, 宋汶秦, 刘永成. 考虑DG不确定性的有源配电网两阶段动态鲁棒重构方法[J]. 太阳能学报, 2024, 45(6): 208-216. DOI: 10.19912/j.0254-0096.tynxb.2023-0347
引用本文: 靳攀润, 宋汶秦, 刘永成. 考虑DG不确定性的有源配电网两阶段动态鲁棒重构方法[J]. 太阳能学报, 2024, 45(6): 208-216. DOI: 10.19912/j.0254-0096.tynxb.2023-0347
Jin Panrun, Song Wenqin, Liu Yongcheng. TWO-STAGE DYNAMIC ROBUST RECONFIGURATION METHOD OF ACTIVE DISTRIBUTION NETWORK CONSIDERING DG UNCERTAINTY[J]. Acta Energiae Solaris Sinica, 2024, 45(6): 208-216. DOI: 10.19912/j.0254-0096.tynxb.2023-0347
Citation: Jin Panrun, Song Wenqin, Liu Yongcheng. TWO-STAGE DYNAMIC ROBUST RECONFIGURATION METHOD OF ACTIVE DISTRIBUTION NETWORK CONSIDERING DG UNCERTAINTY[J]. Acta Energiae Solaris Sinica, 2024, 45(6): 208-216. DOI: 10.19912/j.0254-0096.tynxb.2023-0347

考虑DG不确定性的有源配电网两阶段动态鲁棒重构方法

TWO-STAGE DYNAMIC ROBUST RECONFIGURATION METHOD OF ACTIVE DISTRIBUTION NETWORK CONSIDERING DG UNCERTAINTY

  • 摘要: 针对分布式电源(DG)不确定性的有源配电网重构问题,提出两阶段动态鲁棒重构优化方法。在第一阶段进行辐射状配电网络开关重构优化,在第二阶段在考虑DG出力和负荷需求的不确定预算集下,进行含电池储能的有源配电网最优潮流优化。由于第二阶段优化问题含电池储能的充放电状态,采用嵌套列与约束生成算法将两阶段动态鲁棒重构模型转化为双层迭代求解。在4个不同规模的配电系统算例结果表明该文所提基于嵌套列与约束生成算法(CCG)的配电网动态鲁棒重构方法具有良好收敛性。

     

    Abstract: Aiming at the problem of active distribution network reconfiguration under DG uncertainty, a two-stage dynamic robust reconfiguration optimization method is proposed. In the first stage, the switch reconfiguration optimization of radial distribution network is carried out. In the second stage, the optimal power flow optimization of active distribution network with battery energy storage is carried out considering the uncertain budget set of DG output and load demand. Because the second stage optimization problem includes the charging and discharging state of battery energy storage, the nested column and constraint generation algorithm is used to transform the two-stage dynamic robust reconfiguration model into a bilevel iterative solution. The results of four distribution systems with different scales show that the proposed dynamic robust reconfiguration method of distribution network based on nested CCG has good convergence.

     

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