Energy Storage Mix Optimization Based on Time Sequence Analysis Methodology for Surplus Renewable Energy Utilization

Abstract

Increasing the proportion of carbon-free power sources, such as renewable energy, is essential for transitioning to a zero-carbon power system. However, when the rate of grid expansion and flexibility cannot match the rate of renewable energy increase, surplus energy is the result. Surplus energy can be discarded through curtailment or stored and utilized when required. The optimal equipment configuration of the storage system should be determined based on the surplus energy characteristics. This study proposes an optimal energy storage mix configuration method by considering long-term forecasts of surplus energy in the South Korean renewable energy supply and power grid expansion plan. The surplus energy by time slot is comprehensively analyzed considering renewable energy power output, power demand, and power system operation constraints. We calculate the required power and energy of storage devices. Furthermore, we construct a long-term optimal energy storage mix using surplus energy generation patterns and technical and economical characteristics of storage technologies. The total cost minimization was considered as the objective function, comprising three elements: initial construction, equipment replacement, and loss costs for charging and discharging. We propose a time sequence analysis (TSA) method that enables chronological analysis from the starting year to the final target year. The TSA method provides an energy storage mix configuration roadmap that can utilize surplus energy for various years over the entire period, considering the annual increase in surplus energy and commercialization timing of each storage technology. We compare the difference between our proposed TSA method and the method that analyzes only the final target year to validate the superiority of this methodology.

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“…In Reference 17 , introduces a pioneering time sequence analysis (TSA) method. This method stands out for its ability to facilitate chronological analysis spanning from the initial year to the ultimate target year.…”

“…Employing a robust optimization (RO) approach grounded in the budget uncertainty set, this study prioritizes both robustness and economic considerations. However, despite the valuable contributions of References 17 , 18 in the realms of time sequence analysis (TSA) and robust optimization (RO) for wind power uncertainty, there exist notable research gaps that our study aims to address.…”