考虑风电和电动汽车不确定性的综合能源系统日前经济调度

综合能源系统中风力发电和电动汽车充放电的不确定性对系统运行的经济性有较大影响。考虑电动汽车的普及对综合能源系统的影响,根据电动汽车的不同出行和充电特性,细化区分各类电动汽车的运行模型;考虑风电出力的分布概率较难获取,通过较多历史数据驱动来构造一个基于不精确狄利克雷模型(imprecise Dirichlet model, IDM)的非参数模糊集,从而搭建一个考虑风电出力和电动汽车充放电不确定性的综合能源系统日前两阶段分布鲁棒调度模型。为了获得该模型的最优解,将其分解为一个混合整数线性规划主问题和一个具有max-min结构的子问题。利用对偶理论和大M方法,将子问题转化为MILP问题,利用列和约束生成(column and constration generation, CC&G)算法对主问题和转化后的子问题进行迭代求得最优解。算例分析表明,该模型能有效地应对风电出力与电动汽车充放电行为的不确定性。

Day-ahead Economic Dispatch of Integrated Energy System Considering Uncertainties of Wind Power and Electric Vehicles

The uncertainty of wind power generation (WPG) output and charging and discharging of electric vehicles (EVs) has a great impact on the economy of integrated energy system(IES) operation. Considering the influence of the popularity of EVs on the IES, the operation models of various types of EVs are refined and differentiated according to the different travel and charging characteristics of EVs. Considering the uncertain probability of WPG output is difficult to obtain, a nonparametric fuzzy set based on imprecise Dirichlet model (IDM) is constructed on the basis of more historical data, so as to build a two-stage robust dispatch model for IES considering uncertainties of wind power and EVs. In order to obtain the optimal solution of the model, it is decomposed into a mixed integer linear programming (MILP) master problem and a sub-problem with max-min structure. By using duality theory and big M method, the sub-problem is transformed into a MILP problem, and the optimal solution of the main problem and the transformed sub-problem is iterated by column and constraint generation (C&CG) algorithm. Case studies show that the presented model can handle the uncertainties of wind power generation and EVs' behaviors.