Distributionally Robust Framework and its Approximations Based on Vector and Region Split for Self-Scheduling of Generation Companies
Linfeng Yang, Ying Yang, Guo Chen, Zhao Yang Dong
Abstract
To ensure a successful bid while maximizing profits, generation companies (GENCOs) need a self-scheduling strategy that can cope with a variety of scenarios. Therefore, distributionally robust optimization (DRO) is a good choice because it can provide an adjustable self-scheduling strategy for GENCOs in an uncertain environment, which can balance robustness and economics well compared to strategies derived from robust optimization and stochastic optimization. In this article, a novel moment-based DRO model with conditional value-at-risk is proposed to solve the self-scheduling problem under electricity price uncertainty. The size of the model mainly depends on the system size, and the computational burden increases sharply as the system size increases. For this drawback, two effective approximate models are proposed: one approximate model based on vector splitting (DRA-VS) and another based on the alternate direction multiplier method (DRA-ADMM). Both can greatly reduce calculation time and resources, while ensuring the quality of the solution, and DRA-ADMM only needs the information of the current area in each step of the solution, thus, private information is guaranteed. Simulations of three IEEE test systems are conducted to demonstrate the correctness and effectiveness of the proposed DRO model and two approximate models.