A Robust Optimization Model for a Smart Home with an Electric Vehicle Parking Lot

The transportation sector is a significant contributor to global carbon dioxide emissions. While electric vehicles (EVs) offer a solution to reduce these emissions, charging them with electricity from conventional sources can negate their environmental benefits. To address this, charging EVs with renewable energy sources is proposed, facilitated through the development of smart cities and smart homes. In this paper, a robust optimization problem is proposed to minimize the billing cost of the smart home while the thermal preferences of end-users are addressed. Also, in order to protect the upstream energy system with the high variation of smart home net loads with respect the renewable energies, flexibility constraints are added to the model. The upstream grid price uncertainty is addressed by the robust optimization approach. The proposed approach is a Mixed Integer Programming (MILP) problem and the CPLEX solver in the GAMS environment is used to solve the model. The results show that the flexibility constraint program has a significant impact on the energy cost of the smart home.