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A methodology for determining the optimal size of energy storage system (ESS) integrated with thermal power system is presented in this study. The optimal size is characterised by the rated stored energy and the maximum power rating of installed ESS for which the power system can achieve maximum revenue. Therefore the ESS cost formulation is conducted by analysing economic cost benefit measures considering life cycle of ESS. Since unit commitment (UC) scheduling is an important and integral part of power system cost optimisation, this study considers the operating schedule of thermal units (TU) while resolving ESS schedule. This proposed method uses tabu search (TS)-based evolutionary technique for solving this optimisation problem. TS is included in this algorithm to avoid re-evaluation of already evaluated ESS size which is powered by max priority heap and hash table data structure. The weekly schedule period is considered instead of daily to achieve more precise results. The proposed method is applied in two different power systems to determine the appropriate size of to be installed ESS. Experimental results reported that establishing the proposed method is an effective one to compute the optimal size of ESS for different sized power systems.