Zhang Zhi¹, Haiyu Huang², Wei Xiong², Yijia Zhou³, Mingyu Yan^3,*, Shaolian Xia², Baofeng Jiang², Renbin Su², Xichen Tian⁴

Energy Engineering, Vol.121, No.6, pp. 1557-1576, 2024, DOI:10.32604/ee.2024.047401

Abstract Stochastic unit commitment is one of the most powerful methods to address uncertainty. However, the existing scenario clustering technique for stochastic unit commitment cannot accurately select representative scenarios, which threatens the robustness of stochastic unit commitment and hinders its application. This paper provides a stochastic unit commitment with dynamic scenario clustering based on multi-parametric programming and Benders decomposition. The stochastic unit commitment is solved via the Benders decomposition, which decouples the primal problem into the master problem and two types of subproblems. In the master problem, the committed generator is determined, while the feasibility and… More >

Yuchen Hao^*, Dawei Su, Zhen Lei

Energy Engineering, Vol.119, No.5, pp. 2081-2104, 2022, DOI:10.32604/ee.2022.021342

Abstract Renewable energy sources (RES) such as wind turbines (WT) and solar cells have attracted the attention of power system operators and users alike, thanks to their lack of environmental pollution, independence of fossil fuels, and meager marginal costs. With the introduction of RES, challenges have faced the unit commitment (UC) problem as a traditional power system optimization problem aiming to minimize total costs by optimally determining units’ inputs and outputs, and specifying the optimal generation of each unit. The output power of RES such as WT and solar cells depends on natural factors such as… More >