Distributionally robust optimization model considering deep peak shaving and uncertainty of renewable energy

Yansong Zhu, Jizhen Liu, Yong Hu, Yan Xie, Deliang Zeng and Ruilian Li

Energy, 2024, vol. 288, issue C

Abstract: In order to achieve the goal of carbon neutrality, the capacity of renewable power generation is continuously expanding while thermal power units are transitioning from main power source to auxiliary power source. To alleviate the peak shaving burden of thermal power units under the uncertainty of renewable energy and improve the absorption level of renewable energy, a two-stage distributionally robust optimization (DRO) model considering deep peak shaving and the uncertainty of renewable energy is proposed. The day-ahead unit commitment solutions are determined in the first stage, and the detailed scheduling strategies are obtained in the second stage. Column-and-constraint generation (C&CG) algorithm is applied to solve the model, and the master problem and subproblem are reformulated as duality-free mixed integer linear programming problems. The results show that the scheduling strategy obtained based on the model can alleviate the peak shaving burden brought by the uncertainty of renewable energy and reduce the abandonment rate of wind resources and solar resources, and the proposed DRO model provides a good trade-off between economy and robustness compared to stochastic optimization (SO) and robust optimization (RO).

Keywords: Deep peak shaving; Renewable energy consumption; Uncertainty set; Distributionally robust optimization; Battery energy storage system (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:288:y:2024:i:c:s0360544223033297

DOI: 10.1016/j.energy.2023.129935

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