Genetic Algorithm-Based Optimal Sizing of Hybrid Battery/Ultracapacitor Energy Storage System for Wave Energy Harvesting Applications

Abstract

The output power of an ocean wave energy (WE) system has an intermittent and stochastic characteristic. WE output power can be transferred to the grid without sudden fluctuations when combined with a hybrid energy storage system (HESS) consisting of a battery pack and an ultracapacitor (UC) module. The study presented in this paper identifies the lowest-cost HESS sizing for WE systems by using a genetic algorithm (GA) optimization method. In this study, the system cost was reduced with the HESS cost and sizing study for ocean WE converter systems, and the battery was used effectively for a longer cycle. GA optimization has been applied in the field of HESS in ocean WE systems and has brought innovation to the literature with its optimum cost and sizing study. An optimum design model is presented considering the maximum/minimum voltage and current limits and the energy storage units’ temperature and depth of discharge parameters. The series and parallel connection calculations and the required number of battery and UC cells are given in the sizing section. The GA optimization was performed in Matlab, and the energy storage rate for the 625-kW system and the power and energy results of the energy storage units were given as a result of the optimum cost analysis. It has been calculated that 15936 battery cells are normally required for a 625-kW ocean WE converter system. As a result of the proposed optimally sized, cost-effective HESS, it is presented that a hybrid energy system can be used at a lower cost with 5548 battery cells and 2125 UC cells for the same performance and functionality.

Keywords

• Battery
• cost analysis
• genetic algorithm
• hybrid system
• ultracapacitor
• wave energy converter