Integrating renewable energy in electric V2G: Improved optimization assisting dispatch model

• Published in: International journal of energy research Vol. 46; no. 6; pp. 7917 - 7934
• Main Authors: Shobana, Selvaraj, Praghash, Kumaresan, Ramya, Ganesan, Rajakumar, B. R., Binu, D.
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Inc 01.05.2022
• Subjects: Algorithms; Alternative energy sources; Distributed generation; Electric power demand; Electric power grids; Electric vehicle charging; Electric vehicles; Electrical loads; Electricity distribution; Energy resources; Environmental control; Environmental management; FS‐MSA model; Genetic algorithms; Lagrange multiplier; Mathematical models; microgrid; moth search; Optimization; Particle swarm optimization; Renewable energy; Renewable energy sources; Renewable resources; RES; Resource management; Search algorithms; Transportation systems
• ISSN: 0363-907X; 1099-114X
• DOI: 10.1002/er.7690

Summary “Electric vehicles (EVs) are one of the most promising technologies to green the transportation systems. However, high penetration of EVs brings heavy electricity demand to the power grid.” Due to the intermittent character of renewable energy sources (RESs), it turns out to be very challenging to manage EV charging with other renewable generation and grid load. This paper aims to introduce a dispatch strategy assisted with the optimization concept for enhancing the economy of the microgrid system. The major objective is to minimize the cost of system operation and environmental control when meeting system load requirements. The output constraints related to the distributed power supply, such as power limits, are subjected to optimization. To solve this optimization issue, a new “Fitness Sorted Moth Search algorithm (FS‐MSA)” is introduced. Finally, the proposed work is compared and validated with other existing works with respect to various measures. The enhanced outcomes prove the efficacy of the implemented FS‐MSA model. This work concentrates on EV adoption integrated with RES for sustainable mobility. On observing the result, it can be noticed that the adopted scheme was 81.97%, 82.03%, 82%, and 81.97%, better than existing genetic algorithm, particle swarm optimization (PSO), moth search algorithm, and lagrange multiplier optimization (LMO) models for mean case scenario.