Optimized sizing of photovoltaic grid‐connected electric vehicle charging system using particle swarm optimization

• Published in: International journal of energy research Vol. 43; no. 1; pp. 500 - 522
• Main Authors: Bhatti, Abdul Rauf, Salam, Zainal, Sultana, Beenish, Rasheed, Nadia, Awan, Ahmed Bilal, Sultana, Umbrin, Younas, Muhammad
• Format: Journal Article
• Language: English
• Published: Bognor Regis John Wiley & Sons, Inc 01.01.2019
• Subjects: Batteries; Computer simulation; Economic impact; Economic models; Economics; Electric vehicle charging; Electric vehicles; Energy; Energy management; Energy storage; energy storage unit; EV charging station; Lithium-ion batteries; Objective function; optimum system sizing; Particle swarm optimization; Photovoltaic cells; photovoltaic module; Photovoltaics; PSO; PV‐ESU grid system; Solar cells; solar energy; Tariffs; Vehicles; Weather
• ISSN: 0363-907X; 1099-114X
• DOI: 10.1002/er.4287

Summary In this paper, the particle swarm optimization (PSO) is used to find optimum size of the photovoltaic (PV) array and energy storage unit (ESU) for PV grid‐connected charging system (in office workplace) for electric vehicles (EV). It is designed in such a way that the EVs are charged at a fixed price (rather than time‐of‐use price) without incurring economic losses to the station owner. The simulation is modeled using the single diode model (for PV) and the state of charge of Li‐ion battery (for ESU and EV). The objective function of the PSO is formulated based on a financial model that comprises of the grid tariff, EV demand, and the purchasing as well as selling prices of the energy from PV and ESU. By integrating the financial model with energy management algorithm (EMA), the PSO computes the minimum number of PV modules (Npv) and ESU batteries (Nbat) for a various number of vehicles and office holidays. The resiliency of the proposed system is validated under different weather conditions, EV fleet, parity levels, energy prices, and operating period. Furthermore, the performance of the proposed system is compared with the standard grid charging system. The results suggest that with the computed Npv and Nbat, the charging price is decreased by approximately 16%, while the EV charging burden on the grid is reduced by 94% to 99%. It is envisaged that this work provides the guidance for the installers to precisely determine the optimum size of the components prior to the physical construction of the charging station. Methodology for modeling of PV‐ESU grid‐based EV charging system is described. Main features of existing popular system sizing techniques are compared. An energy management algorithm (EMA) is developed to control the charging system. Optimum sizes of PV modules and ESU batteries are determined by means of PSO. Proposed system is benchmarked against the standard grid charging system.