Abstract
The transportation industry is a major contributor to global greenhouse gas emissions, and the adoption of electric vehicles (EVs) presents a viable solution to this problem. The implementation of vehicle-to-grid (V2G) systems has significantly enhanced energy supply and utilization efficiency, particularly through the integration of renewable energy sources such as solar and wind. However, the complexity of the V2G system necessitates addressing multiple objectives and constraints. Previous research has employed optimization methods to refine the V2G system. However, traditional linear and quadratic programming approaches are limited to simple and linear objectives, while non-linear programming methods encounter challenges when dealing with uncertain variables. Consequently, there is a pressing need for a more dependable optimization method capable of managing uncertain variables in this intricate system, particularly in the context of renewable energy source integration. This paper reviews the feasible and reliable optimization approaches utilized by scholars and researchers, examining the multiple objective functions and constraints required for optimizing the V2G system.
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