摘要
随着风电场装机容量的增加,风电场并网对电网的影响越来越大,因此对风电并网后电力系统的不确定分析显得尤为重要。首先将随机响应面法(SRSM)应用到风电并网后电力系统的不确定分析中,并利用该方法建立了含风电场的电力系统概率潮流计算模型。然后将基于个体最优位置自适应变异扰动粒子群算法与前述概率计算模型相结合,建立了以系统有功网损期望值、节点电压越限概率为优化目标的多目标无功优化模型。接着以风电场接入IEEE14节点标准测试系统为例,根据SRSM计算出节点电压累积分布,与蒙特卡洛法进行比较,算例结果表明随机响应面法具有较高的效率和精度,证实了SRSM的有效性。最后将该无功优化模型应用于IEEE14节点标准测试系统进行仿真分析,证明了基于个体最优位置自适应变异扰动粒子群算法相对于常规改进粒子群算法(IWPSO)而言,能够有效地避免早熟收敛。
With the increasing of installed capacity, wind farm that incorporates into power network has great influence on power system. The uncertainty analysis of wind farm incorporating into power network becomes more important. Firstly, the SRSM is applied to the uncertainty analysis. A probabilistic load flow model considering the wind farm is built with SRSM. Secondly, combining the AMDPSO with the probabilistic load flow model, a multi-objective reactive power optimization model considering the expectation value of active power losses and bus voltage beyond limits probability is built. Then, the simulation is conducted for IEEE-14 bus system integrated with wind farms. And the paper calculates bus voltage cumulative distribution by SRSM and Monte Carlo. The results show that SRSM has a high precision and efficiency. At last, a multi-objective reactive power optimization model applied to IEEE-14 bus system is simulated. The experiment proves that AMDPSO can avoid stagnation effectively compared with IWPSO.
出处
《电力系统保护与控制》
EI
CSCD
北大核心
2013年第1期197-203,共7页
Power System Protection and Control
基金
国家自然科学基金(U1134205
51007074)
教育部新世纪优秀人才支持计划项目(NECT-08-0825)
中央高校基本科研业务费专项资金资助项目(SWJTU11CX141)~~
关键词
随机响应面法
概率潮流
自适应变异
粒子群算法
无功优化
SRSM
probabilistic load flow
adaptive mutation
particle swarm optimization
reactive power optimization
