Smart Scheduling and Cost-Benefit Analysis of Grid-Enabled Electric Vehicles for Wind Power Integration

This paper proposes a stochastic framework to mitigate the effects of uncertainty and enhance the predictability of wind power using the vehicle-to-grid (V2G) capabilities of electric vehicles (EVs). An Auto Regressive Moving Average (ARMA) wind speed model forecasts the wind power output. Using Fuzzy C-Means (FCM) clustering, EVs are grouped into 6 fleets of similar daily driving patterns. A Genetic Algorithm (GA) is used in combination with a Monte Carlo simulation (MCS) to optimize charging and discharging of the EVs. The optimization scheme minimizes the sum of the penalty cost associated with wind power imbalances and V2G expenses associated with purchased energy, battery wear and capital costs. The proposed method provides a collaborative strategy between the wind participants and EV owners to increase their revenues and incentives. A cost-benefit analysis assesses the economic feasibility of V2G services for wind power integration. The coordinated charging/discharging scheme optimally utilizes the V2G capacities of EVs and compensates for power imbalances due to random variations of wind power.