Özet:

Decline of natural resources reserves , global warming, energy cost increase, and rising electricity demand make clean and sustainable energy provision using hybrid renewable resources inevitable.  Solar and wind energy as free and eco-friendly sources of energy have been considered a promising choice for remote (or rural) area electrification. Dependence of PV/WT production on weather condition necessitates integrating a backup system and storage banks. While a fuel-cell system makes a clean backup available, incorporating both energy type and power type storage technologies, such as batteries, hydrogen-based storage systems, and supercapacitors, extends the energy sources/storage units useful lifespan and decreases the operation cost of the system. An energy control system is required to provide the load with reliable, continuous, high quality and economical energy. Considering PV/WT production uncertainty, load power variations and measurement imprecision, energy management system based on fuzzy logic technique serves an effective method to meet the design objectives, such as energy efficiency maximization, reliable and continuous energy supply, DC bus voltage stabilization etc. The fuzzy logic controller is designed based on the prior knowledge and experience of an expert. Thus, it is not an optimal strategy. Aiming to optimize preferred aspects of the fuzzy controller, it should be combined with evolutionary algorithms, such as genetic. Therefore, this paper deals with the rule-based fuzzy logic energy control of an off-grid PV/WT/FC/UC hybrid renewable system. Applying the genetic algorithm, the ECMS and the EEMS) are utilized to tune off-line the fuzzy logic control, in order to the fuel consumption optimization. To reduce computation time during the optimization process, the fuzzy rule set remains fixed. Employing a simulation model of the hybrid renewable system, multiple criteria such as the fuel efficiency, the fuel-cell stack efficiency, and the fuel consumption are taken into account to evaluate the energy management strategy's performance. Simulation results show that The fuzzy-ECMS and the fuzzy-EEMS keeps the battery SOC around the “0.5 ( + †and the , respectively. As a result, better fuel economy and higher battery lifetime can be achieved via the fuzzy-EEMS and the fuzzy-ECMS, respectively.<

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