Optimizing the self-consumption of residential photovoltaic energy and quantification of the impact of production forecast uncertainties

Abstract An energy management system optimizes the self-consumption of a residential photovoltaic installation, and the performance losses due to production uncertainties are evaluated. The specific case under study is an individual home equipped with photovoltaic (PV) panels where only an Electric Water Heater (EWH) is manipulated, and the rest of the appliances represent a fixed load. By formulating the problem of maximizing self-consumption as an unconstrained optimization problem, a novel and computationally efficient optimization algorithm has been proposed. The next step was to numerically evaluate the performance of this EWH management strategy under various PV power production scenarios, generated through a presented methodology. The reference baseline is a rule-based controller using a most likely forecast of PV production. Simulations performed in Dymola over 10 months demonstrate that, at a 30-minute timestep, the impact of a “perfect” PV production forecast is negligible compared with the impact of the choice of the control algorithm. Besides, a most likely forecast is good enough for the proposed algorithm to reach high self-consumption levels. Indeed, although the proposed optimization based on a most likely forecast yields an increase of 10 points of self-consumption compared to the baseline, only an additional 2 points of increase can be reached using “perfect” production information.