Configuration optimization and energy management of hybrid energy system for marine using quantum computing

Abstract

Improving the technology of hybrid energy systems is an important development direction for the greening of ships, the configuration optimization and energy management of ship hybrid energy system is vital to enhance the marine electric power system reliability, economic efficiency, and sustainability. Focusing on this problem, this paper has carried out a study on the multi-objective configuration optimization method (COM) and energy management strategy (EMS) for ship hybrid energy system based on quantum computing. First, the mathematical model of distributed power modules of the hybrid energy system is established, and a configuration optimization objective function that aims at low-cost, long equipment life, and high reliability of power supply is constructed. Then, the combination with fuzzy rules and quantum multi-objective artificial bee colony algorithm to solve the objective function, the configuration scheme satisfying multiple constraints is obtained. On this basis, an energy management optimization objective function that meets both low-cost operation and maximum clean energy utilization for ship electric power system is established, the objective function is optimized in multi-objective quantum particle swarm optimization (QPSO) algorithm, and the real-time optimal scheduling for hybrid energy systems of the ship is realized. Experiments on simulative navigation data verify the feasibility of the multi-objective configuration optimization method using the quantum artificial bee colony (QABC) algorithm. Furthermore, energy management experiments with different strategies, experimental results show that the energy management strategy proposed in this paper outperforms other methods- and effectively reduces the operating costs, fuel costs, and pollutant emissions of marine power system, meeting the environmental requirements of the Energy Efficiency Operating Index (EEOI) for ships.