A Multitask Multiobjective Operation Optimization Method for Coal Mine Integrated Energy System

The operation optimization problem of coal mine integrated energy system (CMIES) is characterized by multiobjective, strong constraints, large scale, and mixed variables. It is difficult for existing multiobjective evolutionary algorithms to obtain a set of nondominated solutions with good convergence and uniform distribution, primarily due to the absence of suitable constraint-handling techniques. This research proposes a multitask multiobjective operation optimization framework combining evolutionary algorithm and mathematical programming (MO-EAMP) to address this issue. Within this framework, the main task employs an evolutionary algorithm with global search capability to solve the multiobjective CMIES operation optimization problem. Meanwhile, auxiliary tasks utilize mathematical programming method with robust linear constraint handling capability to solve multiple weighted single-objective CMIES operation optimization problems. During the iteration process of MO-EAMP, the scale and form of auxiliary tasks are adjusted autonomously based on the current state of population, with the aim of guiding the population search toward more promising regions. Finally, the presented algorithm is applied to a coal mine in Shanxi Province, China, and the experimental results demonstrate that the proposed algorithm can obtain a set of optimal operation plans with better convergence and distribution in a shorter time, compared with 7 other existing algorithms.

I. Introduction

The regional integrated energy system (RIES) is an integrated system encompassing heterogeneous energy production, transmission, conversion, storage, and consumption activities within a specific region. It can effectively harness renewable energy to maximize meeting the diverse energy demands of various user [1]. Currently, the RIES operation optimization problem has become a focal point in energy and power. Most existing methods model this problem as a mixed integer programming (MIP) problem [2]. Common solving methods include Benders decomposition [3], [4], Cplex or Gurobi solver [5], [6], [7], [8], and other mathematical programming methods. While these methods show good speed advantage in small-scale linear problems, they are not suitable for large-scale, nonlinear, or multiobjective optimization problems. Therefore, scholars have begun to study evolutionary algorithm (EA)-based methods. For example, Liu et al. [9] presented a cutting and repulsion-based evolutionary framework, Yu et al. [10] given a Q-learning-based meta-heuristic method, and Li et al. [11] proposed an improved artificial bee colony algorithm.