Academic Journal of Engineering and Technology Science, 2020, 3(1); doi: 10.25236/AJETS.2020.030105.
Jicheng Liu1,3, Qiongjie Dai1,2,3,*
1. School of Economics and Management, North China Electric Power University, Changping, Beijing, China, 102206; [email protected]
2. School of Mathematics and Computer Engineering, Ordos Institute of Technology, Ordos, Inner Mongolia, China, 017000;
3. Beijing Key Laboratory of New Energy and Low-Carbon Development (North China Electric Power University), Changping, Beijing, China, 102206
*Correspondence: [email protected]
An optimized allocation method of hybrid energy storage capacity has been proposed aimed at the random and intermittent characteristics of photovoltaic power generation in photovoltaic charging stations of electric vehicle. The method aims at maximizing the annual net profit of photovoltaic charging stations of electric vehicles and the photovoltaic consumption rate, and then establishes an optimization allocation model of multi-objective hybrid energy storage capacity. In this paper, an improved MOPSO is used to solve the Pareto solution for optimal allocation of the capacity of a hybrid energy storage system, and an improved TOPSIS algorithm is used to select the optimal solution in the Pareto optimal solution set as well. Through examples, the simulation is performed in two scenarios of single energy storage and hybrid energy storage. The simulation result shows that hybrid energy storage can reduce costs and promote photovoltaic energy consumption. Finally, the results of the improved algorithm are compared with the basic MOPSO, which verifies that the proposed algorithm is more effective and reasonable.
Multi-Objective Quantum Particle Swarm, Hybrid Energy Storage Capacity, Electric Vehicle Photovoltaic Charging Stations
Jicheng Liu, Qiongjie Dai. Optimization of Hybrid Energy Storage Capacity for Electric Vehicle Photovoltaic Charging Stations based on Multi-Objective Quantum Particle Swarm Optimization. Academic Journal of Engineering and Technology Science (2020) Vol. 3 Issue 1: 26-39. https://doi.org/10.25236/AJETS.2020.030105.
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