Two-stage robust unit commitment with the cascade hydropower stations retrofitted with pump stations

•A detailed model of cascade hydropower stations retrofitted with pump stations is proposed.•Short-term optimal dispatch for the multi-energy hybrid system is proposed based on a two-stage robust optimization.•Nested column-and-constraint algorithm is designed to solve the two-stage unit commitment...

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Vydáno v:Applied energy Ročník 334; s. 120675
Hlavní autoři: Ju, Chang, Ding, Tao, Jia, Wenhao, Mu, Chenggang, Zhang, Hongji, Sun, Yuge
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 15.03.2023
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ISSN:0306-2619
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Shrnutí:•A detailed model of cascade hydropower stations retrofitted with pump stations is proposed.•Short-term optimal dispatch for the multi-energy hybrid system is proposed based on a two-stage robust optimization.•Nested column-and-constraint algorithm is designed to solve the two-stage unit commitment problem. Cascade hydropower stations are excellent flexible resources to regulate the drastic fluctuations of wind and photovoltaic power generation in the hybrid energy system. By constructing pump stations between two adjacent upstream and downstream reservoirs, the conventional cascade hydropower stations can be transformed into a cascade pumped hydro energy storage (CPHES) system, which further can promote the integration of clean energy resources. In this paper, a two-stage robust unit commitment model for the cascade hydropower stations retrofitted with pump stations is established to address the renewable energy uncertainties. A short-term scheduling framework is proposed for the hybrid energy system including CPHES, which coordinates the operating cost and clean energy curtailment. Besides, a modified nested column-and-constraint algorithm is employed to solve the two-stage robust optimization problem with integer resources. Numerical tests performed on a modified IEEE 24-bus system and a large-scale practical power system verify the effectiveness of the proposed scheduling model and algorithm.
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ISSN:0306-2619
DOI:10.1016/j.apenergy.2023.120675