Stochastic capacity planning and management of residential sizing under V2H capabilities: A joint Monte Carlo-PSO HEMS framework

This paper proposes a stochastic optimization framework for the optimal sizing and energy management of a residential Home Energy Management System (HEMS) with Vehicle-to-Home (V2H) capability. The proposed framework explicitly accounts for uncertainty in solar irradiance, residential electricity demand, and electric vehicle availability by modeling their probabilistic behavior with appropriate probability distributions and Monte Carlo simulations. Within this framework, Particle Swarm Optimization (PSO) is employed as a computational method to determine optimal photovoltaic and battery storage capacities and to schedule energy flows among the photovoltaic system, battery storage system, electric vehicle, household loads, and the utility grid, subject to operational and energy storage constraints. The main contribution of this work is the development of an integrated stochastic HEMS framework that jointly optimizes system sizing and energy management under uncertainties in photovoltaic generation, residential demand, and EV availability, enabling economically robust operational decisions. Simulation results demonstrate that the proposed framework reduces household electricity costs by 58.44%, yielding optimal asset allocations of 3.32 kW for the photovoltaic array and 0.11 kWh for the stationary battery storage system, compared with a conventional, non-optimized residential energy system, highlighting its effectiveness for cost-efficient and uncertainty-aware residential energy management.