Artificial Intelligence-Based Incorporation of Energy Storage Systems and Renewable Energy Sources

Abstract

The broad use of Renewable Energy Sources (RES) is aided by Energy Storage System (ESS), which in turn helps improve new energy consumption capabilities and maintain the reliable and cost-effective functioning of power grids. When RES are incorporated, the ESS is used to equalize electricity production and consumption. Due to its intermittent nature and fluctuating costs, RES necessitated the development of ESS. The current difficulty of energy shortage is significant. It has had an impact on research into alternative energy. To overcome this limitation we introduce Artificial Intelligence (AI). This research aims to fill that gap by presenting an AI approach for use in energy storage systems that make use of renewable energy.  ESSs may be integrated into the network; when these ESSs are of sufficient size and strategically placed, they increase the dependability of the entire system. To minimize the yearly cost of the system, which includes energy costs are not specified, and the operational costs of the ESSs, this paper proposes an effective method which is based on the Enhanced Dynamic Grey Wolf Optimizer (ED-GWO) to identify the best size and position of ESSs in a distribution systems. The experimental results show that our technology is more efficient than the other technique.