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Energy management strategy governs the performance and efficiency of the system. This paper reports the use of an innovative strategy that implements monthly power split parameters to decide the division of load between the fuel cell and the batteries. These parameters are optimized and derived from swarm intelligence during the optimal sizing of a wind/PV/battery/Fuel cell configuration along with the optimized numbers of solar panels, wind turbines, fuel cells, batteries, DOD of batteries and the amount of hydrogen storage requirement. The energy management strategy also divides the operation of the system into five modes. While wind and solar form the primary sources of energy, batteries and a fuel cell stack forms the secondary and primary back-up source respectively. Two parameters, expected value of energy not served and the per-unit cost, have been chosen for the justification of the most optimal configuration. An adaptive particle swarm optimization is used for optimal sizing and a couple of radial basis neural networks trained with reset particle swarm optimization are used to calculate the life of batteries and fuel cell under specific operating conditions, thus helping us to maximize the life of fuel cells.

Off – Line Optimization of Strategic and Design Parameters of a Hybrid Stand-Alone Wind/Solar/Fuel Cell/Battery System