Design for Electrical Energy Storage System Using Machine Learning Application

Clean energy, typified by solar energy and wind energy, is employed to transform the energy structure and solve problems with energy and the environment. However, the generation of wind or solar electricity is uncertain owing to environmental consequences. Eliminating the instability is a critical method, and lithium batteries offer an option that is becoming more established. A capacity that is too big would result in waste and increased expenses, while a capacity that is too small would conflict with scheduling. The quantity of energy that can be stored is also controlled by power consumption, even if large industrial power demand is uncertain and inconsistent. For this demanding problem, a design that deploys as well as having a considerable loading capacity is essential. A battery installation model and a lithium battery's cycle life were taken into consideration as part of the design of an energy management model utilizing data on the electric power consumption and solar power production of an industrial estate. The performance of these lithium batteries and grid demand imposed constraints on the energy management system's ability to provide optimum energy storage capacity while also minimizing operating expenditures. With operating cost and an adequate battery capacity because the optimization objectives and lithium battery performance and power grid needs as the constraints, the DDPG technique (Deep-Deterministic-Policy-Gradient) was utilized. The simulation data demonstrate that, in compared to the present energy sources, the three ways of management of energy lowered the cost of volume and operation of the energy storage system by 19.1%.