Energy management optimization of integrated generation systems by fuzzy logic control

Energy flow management (EFM) is a very important task for integrated generation systems (IGS) designers and planners because in practice the amount of renewable source for long time operating is unknown depending on meteorological conditions. The wide variety of renewable energy sources and their highly site-specific and variable nature are coupled with different types of load demand claim for efficient and reliable EFM of IGSs. The forecasting errors of the meteorological data and model calculated must be taken into account to avoid poor performance in calculating required power. Fuzzy sets enable to handle these uncertainties and so making reliable and efficient EFM of IGS. In this paper a fuzzy logic approach has been used to address the EFM problems in IGS. As a case of study a proposed rule based EFM strategy has been implemented to a small sized IGS experimental prototype assembled in the frame of the project of the European Union “Combined multiple renewable energy source systems simulator”. The results show relevant improvement in energy performance, so as problems that rise by fuzzy logic application in IGS. Comparison with simulation results from an in house developed logistical program embedding conventional EFM are pointed out as the fuzzy EFM reduces fuel consumption and increases the penetration of renewable sources