Optimal integration of Vehicle to Building (V2B) and Building to Vehicle (B2V) technologies for commercial buildings

The advent of vehicle-to-building (V2B) technology provides an option to export electricity from the battery in electric vehicles (EVs) into buildings to assist in meeting building electric demands. However, there are some constraints on operating the EV fleets as flexible energy storage for buildings, including (i) stochastic and limited availability of EVs for building energy management; and (ii) necessity of bi-directional flow of EVs to operate in both V2B and building-to-vehicle (B2V) modes. Hence, these constraints and the changing degree of electric energy exchange provided by the different numbers of EVs could affect the building energy management system and the dynamic energy usage. The main objectives of this paper are to study the optimal configurations of the number of EVs interacting with a building by examining (1) the impact of different numbers of EVs on the daily electricity bills in a grid-connected commercial building microgrid, (2) the range of allowable EV numbers that enable optimal V2B capacity and reasonable charging loads for enhancing the cost-effectiveness of building energy management; (3) the role of different EV numbers in affecting the required stationary battery energy storage system (BESS) capacity in an isolated commercial building microgrid. It is observed that optimal building connected EV numbers are determined by EV driving patterns, seasonal solar output, building demand profiles, and the price of grid electricity. Thus, a scenario analysis is conducted to determine a range of suitable EV numbers in each scenario. Simulation results indicate that the optimal number of EVs varies greatly between summer and winter, as well as across different solar irradiance scenarios. In a case study where a building’s peak electric consumption is 30 kW and the PV system’s rated capacity is 50 kW, the allowable EV number, depending on the amount of available solar energy, varies from 8 to 29 in summer and changes from 2 to 15 in winter. These results could be used to suggest the optimal EV numbers based on the day-ahead solar energy forecasting, enabling day-ahead planning to either attract more vehicle owners to boost V2B capacity or to limit the number of integrated EVs in order to avoid a spike in total EV charging demand.