A Forecasting-Based Control Algorithm for Improving Energy Managment in High Concentrator Photovoltaic Power Plant Integrated with Energy Storage Systems

Abstract

The High Concentrator Photovoltaic (HCPV) technology, due to its high efficiency, is considered one of the most promising solutions for the exploitation of sun-irradiation-based Renewable Energy Sources (RES). Nevertheless, the HCPV production is strictly connected to the Direct Normal Irradiation (DNI) making this photovoltaic technology more sensible to cloudiness than traditional ones. In order to mitigate the power intermittence and improve production programmability, the integration between Energy Storage Systems (ESSs) and HCPV, resorting to forecasting algorithms, has been investigated. Specifically, a local weather forecasting algorithm has been used for estimating the daily time evolution of DNI, air Temperature (T), Wind Speed (WS), and Air Mass (AM). These data are subsequently processed by means of an accurate HCPV model for the estimation of one day-ahead daily power production profile. The processing of HCPV forecasted generation by means of a properly tuned filter-based algorithm allows one day-ahead the definition of power profiles of ESS and power plant respectively, considering also the ESS constraints and the characteristic of the implemented real-time control algorithm. The effectiveness of the proposed forecasting model and control algorithm is verified through a simulation study referring to the solar power plant constituted by HCPV and ESS installed in Ottana, Italy. The results highlight that the application of the proposed approach lessens the power fluctuation effect caused by HCPV generation preserving the batteries at the same time. The feasibility and advantages of the proposed approach are finally presented.