风光联合优化配置的多目标机会约束规划方法  

Multiobjective chance-constrained programming method for wind generations and photovoltaic allocating

作　　者：夏澍[1] 顾劲岳[1] 葛晓琳[2] 钱耀兴[1] 

机构地区：[1]国网上海市电力公司市北供电公司,上海200072 [2]上海电力学院电气工程学院,上海200090

出　　处：《电力系统保护与控制》2016年第6期35-40,共6页Power System Protection and Control

基　　金：上海市青年科技英才扬帆计划(15YF1404600)

摘　　要：针对风速、太阳辐射、负荷的随机性和相关性,综合考虑成本、网损和电压质量,应用蒙特卡洛模拟法和机会约束规划法建立了风力发电机组和光伏方阵两种分布式可再生能源接入现有配电网的多目标优化配置模型。在蒙特卡洛法的基础上,提出了多区间划分、建立概率分布的方法,从而减少抽样次数。在求解模型过程中,首先利用多目标微分进化算法进行全局寻优,得到一组pareto最优解集,然后采用基于熵的模糊多属性决策方法选取折衷最优解。IEEE-33节点配电系统规划结果验证了模型的合理性和方法的有效性。A multi-objective planning scheme based on chance-constrained programming and Monte-Carlo method for wind generations and photovoltaic allocating is proposed to deal with the randomness and relevance of wind speed, solar radiation, and load. In the mathematic model, three indexes are introduced to evluate distributed generation profits, namely cost index, power loss index and voltage deviation index. Monte-Carlo method costs large computation time, therefore a method of established probability distribution based on multi-interval division is introduced to reduce the number of sampling. In the process of solving the model, the first multiobjective differential evolution is employed to get a set of pareto optimal solutions, then fuzzy multi-attribute decision making method based on information entropy is adopted to select the best compromise solution from the pareto optimal solutions. The case studies are carried out on the IEEE-33 nodes distribution network, and the results show that the prosed optimal model is rational, and the algorithm is effective.

关 键 词：风力发电机组 光伏方阵 优化配置 蒙特卡洛法 机会约束 多目标优化 

分 类 号：TM61[电气工程—电力系统及自动化] TM715