Stochastic Multi-Objective Optimization with the Aim of Household Energy Management

Currently, the use of renewable energy is considered to assist in meeting the demand for energy in residential homes. Utilization of renewable energy causes greenhouse gas emissions reduction. In this regard, a multi-objective two-stage scenario-based stochastic optimization is proposed in this paper. Uncertainties related to solar radiation and electrical load are assumed based on their specific probabilistic distribution function (PDF). To generate a large number of scenarios with the aim of modeling the uncertainties in this problem, the Monte-Carlo method is employed. Then, a comprehensive clustering method for scenario reduction is proposed. The residential energy hub has also considered integrating fuel cell and natural gas resources to supply thermal and electrical demands. Minimizing energy supply costs in a residential home is considered to be simultaneous with minimizing greenhouse gas emissions. A Pareto front including non-dominated solutions is then used to show the optimal points. A sensitivity analysis is also performed on the battery capacity to investigate its effect on electrical and thermal results. Therefore, an appropriate battery capacity according to stochastic multi-objective optimization can be specified.