Medium-term forecast of multi-energy photovoltaic/biogas/battery system’s operation adopting smart energy management strategy

The increasing adoption of hybrid power systems requires the development of advanced forecast models and smart energy management strategies. This work investigates the performance of a rule-based control multi-energy renewable system that combines solar photovoltaic (PV) and biogas technologies. The system incorporates a battery energy storage system with an objective to optimally mitigate the inherent variability of renewable energy systems employing advanced one-week forecasting models based on estimations derived from environmental data. The modeling of PV power output uses forecasted weather data which includes temperature and solar irradiation. To forecast biogas yields, the research employs Anaerobic Digestion Model No. 1. The multi-energy system is examined under real-world operational conditions by leveraging available measured electrical data. The validation of the developed one-week forecasting model has been performed and yielded interesting results with a normalized root mean square error ranging from 11.36% to 20.87%. The robustness of the proposed system has been investigated through sensitivity analyses on the BESS’s depth of discharge, capacity, and efficiency. The obtained findings demonstrate a strong correlation between experimental and forecasted PV output. The biogas system results in consistent energy generation over the course of the studied period, and the proper use of battery storage with the proposed smart energy management strategy made it possible to efficiently manage energy with minimal loss of power supply.