In view of the mining integrated energy system suffers from multiple source-load uncertainties including fluctuating wind and photovoltaic output, dynamic variation of mine production load and unstable associated energy output after the integration of new energy such as wind power and photovoltaic power, which greatly increases scheduling complexity and makes it difficult to guarantee energy efficiency, a scheduling method integrating uncertainty analysis and multi-level collaboration optimization was proposed. Firstly, an energy flow model covering source-grid-load-storage was constructed, and a multi-scale source-load uncertainty interval model was established to quantify fluctuation risks. Secondly, a multi-time scale collaborative scheduling framework of day-ahead, intra-day slow layer and intra-day fast layer was designed, and the NSGA-Ⅱ algorithm was improved by adopting a time series segmentation strategy to realize the global optimization of operational cost and energy efficiency. Simulation results based on the integrated energy system of Datong Mining Area show that the system operation cost is reduced to 170 500 yuan, the renewable energy utilization rate is increased to 89.1%, the carbon emission is decreased to 6.83 t, the load tracking error is cut down to 18.9 kW, and the peak shaving rate reaches 17.2%. In addition, the average single-time period solution time of the scheduling method based on series segmentation NSGA-Ⅱ is only 7.0 ms, which is significantly superior to that of traditional methods. The research results verify the effectiveness of the energy scheduling method integrating source-load uncertainty and multi-level collaboration in improving the operational efficiency, energy conservation and emission reduction level of mine energy systems. Feasible scheduling strategies and algorithm support are provided for the efficient and low-carbon operation of mining integrated energy systems under complex uncertain conditions.

Mining integrated energy system  /  Source-load uncertainty  /  Multi-time scale optimization  /  Multi-level collaboration  /  Energy scheduling