Using Digital Twins to Enhance Energy Grid Sustainability

Abstract

In the contemporary energy landscape, power generation is derived from both fossil fuels and renewable sources such as solar, wind, and hydropower1. While fossil fuels constitute a significant contributor to global energy supply, they also emit substantial quantities of greenhouse gases, thereby driving climate change. In contrast, increasing the utilization of renewable energy offers a potential means to mitigate these emissions. Renewable energy generation is highly dependent on meteorological patterns2, rendering accurate prediction of energy production challenging and occasionally necessitating the overproduction of fossil fuel-based electricity as a contingency measure. This paper investigates the application of Digital Twins—virtual models of physical systems3—to simulate meteorological and geographical factors in order to enhance renewable energy forecasts and address this challenge.