Two-Stage Minimax Regret-Based Self-Scheduling Strategy for Virtual Power Plants

The market and renewable generation uncertainties cast great challenges to the profit-oriented self-scheduling of commercial virtual power plants (VPP). To address the uncertainty issues, this paper proposes a two-stage minimax regret (MMR)-based optimization model to reach optimal VPP self-scheduling solutions, in which the underneath problem is intrinsically NP-hard. To obtain an exact solution of the formulated problem, we firstly reformulate it into a two-stage robust optimization (TSRO) problem with fixed recourse, then re-solve it by adopting the column-and-constraint generation algorithm. In the numerical experiments, we evaluate the performance of the proposed MMR approach by comparing it with the maximin profit approach and the perfect information approach under different occasions. The results suggest that the two-stage MMR approach can achieve a near-optimal solution. Also, it is demonstrated that the performance of the MMR approach is robust in highly volatile environments and significantly penalizing balancing markets.