考虑风电出力不确定性的分布鲁棒主备协同优化调度

随着大量可再生能源如风电接入电网，如何最大化地利用可再生能源、减小传统火电机组的成本成为亟需解决的难题，特别是机组运行和备用容量的协同调度成为现在研究的一大热点。考虑风电出力的不确定性，建立了成本最小化的两阶段经济调度模型。在第一阶段，即日前调度，根据预测的风电出力制定机组预调度方案。在第二阶段，即实时调度阶段，根据风电的实时出力对第一阶段调度方案进行反馈调节。针对风电出力的不确定性，应用Kullback-Leibler离散度原理对不确定因素进行建模。结合分布鲁棒方法，建立了极端概率分布下的两阶段分布鲁棒主备协同优化模型，并将其转化为可解的混合整数非线性规划问题。基于广义Benders分解方法，提出了分解协调算法对优化模型进行求解。最后以IEEE 6 节点系统和IEEE 118 节点系统进行算例仿真分析，并对比传统鲁棒及随机规划方法，验证了所提方法的可行性和优越性。

Distributionally robust co-optimization of energy and reserve dispatch considering uncertain wind power output

With a large number of renewable energy sources such as wind power connected to the grid, how to maximize the use of renewable energy and reduce the cost of traditional thermal power units have become urgent problems. In particular the co-optimization of energy and reserve dispatch has become a hot research topic. Considering the uncertainty of wind power output, a two-stage economic dispatch model is established to minimize the cost of the objective value. In the first stage, day ahead dispatching is determined according to the predicted wind power output. In the second stage, i.e., the real-time dispatching stage, feedback regulation is used for the first stage scheduling considering the real-time output of wind power. Given the uncertainty of wind power output, Kullback-Leibler divergence is used to model the uncertain variables. Combined with the distributed robust method, a two-stage distributionally robust energy and reserve co-optimization model with an extreme probability distribution is established, and it is transformed into a solvable mixed integer nonlinear programming problem. Based on the generalized Benders decomposition method, a decomposition coordination algorithm is proposed to solve the optimization model. Finally, IEEE 6-bus system and IEEE 118-bus system are taken as tested simulation systems, and the feasibility and superiority of the proposed method are verified by comparing the traditional robust and stochastic programming methods. This work is supported by National Natural Science Foundation of China (No. 51777126).