This paper deals with an approach to optimally size a supercapacitor-battery hybrid energy storage system for solar applications using the Genetic Algorithm (GA). GA simulation shows that the cost of the proposed supercapacitor-battery renewable energy system is lower than the cost of the conventional renewable energy system, which contains only battery system as energy storage devices. This approach suggests the optimal number of components used in the renewable energy system and ensures that the 20-year round total system cost is optimized subject to the constraint that the load energy requirements are completely covered (zero load rejection) or capacity shortage of 1% and 2%. The implemented GA fitness function optimized the 20-year round total system cost which equals to the sum of the respective component capital (initial), operational and maintenance cost. The concept of the proposed supercapacitor-battery hybrid energy storage system exploits the strengths and compensates for the weakness of each storage device. This means that the batteries which known as a high energy density is sized at the average power for delivering the average power, whereas, supercapacitors is designed to cater the peak power. This is feasible because the supercapacitor has lower resistance which is able to shield the battery from at least a portion of the current pulses and thus extend the battery lifetime. This not only aids the reduction of the cost of replacement batteries throughout the project lifetime but it is also said to be a more environmental friendly system. The main contribution of this approach is to optimize the cost of the Supercapacitor-battery hybrid energy storage system in renewable energy system which cannot be solved in most of the commercial simulation tools, such as HOMER and HYBRIDS due to the absence of the supercapacitor components in these commercial software.
|International Journal of Robotics and Mechatronics
|Published - 2014