含风电场机组组合的模糊建模和优化

Fuzzy Modeling and Optimization Based on Unit Commitment of a Power System Intergrated with Wind Power

环境保护的兴起和传统化石能源的日益枯竭增加了人们将可再生能源融入现有系统的兴趣。风电是最有前途的可再生能源之一。但由于风力发电的间歇性和不可预测性使得控制发电频率和实际调度变得困难,在含可再生能源的混合电力系统中存在许多问题。针对风电场出力的间歇性与不可预测性,应用模糊理论建立了含风电场的机组组合模型,综合考虑了一次能源的消耗与风电加入给系统带来的风险。通过定义隶属度函数将确定性问题模糊化,采用最大化满意度指标法将问题转化为非线性规划问题,并采用遗传算法求解该优化问题。对一个10机算例进行了仿真,结果表明本文的算法可行,且能根据决策者的意愿在一次能源的消耗与风险之间折中,为运行和计划提供了宝贵的信息。

With the drive of environmental protection and the trend of progressive exhaustion of traditional fossil energy sources, the interest is increased on integrating renewable energy sources into existing power systems. Among various renewable energy sources, wind energy is one of the most promising renewable energy sources. However, the intermitteney and unpredictability of the wind power generation poses difficulty in control of frequency and scheduling of generation, which causes problems in the renewable-energy based hybrid power system. In views of this point, a fuzzy unit commitment model including wind farms is proposed. Primary energy emission and the risk are synthetically considered in the paper. Through defining the membership function, the deterministic problem is transformed into a fuzzy problem. Then it is reformulated into a nonlinear problem by means of the fuzzy satisfaction maximum-minimum. Genetic algorithm （GA） is used to solve this optimization problem. The simulation results of a 10 unit system demonstrate the feasibility of the proposed method. It can compromise the primary energy consumption with the risk according to the decision-maker＇s will. This paper provides some valuable information in both operation and planning in future.