Capacity configuration of a multi-energy complementary integrated energy system in a mining area based on multi-objective optimization

Driven by the “dual carbon” strategic goals, the coal mining industry, characterized by high energy consumption and emissions, was faced with a severe challenge of green and low-carbon transition. To fully utilize associated resources within the mining area, such as coal mine methane, mine water, and waste heat from ventilation air, and to effectively consume local renewable energy, a multi-objective capacity configuration optimization method was proposed for a multi-energy complementary integrated energy system. A system architecture including photovoltaics, wind power, a gas internal combustion engine, heat pumps, and energy storage was constructed. A multi-objective model was then established with the goals of minimizing annual total cost, life-cycle carbon emissions, and maximizing energy utilization rate. The Mixed-Integer Linear Programming (MILP) method was used to generate a Pareto optimal set. Subsequently, the Entropy-TOPSIS method was applied to select a balanced solution. The impacts of resource uncertainty and reliability on the optimal configuration were quantitatively analyzed by constructing multiple fluctuation scenarios and a 48-hour islanded operation scenario. The results indicate that a significant conflict exists among the optimization objectives. The lowest annual cost (91.938 5 million CNY) is achieved by the economical optimal solution, but its carbon emissions are the highest (83 600 tons) and its energy utilization rate is only 83.59%. In the environmental optimal solution, carbon emissions are minimized to 63 800 tons through large-scale configuration of photovoltaics and energy storage, but its cost is high. The highest energy utilization rate (95.38%) is reached by the energy-efficiency optimal solution, but its economic cost is also the greatest. Through the balanced solution selected by multi-objective decision-making, a significant 11.7% reduction in carbon emissions is achieved with only an 8.8% increase in cost compared to the economical optimal solution. Simultaneously, the energy utilization rate is increased to 89.35%. An effective trade-off among the economic, environmental, and energy efficiency goals is thereby achieved.