电网技术
電網技術
전망기술
POWER SYSTEM TECHNOLOGY
2014年
4期
1032-1037
,共6页
配电网%网损期望值%无功优化%概率潮流%化学反应算法
配電網%網損期望值%無功優化%概率潮流%化學反應算法
배전망%망손기망치%무공우화%개솔조류%화학반응산법
distribution network%power loss expectation%reactive power optimization%probabilistic power flow%chemical reaction optimization
以有功网损期望值最小为优化目标，以节点电压的合格概率大于一定的阈值为约束条件，建立了同时考虑风能、太阳能分布式发电出力和负荷随机波动的配电网无功优化模型。目标函数和约束项中所涉及的概率潮流由一种结合传统解析法的基于全概率公式的计算方法求得。使用化学反应算法对所建优化模型进行求解。在同时接入风能、太阳能分布式电源的33节点和69节点系统上对所提方法进行了验证，得到了具有概率统计意义的最优方案。通过与包括遗传算法(genetic algorithm ， GA)、Stud GA(stud genetic algorithm)、生物地理学算法(biogeography based optimization ， BBO)和粒子群算法(particle swarm optimization，PSO)在内的多个智能算法对比，验证了所构建的化学反应算法在求解上述无功优化模型时性能更加稳定。
以有功網損期望值最小為優化目標，以節點電壓的閤格概率大于一定的閾值為約束條件，建立瞭同時攷慮風能、太暘能分佈式髮電齣力和負荷隨機波動的配電網無功優化模型。目標函數和約束項中所涉及的概率潮流由一種結閤傳統解析法的基于全概率公式的計算方法求得。使用化學反應算法對所建優化模型進行求解。在同時接入風能、太暘能分佈式電源的33節點和69節點繫統上對所提方法進行瞭驗證，得到瞭具有概率統計意義的最優方案。通過與包括遺傳算法(genetic algorithm ， GA)、Stud GA(stud genetic algorithm)、生物地理學算法(biogeography based optimization ， BBO)和粒子群算法(particle swarm optimization，PSO)在內的多箇智能算法對比，驗證瞭所構建的化學反應算法在求解上述無功優化模型時性能更加穩定。
이유공망손기망치최소위우화목표，이절점전압적합격개솔대우일정적역치위약속조건，건립료동시고필풍능、태양능분포식발전출력화부하수궤파동적배전망무공우화모형。목표함수화약속항중소섭급적개솔조류유일충결합전통해석법적기우전개솔공식적계산방법구득。사용화학반응산법대소건우화모형진행구해。재동시접입풍능、태양능분포식전원적33절점화69절점계통상대소제방법진행료험증，득도료구유개솔통계의의적최우방안。통과여포괄유전산법(genetic algorithm ， GA)、Stud GA(stud genetic algorithm)、생물지이학산법(biogeography based optimization ， BBO)화입자군산법(particle swarm optimization，PSO)재내적다개지능산법대비，험증료소구건적화학반응산법재구해상술무공우화모형시성능경가은정。
Taking the minimum expectation of active network loss as the optimization objective and the qualified probability of nodal voltage, which larger than a certain threshold, as the constraint, a reactive power optimization model of distribution network, in which the output fluctuation of distributed wind power generation and PV generation as well as the random fluctuation of load are considered simultaneously, is established. The probabilistic power flows involved in objective function and constraints are solved by a complete probability formula based computing method that combines with traditional analytical method. The established reactive power optimization model is solved by chemical reaction optimization (CRO). The proposed model is verified by IEEE 33-bus system and PG&E 69-bus system respectively, to which the distributed PV generation and wind power generation are simultaneously added, and an optimal scheme possessing the meaning of probability statistics is achieved. Comparing the constructed CRO algorithm with other intelligent algorithms, such as genetic algorithm (GA), stud genetic algorithm (Stud GA), biogeography based optimization (BBO) and particle swarm optimization (PSO), it is validated that the constructed CRO algorithm possesses more stable performance when it is used to solve above-mentioned reactive power optimization model.
