A novel multi-task algorithm for operational optimization of coal mine integrated energy system under multiple uncertainties

The operational optimization of the coal mine integrated energy system (CMIES) is crucial for reducing costs and carbon emissions. However, the system's multi-objective nature, stringent constraints, and the uncertainty of renewable and mine-derived energy make solving its optimization challenging. Thus, this paper first presents a data-driven uncertainty transformation method to address the uncertainty of renewable energy and mining derived energy output; then, a multi-task multi-objective evolutionary algorithm based on adaptive auxiliary tasks (MMOEA-AS) is proposed, which includes a main task and three auxiliary tasks. Meanwhile, an adaptive update strategy for auxiliary tasks and a matching degree-guided knowledge transfer mechanism are proposed to improve the performance of the algorithm. Finally, taking the energy scheduling problem of a coal mine in Shanxi, China as an example, MMOEA-AS is compared with five advanced evolutionary algorithms. The results show that MMOEA-AS can effectively solve the operation optimization of the CMIES, and obtain the optimal scheduling results.