Preparatory Curtailment Scheduling of Renewable Energy with Data-Driven Continuous Uncertainty

Abstract

Renewable energy (RE) sources in power systems can cause decreased stability and increased uncertainty, which can lead to operational risks and real-time emergency control. To prevent this, a preparatory curtailment schedule is necessary. This paper proposes a two-stage robust generation dispatch (RGD) model that calculates RE curtailment in the first-stage considering RE-load uncertainty in advance. The core contribution of this model is the introduction of a data-driven continuous uncertainty (DCU) set, which decreases over-conservatism and eliminates impractical scenarios. In the second-stage, the power balance is maintained through the reserve power of the diesel engine generator (DE), the output of the energy storage system (ESS) and the real-time curtailment of RE in the worst-case scenario, and a complete recourse assumption is proved. A column-and-constraint generation algorithm is implemented to solve the proposed min-max-min RGD problem. A secondary contribution is using the McCormick method for the first time for the linearization sub-problem and maintaining uncertainty variable characteristics. We prove this algorithm can produce optimal solutions in a finite number of iterations. The numerical example confirms the computation efficiency of solution time and scalability of the proposed model and algorithm.