This paper proposes a solution for the day-ahead planning and the real-time control of an integrated system composed of a PhotoVoltaic plant (PV) and a Battery Energy Storage System (BESS). The day-ahead algorithm provides the optimal daily energy delivery profile based on a prediction of the PV production. The objective is to maximize the power delivery, according to the request of specific profile shape. The real- time operation algorithm dynamically regulates the BESS power exchange, in order to realize the scheduled power profile. Both the proposed algorithms exploit chance-constrained stochastic optimization, in order to take into account the uncertainties of the PV power predictions. Simulation results show the effectiveness of the proposed solutions, while their validation is achieved by experimental tests, executed on a LV microgrid controlled by a Distribution Management System (DMS).

PhotoVoltaic (PV) plants can provide important economic and environmental benefits to electric systems. On the other hand, the variability of the solar source leads to technical challenges in grid management as PV penetration rates increase continuously. For this reason, PV power forecasting represents a crucial tool for uncertainty management to ensure system stability. In this paper, a novel hybrid methodology for the PV forecasting is presented. The proposed approach can exploit clear-sky models or an ensemble of artificial neural networks, according to day-ahead weather forecast. In particular, the selection among these techniques is performed through a decision tree approach, which is designed to choose the best method among those aforementioned. The presented methodology has been validated on a real PV plant with very promising results.

The exploitation of combined photovoltaic (PV) plants and storage systems is nowadays assuming growing importance, due to the technical, environmental, and economical benefits which can derive from an optimal integration. In this paper, a mixed-integer algorithm for the optimal dispatch of a storage system, based on the day-ahead PV forecasting is developed. The optimization objective is the maximization of the total production of the integrated system, according to a requested active power profile, which can be defined by the operator. The study case of an existing distribution management system, which operates on the low-voltage microgrid at University of Genova is analyzed. The procedure is validated by field results with particular attention to the storage round-trip efficiency.