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This research aims to devise a comprehensive methodology for optimizing the size of a Battery Energy Storage System (BESS) supporting Wind Energy Systems (WES) to enhance power commitment flexibility in the energy market. The methodology involves three essential steps: (i) estimating rated kW, (ii) initializing rated kWh of the BESSs, and (iii) iteratively adjusting the BESS size based on heuristic rules to prevent State of Charge (SoC) limit violations following the load cycle. Three realistic load cycles for the BESSs out of which one load cycle is generated based on maximum error values and the other load cycles are generated based on the mean and 1&#x03C3; of the Normal Distribution Curve (NDC) of forecast errors of WES located at Agasthianpalli, Tamil Nadu, India. Two simple yet effective heuristic rules have been proposed to optimize the size of the BESS. The BESS size also ensures to start every day with maximum SoC and maintain SoC within the limits throughout the day. This leads to two scenarios considering a single set of the BESS to serve the load cycle and two sets of the BESS operating alternatively, reaching maximum SoC on the subsequent day by charging from the grid. Validation using Genetic Algorithm (GA) is done by comparing the costs of BESSs, emphasizing the suitability of the proposed technique. The cost analysis indicates that scenario 1 is optimal in terms of both cost and BESS size, surpassing scenario 2 by 8.89% and 9.95%, respectively.

An Iterative Heuristic Optimization Method for the optimum Sizing of Battery Energy Storage System to augment the dispatchability of Wind Generators