考虑需求响应与风电不确定性的两阶段鲁棒旋转备用容量优化模型

以风电为代表的新能源大规模并网威胁到了传统电力系统的稳定运行，对备用容量的配置也有了新的要求。在新电改和能源需求侧改革的背景下，用户侧的需求响应（demand response，DR）资源在备用容量配置的问题上凭借其灵活性和可操作性越来越得到重视。为此，文章分别从基于价格和激励的需求响应参与备用容量优化角度，提出了以调度成本和备用成本最小为目标函数的两阶段鲁棒优化模型。利用列和约束生成（columa and constraint generation， C&CG）算法将其分解为考虑风电不确定性的日前备用容量优化配置主问题和实时备用电量分配子问题，运用AD算法计算子问题并寻找“最坏点”代入到主问题中进行迭代求解，提高了求解效率。算例结果验证了模型的有效性和正确性，并表明需求响应作为一种灵活的调控资源可以有效促进风电消纳，同时可以降低系统调度成本和备用成本。

Two-Stage Robust Optimization Model for Spinning Reserve Capacity Considering Demand Response and Uncertainty of Wind Power

Large-scale grid-connection of new energy represented by wind power threatens the stable operation of traditional power system， and it also has new requirements on the allocation of reserve capacity. Under the background of new power reform and energy demand-side reform， more and more attention has been paid to user-side demand-response resources in reserve capacity allocation by virtue of their flexibility and operability. Therefore， this paper proposes a two-stage robust optimization model with minimum scheduling cost and reserve cost as the objective function from the perspective of price and incentive-based demand response participating in spare capacity optimization. C&CG algorithm is used to decompose it into the main problem of optimal allocation of day-ahead reserve capacity and the sub-problem of real-time reserve power allocation considering the uncertainty of wind power. AD algorithm is used to calculate the sub-problem and find the “worst point” to improve the solving efficiency. The results of numerical examples verify the validity and correctness of the model. It has shown that demand response as a flexible regulation resource can effectively promote the absorption of wind power and reduce the cost of system scheduling and standby.