基于分布鲁棒优化的灵活爬坡备用调度方法

Distributionally Robust Optimization Based Dispatch Methodology of Flexible Ramping Products

可再生能源大规模接入电网对电力系统的爬坡能力提出很大挑战,工业界得到应用的灵活爬坡产品的调度模型为含确定性爬坡需求约束的模型,该模型无法保证得到的爬坡容量决策解在不确定性实现下的可行性。该文研究灵活爬坡备用的分布鲁棒调度方法,提出考虑灵活爬坡备用可调度性的两阶段分布鲁棒调度模型,并考虑安全约束违反的风险,基于Wasserstein距离构建不确定性模糊集。所提方法能自动确定系统所需的灵活爬坡需求,并能保证得到的爬坡容量具有可调度性。所建两阶段模型可等效为混合整数线性规划问题直接进行求解。通过IEEE 14节点系统和IEEE 118节点系统的算例分析,验证所提方法的有效性。对比所提方法相对于含确定性爬坡需求调度模型和考虑不确定性的随机调度模型的优势,并进行灵敏度分析。

The large-scale integrations of renewable energy generation have imposed higher demands for ramping capability of power systems. The widely applicable dispatch model for flexible ramping products(FRPs) in the industry is the optimization model with constraints of deterministic ramping requirements. However, this model cannot guarantee the feasibility of decision solutions of ramping capacity under the realization of uncertainty. In this paper, a two-stage distributionally robust dispatch model for FRPs considering the risk of violation of security constraints was establish, and the ambiguity set based on Wasserstein metric was constructed. The proposed model can compute ramping requirements automatically and guarantee the dispatchability of the derived decision solutions of FRPs. The model can be approximately recast as a mixed integer linear programming. Experiments on IEEE 14-bus and 118-bus systems were carried out to demonstrate the effectiveness of the proposed method. Comparing with the dispatch model with deterministic ramping requirements and the stochastic dispatch model considering uncertainty, the advantages of the proposed dispatch model were verified, and sensitivity of number of training samples on the proposed method was analyzed.