Optimal location identification for aggregated charging of electric vehicles in solar photovoltaic powered microgrids with reduced distribution losses

• Published in: Energy sources. Part A, Recovery, utilization, and environmental effects Recovery, utilization, and environmental effects, 2024-12, Vol.ahead-of-print (ahead-of-print), p.1-16
• Main Authors: Gupta, Khushboo, Achathuparambil Narayanankutty, Ravishankar, Sundaramoorthy, Kumaravel, Sankar, Ashok
• Format: Article
• Language: English
• Subjects: Battery storage systems; electric vehicle; microgrid; optimal location; particle swarm optimization; renewable generation
• ISSN: 1556-7036; 1556-7230
• DOI: 10.1080/15567036.2020.1745335

The battery-powered electric vehicle finds an alternative for fossil fuel-based vehicles in the transportation sector. The charge-discharge power profiles of the battery storage systems (BSS) contribute toward distribution losses, which can be minimized by proper scheduling. Such scheduling gives better results if the charging stations are optimally placed in the solar photovoltaic (PV) powered microgrid. This paper proposes a methodology to identify the optimal location to charge the electric vehicle in the microgrid. The proposed methodology has been developed using particle swarm optimization (PSO)-based optimal power flow (OPF) with an integrated power management (IPM) algorithm. The novelty of the IPM algorithm is the coordinated charging-discharging of the multiple numbers of aBSS of the EVs to reduce the overall distribution losses of the microgrid. The proposed methodology is tested in a standard solar PV powered microgrid network, where the optimal locations to charge the electric vehicles are identified. The daily distribution loss of the network is computed for all possible charging locations of the electric vehicle in the microgrid, and it is found that the distribution loss is minimum for the identified optimal locations. Also, to evaluate the effectiveness of the proposed methodology, the distribution loss analysis is carried out for three test cases; i) un-optimized power flow, ii) PSO based-OPF, and iii) PSO-based OPF with IPM. The case study shows that the PSO-based OPF gives 84% reduction in daily distribution loss compared to the conventional un-optimized power flow test case. The daily distribution loss is further reduced by 8% by incorporating the IPM algorithm in the PSO-based OPF. The utility can thereby encourage the electric vehicle (EV) owners to park their EVs at the optimal locations to reduce the distribution losses.