A two-stage stochastic programming approach for green smart homes: optimal panel sizing and cost-efficient scheduling of photovoltaic systems, battery storage, and EV charging under carbon emission pricing

Population growth and household lifestyles make energy management and improving energy consumption patterns global concerns. Growing energy consumption within residential section increases the direct dependency on fossil fuels. To reduce reliance, smart home technology and home energy management systems have been established. These systems plan the use of household appliances from peak hours to off-peak times to minimize energy consumption costs. Moreover, combining these systems with renewable energy sources like photovoltaic addresses environmental concerns and lowers daily grid energy usage. Incorporating an energy storage system with solar panels is essential for reducing solar energy waste, guaranteeing continuous electricity supply during blackouts, and lowering energy costs at peak hours, especially when combined with effective battery charge and discharge control. This paper presents a two-stage stochastic programming model for optimizing solar panel and energy storage sizing, along with daily energy scheduling, in a green smart home equipped with photovoltaic generation, battery storage, and electric vehicle (EV) charging under carbon emission pricing. The proposed model minimizes daily grid energy costs and consumption by utilizing day-ahead electricity prices and implementing efficient battery management strategies, while simultaneously incorporating EV charging dynamics to enhance both economic and environmental performance. The proposed model generates optimal 24-hour scheduling profiles for controllable appliances, battery charging/discharging operations, and electric vehicle charging, ensuring efficient energy utilization across all manageable household assets. To validate the efficacy of the proposed approach, three real-life case studies were conducted utilizing the embedded CPLEX solver within GAMS Studio, enabling comprehensive implementation and evaluation of the models,