Study of optimal placement of energy storage units within a deregulated power system

This paper proposed optimal placement of the energy storage units within a deregulated power system to diminish its hourly cost. Wind generation and load are modeled using actual data approach. Based on a model of the electricity market, we diminish the hourly communal cost using probabilistic optimal power flow (POPF) then use an Improved Adaptive Genetic Algorithm (IAGA) to exploit wind power utilization over a scheduling period. A commerce model is developed to evaluate the economics of the storage system based on the energy time-shift occasion from wind generation. The planned method is used to carry out simulation studies for the IEEE 24-bus system. Transmission line constraints are addressed as a block for efficient wind power integration with higher diffusion levels. Distributed storage is then proposed as a solution to effectively utilize the transmission capacity and put together the wind power more efficiently.