Improved Differential Mutation Aquila Optimizer-Based Optimization Dispatching Strategy for Hybrid Energy Ship Power System
Xinyu Wang, Ruihao Li, Xiaoyuan Luo, Xinping Guan
Abstract
As ship multi-energy coordination becomes more prevalent, there is a demand for sophisticated optimal scheduling approaches to enhance energy efficiency. However, previous research has primarily focused on optimizing the scheduling of new energy sources, neglecting the utilization of excess heat energy. For this reason, this paper develops a novel optimization strategy incorporating waste heat recovery systems and thermal energy storage system for hybrid energy ship power systems (HESPS) using the improved Aquila Optimizer. Firstly, a synchronized model for a HESPS integrating of waste heat recovery system and thermal energy storage system can recycle excess heat from diesel engines to improve system energy consumption. Based on this, a multiple-objective optimization model with constraints on total cost and greenhouse gas emissions is established. Subsequently, an enhanced Differential Mutation Aquila Optimizer (DMAO) algorithm is introduced to identify the best optimal solutions. Finally, simulation cases demonstrate that the operating total cost and greenhouse gas emission of HESPS can be reduced at least by 11.965%and 20.980%. In comparison to current works, the total cost and greenhouse gas emissions can be reduced by at least 0.749% and 0.787% respectively. In terms of cost and emissions, the proposed Algorithm 1 shows superior stability and reliability compared to other algorithms.