A more efficient microgrid operation through the integration of probabilistic solar forecasts

Abstract

This work proposes a methodology based on the probabilistic dynamic programming (PDP) to integrate operational probabilistic forecasts of a photovoltaic (PV) plant into the optimization of the day-ahead schedule of an energy storage system (ESS). The proposed approach is tested on a microgrid based on a real educational building, a PV farm and Li-ion batteries. The objective is to minimize the operating cost of the microgrid. The operational day-ahead forecasts are derived from the Ensemble Prediction System (EPS) provided by a well-known Numerical Weather Prediction (NWP) model. Contrary to the classical use of deterministic forecasts, we demonstrate that the integration of the probabilistic forecasts in the optimization process leads to a more efficient microgrid management and to a reduction of up to 38% of the operating costs. Besides, it is shown that the non linearity resulting from the power dependency of the efficiency of the inverters must be taken into account in order to yield relevant optimization results. • A method to improve microgrid operation. • Use of probabilistic dynamic programming to solve the unit commitment problem. • Integration of probabilistic solar forecasts in the optimization. • Even the worst probabilistic forecasts over perform deterministic ones. • Non-linear optimization method is required to achieved suitable results.