Battery electric and fuel cell electric vehicles have the potential to cover the shortage in renewable power generation by engaging in vehicle-to-grid. However, the vehicle-to-grid service cannot completely make up for the intermittent nature of renewables. Deterministic models were used to compute the extent to which the vehicles can engage in the vehicle-to-grid service in a smart city domain using the ‘Car as Power Plant’ model. The extent to which the vehicles can provide grid support in terms of energy valley filling is dependent on the method of selecting the vehicles for vehicle-to-grid and the nature of the load demand. Constraining algorithms limiting the extent of refuelling and recharging of the vehicles can help curtail import of hydrogen and power and spread the demand more evenly across the timeline, but also increase the waiting times during the same. An aggregator while coordinating vehicles for the vehicle-to-grid service may encounter some conflicts of interests with respect to ensuring equal vehicle-to-grid participation amongst its customers and investing in the supporting energy infrastructure. The setting of a minimum threshold fuel requirement for participating in vehicle-to-grid strongly relates to the effectiveness of the vehicle-to-grid service. There are some barriers for the adoption of vehicle-to-grid adoption such as competition from stationary batteries and its unreliability that is limiting its uptake. Additionally, the lack of mass uptake of battery electric and fuel cell electric vehicles has not yet got the market participants interested to invest in the vehicle-to-grid technology. Optimal smart charging strategies must address a variety of variables such as the solar hours, hourly grid prices, peak hours surcharge, charging infrastructure and congestion management. Many of the variables associated with smart charging are conflicting in nature and it sheds light on the multi-actor optimisation role of an energy aggregator.