考虑多个风电机组接入配电网的多目标无功优化  

Multi-Objective Optimal Reactive Power Flow of Distribution Network With Multiple Wind Turbines

作　　者：汪文达[1] 崔雪[1] 马兴[1] 汪颖翔[1] 刘会金[1] 

机构地区：[1]武汉大学电气工程学院,湖北省武汉市430072

出　　处：《电网技术》2015年第7期1860-1865,共6页Power System Technology

摘　　要：针对多个风电机组接入配电网带来的不确定性问题,采用基于拉丁超立方采样的Monte Carlo概率潮流计算方法(correlation Latin hypercube sampling Monte Carlo simulation,CLMCS)以及场景缩减技术得到风机组输出功率的典型场景,将不确定性问题转化为单场景确定性潮流问题。并建立以有功网损最小、电压偏差最小作为目标函数的配电网无功优化数学模型。采用e正交多目标差分进化算法(e-orthogonal differential evolution multi-objective algorithm,e-ODEMO)进行计算得到非劣解集,该算法基于一般差分演化算法,结合正交实验方法使初始个体均匀分布在决策变量空间,利用e占优技术对Archive群体进行更新,能得到均匀分布的非劣解集。应用IEEE 33节点以及PG&E 69节点配电网系统进行了测试,结果验证了所提方法和模型的可行性与有效性。In allusion to the uncertainty caused by integration of multiple wind turbines to distribution network, correlation Latin hypercube sampling Monte Carlo simulation and scenario reduction technique are applied to get the typical scenario of wind turbines, then the uncertainty problem is turned into a single scene deterministic load flow problem. Taking minimum active power loss and minimum voltage deviation as objective functions, a new multi-objective reactive power optimization mathematical model is built. ε-orthogonal differential evolution multi-objective algorithm is adopted to calculate Pareto. Based on general differential evolution algorithm, the modified algorithm combines with orthogonal experiment method which enables initial individual to evenly distribute in decision variable space, and uses ε-dominant technology to update Archive groups, thus evenly distributed Pareto can be obtained. Simulation of IEEE 33-bus and PGE69-bus distribution systems is carried out, and results prove the feasibility and effectiveness of the proposed method and model.

关 键 词：风电机组 场景缩减 e占优技术 无功优化 e正交多目标差分进化算法 

分 类 号：TM744[电气工程—电力系统及自动化]