Optimization of Electric Vehicle Charging or Discharging Scheduling and Energy Storage in Multi-Objective Market Transactions Based on Quantum Genetic Algorithm

Electric Vehicles (AEVs) may play an essential role in the future of transportation as the use of electric vehicles grows and new transportation network services evolve. AEVs can automatically plan their routes, park at charging stations, and provide vehicle-to-grid (V2G) services. However, V2G services may disappoint customers due to work delays. EVs hold massive promise for future transportation systems, and effective charge scheduling tactics are vital to growing EV profitability. Two difficulties arise when charging/discharging EVs: how to reduce load and charging costs. The goal is to discover the most convenient EV charging station using VANET. This paper uses Monarch Butterfly African Vulture Optimization Algorithm (MBAVOA) for charge scheduling in EVs. The initial stage is to simulate EVs in the Vehicular Ad-hoc Network (VANET) model. Here, the shifting requests from EVs and accessible charging stations are identified. In addition, the load is computed using a Quantum Genetic Algorithm (QGA). Moreover, the multi-objective fitness parameters, like distance, charging cost, and user preference is considered for a charge or discharging schedule. The QGA-MBAVOA outperformed with the lowest charging cost of 66%, fitness of 0.010, and user convenience of 0.779.