含风电场的电力系统机组组合问题随机模拟粒子群算法  

Particle Swarm Research of Stochastic Simulation for Unit Commitmen in Wind Farms Integrated Power System

作　　者：江岳文[1] 陈冲[1] 温步瀛[1] 

机构地区：[1]福州大学电气工程与自动化学院,福州350002

出　　处：《电工技术学报》2009年第6期129-137,共9页Transactions of China Electrotechnical Society

基　　金：福建省自然科学基金资助项目(2008J0018);福建省教育厅科技项目(JA07006)

摘　　要：由于风电具有随机性,含有风电场的机组组合问题不再是一个常规意义下的确定性问题。利用传统的方法也难获得既经济又有较高可靠性的解。本文建立了基于机会约束规划的含风电场的电力系统机组组合的数学模型,以概率的形式描述相关约束条件,并把组合问题分为内外两层优化子问题求解。外层为机组的启停状态优化,用离散粒子群算法求解,并引入启发式搜索策略,有效提高了机组状态优化效率;内层为负荷经济分配,考虑到风电的不可靠性,利用随机模拟的改进粒子群算法求解,防止种群过早收敛于局部最优解,并确保发电计划的可行性。通过10机系统的算例计算,并与其他文献方法比较,结果表明该算法对解决含有风电场的电力系统机组组合的问题是行之有效的。The unit commitment（UC） problem in wind power integrated system is not a traditional certain problem because of wind power＇s random. The solution which is economic and reliable is difficult to attain by traditional ways. This paper puts forwards a mathematical model of UC in wind power integrated system based on chance constrained programming （CCP）. It describes the related constrained conditions by probability form, and transforms UC problem into inside and outside optimization sub-problems. The outside sub-problem is units＇ on/off status optimization. It is effectively solved by discrete binary particle swarm optimization （BPSO） and heuristic searching strategy. The inside sub-problem is economic dispatch（ED）. Considering the random of wind power, the sub-problem is solved by improved particle swarm optimization （PSO） based on stochastic simulation which avoids local optimal solution and ensures the feasibility of generation plans. The optimization algorithm is proved feasible and effective by testing a 10-unit system and being compared with other methods.

关 键 词：风电场 机组组合 机会约束规划 粒子群算法 随机模拟技术 

分 类 号：TM732[电气工程—电力系统及自动化] TM614