Data-Driven Distributionally Robust Energy and Reserve Scheduling Considering RES Flexibility

Abstract

The growing penetration of variable renewable energy sources (RES) greatly increases uncertainty in the power system, and more flexibility is required to maintain the balance between power generation and consumption. Compared with costly investments in other resources, RES themselves can also be a promising source of flexibility, by providing balancing reserves through deloaded control. This paper proposes an economic dispatch scheme that allows variable RES to provide reserves, so that they can offer flexibility to balance the uncertainty from themselves and other variable resources. The model is versatile and considers different RES reserve control strategies and activation schemes. A data-driven distributionally robust chance-constrained (DD-DRCC) method is proposed to manage uncertainty from RES. Knowledge of variation ranges and probability distributions is captured in the data-driven uncertainty model. The adequacy, availability and deliverability of reserves in the re-dispatch stage are guaranteed by joint chance constraints. The model is transformed into a linear programming problem using conditional value-at-risk (CVaR) and solved efficiently. A case study confirms the effectiveness of the proposed method, and shows that it can achieve a balance between reliability and economy compared with existing approaches.