网架重构后期的负荷恢复优化  

Load Restoration Optimization During Last Stage of Network Reconfiguration

作　　者：瞿寒冰[1] 刘玉田[1] 

机构地区：[1]山东大学电气工程学院,山东省济南市250061

出　　处：《电力系统自动化》2011年第19期43-48,共6页Automation of Electric Power Systems

基　　金：国家自然科学基金资助项目(50877044)~~

摘　　要：大停电后网架重构的最后阶段,负荷的全面快速恢复应在满足约束的前提下分阶段顺序进行,是一个多约束、非线性的整数规划问题。文中建立了考虑多负荷点投入顺序的组合优化模型,在目标函数中计及了负荷投入顺序的影响,综合考虑了不同顺序下频率、电压、机组出力限值及稳态潮流等多个约束条件,并提出一种校验暂态电压下降约束的简化方法。由于网络重构后期待恢复负荷点的优化范围较大,采用一种自适应粒子群算法对模型进行求解,通过分析个体极值的优劣实现优化过程中参数的自适应调整。山东电网仿真结果表明,上述模型能有效反映负荷的投入顺序,且算法的优化速度能满足实际工程要求。During the last stage of network reconfiguration after a major blackout, the quick all-round load restoration with the constraints satisfied by stages in proper order is a multi-constraint, non-linear, and integer programming problem. A combinatorial optimization model is developed with the sequencing problem o[ load pickup taken into account. The effects o[ pickup sequence are taken into account in the objective function. Multiple constraint conditions such as the frequency, voltage, unit output limit and power flow in different sequences are comprehensively considered. In addition, a method for simplifying transient voltage dip constraint check is presented. Owing to the rather large optimization range of the to-be-restored loads in the later stage of network reconfiguration, a new adaptive particle swarm optimization algorithm is adopted to solve the model proposed. The adaptive adjustment of parameters is carried out by comparing the quality of individual extrema in the iterativc process. Case studies performed on the actual Shandong power system demonstrate that the model proposed can effectively reflect the sequencing problem and the algorithm adopted can satisfy the practical requirements on computation speed.

关 键 词：负荷恢复 电力系统恢复 排序 组合优化 自适应粒子群算法 

分 类 号：TM714[电气工程—电力系统及自动化]