Stochastic Optimization Scheduling Method for Mine Electricity–Heat Energy Systems Considering Power-to-Gas and Conditional Value-at-Risk

Chao Han, Yun Zhu, Xing Zhou and Xuejie Wang ()
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Chao Han: CEC Technical & Economic Consulting Center of Power Construction, Electric Power Development Research Institute Co., Ltd., Beijing 100053, China
Yun Zhu: CEC Technical & Economic Consulting Center of Power Construction, Electric Power Development Research Institute Co., Ltd., Beijing 100053, China
Xing Zhou: CEC Technical & Economic Consulting Center of Power Construction, Electric Power Development Research Institute Co., Ltd., Beijing 100053, China
Xuejie Wang: School of Economics and Management, Yanshan University, Qinhuangdao 066000, China

Energies, 2025, vol. 18, issue 15, 1-22

Abstract: To fully accommodate renewable and derivative energy sources in mine energy systems under supply and demand uncertainties, this paper proposes an optimized electricity–heat scheduling method for mining areas that incorporates Power-to-Gas (P2G) technology and Conditional Value-at-Risk (CVaR). First, to address uncertainties on both the supply and demand sides, a P2G unit is introduced, and a Latin hypercube sampling technique based on Cholesky decomposition is employed to generate wind–solar-load sample matrices that capture source–load correlations, which are subsequently used to construct representative scenarios. Second, a stochastic optimization scheduling model is developed for the mine electricity–heat energy system, aiming to minimize the total scheduling cost comprising day-ahead scheduling cost, expected reserve adjustment cost, and CVaR. Finally, a case study on a typical mine electricity–heat energy system is conducted to validate the effectiveness of the proposed method in terms of operational cost reduction and system reliability. The results demonstrate a 1.4% reduction in the total operating cost, achieving a balance between economic efficiency and system security.

Keywords: mine electricity–heat energy system; source–load correlation; Cholesky decomposition; conditional value-at-risk (CVaR); stochastic optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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