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

• Published in: Energies (Basel) Vol. 18; no. 15; p. 4146
• Main Authors: Han, Chao, Zhu, Yun, Zhou, Xing, Wang, Xuejie
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2025
• Subjects: Alternative energy sources; Carbon dioxide; Case studies; Cholesky decomposition; Coal mining; conditional value-at-risk (CVaR); Costs; Decision making; Efficiency; Electricity; Emissions; Energy consumption; Energy resources; Energy storage; Force and energy; Methane; Methods; mine electricity–heat energy system; Mines; Mines and mineral resources; Optimization; Renewable resources; source–load correlation; stochastic optimization; Supply & demand; Sustainable development; Ventilation; Wind power; China
• DOI: 10.3390/en18154146
• ISSN: 1996-1073

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.