Stochastic approach for optimal sizing and allocation of energy storage systems

Storage system utilization provides a pivotal support for techno-economic integration of renewables. However, a precise modeling and estimation for optimal sizing of an energy storage system (ESS) combined with their optimal allocation is pertinent for operational and economical planning. While oversizing of ESS results in high capital costs, under-sizing ESS deters its integrative significance. In this paper, a technique for calculating an ESS size under solar and wind uncertainties is presented based on two stage stochastic programming. An AC-OPF probabilistic optimization problem is solved using the formulated two stage stochastic programming method that aims to improve system reliability by increasing its availability and reducing the total cost of maintenance and operation with the integration of optimal sized ESS. The efficacy of the proposed stochastic framework is presented for a modified IEEE RTS 24 bus system that is integrated with hybrid renewable energy sources considering. Numerous scenarios of summer and winter generation profiles are considered to outline the effectiveness of the proposed framework. The results obtained proves the efficacy of optimal ESS integration related to cost optimization, reliability, and optimal power flow.