Optimized Scheduling Model of Integrated Energy System in Industrial Park Based on Improved MPA

In the context of the global energy shortage and the increasing emphasis on sustainable development goals, optimizing the scheduling of integrated energy systems in industrial parks has become a critical issue. This paper presents an optimal scheduling model and an improved solution method to enhance energy efficiency in industrial parks. Firstly, we develop an energy efficiency optimization model for the integrated energy system in an industrial park. The model aims to maximize system energy efficiency and minimize operational costs while considering load balance constraints, equipment capacity, operational limits, and renewable energy generation constraints. Secondly, we introduce an enhanced Marine Predators Algorithm (MPA) incorporating an adaptive step-size strategy and a multi-subgroup approach. These improvements enable adaptive adjustment of the iteration step size, accelerating convergence and improving solution accuracy, thus facilitating the efficient solving of the model. Then, The effectiveness of the proposed solution method is validated through standard test functions. Finally, we test the model through simulations. The results show that, when considering both energy efficiency and economic factors, our proposed scheduling model effectively improves energy efficiency. It can theoretically reduce energy costs by 3.13% and save 23.683 tons of standard coal annually, all while maintaining the same output level.