摘要
考虑到负荷和风力发电机组(wind turbine generator,WTG)之间存在定的负相关性,文章首先采用拉丁超立方采样(Latin hypercube sampling,LHS)技术和Cholesky分解法排序产生相关性样本,然后以规划期内总成本小为目标,建立了多阶段WTG选址定容机会约束规划模型,并采用模糊自适应遗传算法(fuzzy adaptive genetic algorithm,FAGA)进行求解。在FAGA中,设计了种新的模糊逻辑控制器,使得算法在迭代过程中能够动态调整控制参数,增加算法对解空间的搜索能力,从而克服了基本遗传算法容易陷入局部优、收敛速度慢等缺点。33节点配电网算例的仿真分析表明,在进行WTG选址定容规划时不能忽略负荷和WTG之间的相关性。同时,算例仿真结果也验证了FAGA在求解规划模型时的高效性。
Considering the existence of a certain negative correlation between load demand and wind turbine generator （WTG）, firstly the correlated samples come into being by Latin hypercube sampling （LHS） technique and Cholesky decomposition approach; then taking the minimum total cost during the planning period as the objective, a multistage chance t constraint planning model for siting and sizing of WTGs is established and solved by fuzzy adaptive genetic algorithm （FAGA）. In FAGA, a novel fuzzy logic controller is designed to make parameters of the algorithm enable to be dynamically adjusted, thus the search capability of FAGA in the solution space can be enhanced and such shortcomings of basic genetic algorithm as easy to fall into local optimum and slow convergence speed can be overcome. Simulation results of a 33-bus distribution network show that the correlation between load and WTGs should not be ignored during drafting the planning of siting and sizing of WTGs. Meanwhile, the high-efficiency of FAGA in solving planning model is validated by simulation results of test distribution network.
出处
《电网技术》
EI
CSCD
北大核心
2014年第1期53-59,共7页
Power System Technology
基金
国家重点基础研究发展计划资助项目(973项目)(2009CB219703)
国家自然科学基金资助项目(51261130473)~~
关键词
配电网
风力发电机组
多阶段
选址定容
相关性
模糊自适应遗传算法
distribution network
wind turbine generator
multistage
siting and sizing
correlation
fuzzy adaptivegenetic algorithm
