Home energy system: optimal design via risk-averse stochastic programming

Abstract This paper presents a risk-averse stochastic programming model for the optimal design of a home energy system that integrates renewable energy generation from photovoltaic panels and a battery energy storage system. Prosumer’s loads are classified into base and programmable loads and the possibility of exploiting the flexibility of these latter is considered in the optimal design. Uncertainties associated with weather-related variables, load demand, and electricity tariffs are considered, through the definition of scenarios representing possible joint evolutions of these factors. The model incorporates a risk measure to control the cost variability and the objective function, by the conditional value-at-risk, aims at minimizing the expected costs that the prosumer may incur in a given percentage of worst-case scenarios. The approach is applied to a real case study in the residential sector calibrated on data of the Italian electricity market. Through numerical experiments and sensitivity analysis, optimal system sizing and operational strategies are derived under different risk preferences. Results demonstrate that the risk-averse stochastic programming approach leads to robust decisions, providing a balance between cost-effectiveness and reliability in the management of the home energy system.