Affiliation:
1. School of Electrical Engineering, Yanshan University, Qinhuangdao, China
2. Jiangsu Collaborative Innovation Center for Smart Distribution Network, Nanjing, China
3. School of Electronic and Electric Engineering, Shanghai Jiaotong University, Shanghai, China
Abstract
Due to the energy crisis and environmental deterioration, the emerging hybrid energy ship power system gradually replaced the traditional ship power system to keep environmental friendliness by employing the clean energy. However, the increase of energy storage and photovoltaic generation system brings enormous challenge to the optimization scheduling of hybrid energy ship power system. For this reason, an improved genetic algorithm-based optimal scheduling strategy for the hybrid energy ship power system is developed in this paper. Firstly, a novel hybrid energy ship power system model including the diesel generator, energy storage system, propulsion system, dynamic load and photovoltaic power generation device is constructed under the constraint of energy efficiency and greenhouse gases emissions. Considering the various navigation situations that the ship may encounter, such as photovoltaic power generation limit in extreme weather and diesel generator power change in load shedding, the corresponding scheduling optimization problems for the hybrid energy ship power system are established. Under the cost and gas emission constraints, an improved genetic algorithm-based scheduling optimization algorithm is proposed. By introducing the nonlinear parameter change model in crossover and mutation operator, the performance of improved genetic algorithm can be enhanced, such as convergence speed and global optimization ability. Compared with current works, the proposed scheduling optimization strategy can achieve the lowest cost while reducing environmental impacts. Finally, simulation results under the given navigation cases demonstrate the superiority of the proposed improved genetic algorithm-based scheduling optimization strategy.
Funder
National Nature Science Foundation of China
Science and Technology Plan of Hebei Education Department
Nature Science Foundation of Hebei Province
Jiangsu Collaborative Innovation Center for Smart Distribution Network, Nanjing Institute of Technology
National Natural Science Foundation of China
Hebei Natural Science Foundation
Subject
Mechanical Engineering,Energy Engineering and Power Technology