In order to effectively improve the utilization rate of solar energy resources and to develop sustainable urban efficiency, an integrated system of electric vehicle charging station (EVCS), small-scale photovoltaic (PV) system, and battery energy storage system (BESS) has been proposed and implemented in many cities around the world. This paper proposes an optimization model for grid-connected photovoltaic/battery energy storage/electric vehicle charging station (PBES) to size PV, BESS, and determine the charging/discharging pattern of BESS. The multi-agent particle swarm optimization (MAPSO) algorithm solves this model is solved, which combines multi-agent system (MAS) and the mechanism of particle swarm optimization (PSO). In this model, a load simulation model is presented to simulate EV charging patterns and to calculate the EV charging demand at each time interval. Finally, a case in Shanghai, China is conducted and three scenarios are analyzed to prove the effectiveness of the proposed model. A comparative analysis is also performed to show the superiority of MAPSO algorithm.
With the rapid development of energy Internet (EI), energy storage (ES), which is the key technology of EI, has attracted widespread attention. EI is composed of multiple energy networks that provide energy support for each other, so it has a great demand for diverse energy storages (ESs). All of this may result in energy redundancy throughout the whole EI system. Hence, coordinating ESs among various energy networks is of great importance. First of all, we put forward the necessity and principles of energy storage coordination (ESC) in EI. Then, the ESC model is constructed with the aim of economic efficiency (EE) and energy utilization efficiency (EUE) respectively. Finally, a multi-agent particle swarm optimization (MAPSO) algorithm is proposed to solve this problem. The calculation results are compared with that of PSO, and results show that MAPSO has good convergence and computational accuracy. In addition, the simulation results prove that EE plays the most important role when coordinating various ESs in EI, and an ES configuration with the multi-objective optimization of EE and EUE is concluded at last.
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