To address the challenges posed to the secure and reliable operation of the power grid under the “dual-carbon” goals, an optimal planning and investment return analysis method for grid-side energy storage system (GSESS) is proposed, with multi-dimensional grid security.
This thesis presents a design of experiments -based approach to develop an optimization tool that can predict the optimum performance of a Thermal Energy Storage (TES) system using Computational Fluid Dynamics (CFD), Response Surface Methodology, and Genetic.