Performance Analysis of Smart Grid Energy Systems Under Varying Loads Using Digital Twin and Real-Time Data Analytics

This article describes how to analyze the performance of intelligent grid power system under variable load situations through Digital Twin technology and realtime information processing. The paper used five dimensions to give a comprehensive evaluation: load behavior, forecast accuracy, energy efficiency, PV and battery peak-shaving, whether economical or environmental effects are good or bad points for the project. Diverse load profiles were easily modeled using this Digital Twin, and forecasting models could predict output accurately: whether it is load, PV power or battery state-ofcharge. All use-case conditions resulted in energy efficiency higher than 95 %, and the battery itself did not violate its stress limits. As much as 18.6 % of peak electricity demand could be cut thanks to DT-based implementation, while PV-generated power was indeed used for its own consumption at levels (86.2%) that reduced grid connection requirements. From an economic perspective, compared to conventional methods, on average using Digital Twin technology for energy implementation could save energy costs by at least 20 %. From an environmental point of view, in worst-case scenarios it can reduce 299.3 tons of co2 this year These findings illustrate the capability of DTs to bring flexible operation modes, lower costs and greener environment to smart grid systems. The paper underscores the role Digital Twin systems will play in tomorrow's energy infrastructure as a vital new tool which is intelligent, resilient, and data driven.