An innovative approach of optimizing size and cost of hybrid energy storage system with state of charge regulation for stand-alone direct current microgrids

Abstract This study proposed a novel approach to optimize size and cost of hybrid energy storage systems (HESS) based on a solar photovoltaic (PV) fed stand-alone DC microgrid, while considering the state of charge (SOC) of both batteries and supercapacitors to assure the long life of batteries and well-being during the operation. The sizing strategy is combined with an optimization model and a HESS Assessment Algorithm. The strategy differs from traditional strategies since it comprises of not only the system cost, but also SOC regulation of both batteries and supercapacitors. A unique penalty cost function depending on the unutilized solar power generation and unserved demand power after HESS is fully charged or discharged has been introduced. By this penalty cost function, SOC of HESS can be kept within a safer margin to guarantee the system stability. Also, system availability can be assured by penalizing the unserved demand power. A new SOC regulation concept for supercapacitors has been introduced. SOC of supercapacitors is maintained in between a predefined focused band to ensure the supercapacitors availability to absorb or deliver sudden power fluctuations. The lifetime of HESS and the solar PV system are considered in the objective function to find the optimum sizing of HESS. The simulation was performed under Hambantota Solar Park weather conditions using MATLAB. Multi-objective genetic algorithm is used to optimize the objective functions. Comparing with a traditional sizing method, it shows that the novel optimal sizing strategy can reduce the BS system capacity by 18% by the hybridization of supercapacitors. The best value for the energy ratio between supercapacitors and batteries, for the considered load profile was found to be 25%. Results prove that the number of penalties, surplus solar power generations and unserved demand power were made minimum while obtaining an optimal sizing of HESS with a minimum system investment cost.