Potential impacts of emission control policy on the vehicle to grid environment: a novel approach

• Main Authors: Md Samar Ahmad, Shanmugavelu Sivasubramani
• Format: Article
• Language: English
• Published: Wiley 2019-03-01
• Series: IET Smart Grid
• Subjects: air pollution control; genetic algorithms; electric vehicles; Pareto optimisation; optimisation; battery storage plants; sorting; transportation; battery powered vehicles; existing transportation network; conventional gasoline-based engines; end users; inherent drawbacks; vehicle-to-grid environment; EVs penetration; conventional power generations; environmental conditions; strict emission control policy; emission policy; multiobjective optimisation problem; nondominated-sorting-based genetic algorithm; long-term impact analysis; future V2G market; potential impacts
• Online Access: https://digital-library.theiet.org/content/journals/10.1049/iet-stg.2018.0107

Environmental conditions strained the existing transportation network to replace its conventional gasoline-based engines by electric vehicles (EVs) to reduce its share in pollution. EVs promise a potential solution, but they fail to motivate end users because of their inherent drawbacks. Utilisation of EVs in vehicle-to-grid (V2G) environment is expected to boost EVs penetration but will burden the conventional power generations for their energy demand. For improvement of the environmental conditions, it is likely that the authorities will come up with a strict emission control policy. This study demonstrates the effects of emission policy on the V2G environment. The proposed problem is modelled as a multi-objective optimisation problem. Non-dominated-sorting-based genetic algorithm (NSGA-II) is used to solve this problem. This study also demonstrates a long-term impact analysis of a system consisting of a V2G market, end users and EVs by considering time variance in EVs penetration and vehicles' performance degradation. This analysis can help in planning and formulating the future V2G market.