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

Wang，Han1; Jia，Youwei1; Lai，Chun Sing2; Xie，Peng1; Shi，Mengge1

10.1109/PESGM46819.2021.9637835

2021

1944-9925

1944-9933

978-1-6654-4630-3

IEEE Power and Energy Society General Meeting

2021-July

1-5

26-29 July 2021

Washington, DC, USA

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.

column-and-constraint generation minimax regret robust optimization uncertainty Virtual Power Plant

第一

英语

EI

National Natural Science Foundation of China[72071100];

20220611604680

Commerce ; Optimization ; Scheduling

Industrial Economics:911.2 ; Management:912.2 ; Optimization Techniques:921.5

2-s2.0-85124126762

Scopus

1.Southern University of Science and Technology,Dept of Electrical and Electronic Engineering,Shenzhen,China
2.Brunel Institute of Power Systems,Brunel University London,London,United Kingdom

Wang，Han,Jia，Youwei,Lai，Chun Sing,et al. Two-Stage Minimax Regret-Based Self-Scheduling Strategy for Virtual Power Plants[C],2021:1-5.