Ms Madara Premawardhana Dassanayake Mudiyanselage Using Digital Twins to Enhance Energy Grid Sustainability

Abstract

In an electricity grid, the total energy supply comes from both fossil-fuel based production and renewable sources such as solar, hydropower and wind. Fossil-fuel based electricity production methods emit vast amounts of greenhouse gases to the atmosphere. To minimise greenhouse gas emissions, it is beneficial to increase electricity production from renewable sources and reduce electricity production from fossil-fuels. However, renewal energy generation relies on favourable weather conditions, which makes forecasting energy production from renewal sources difficult.  As a result, an excess of fossil-fuel based electricity must be generated to meet the demand. Our research investigates the use of Digital Twins to forecast electricity generation from renewable energy sources. Such Digital Twins can simulate weather conditions such as rainfall, snow, wind velocity, cloud coverage, sunshine, humidity, and temperature as well as geographical factors such as latitude, longitude, altitude and topographical features. Collective usage of these simulated features and renewable energy powerplant models can mimic real-world scenarios, which can be used to forecast near-accurate electricity production that contributes to the total electricity grid. Such accurate forecasts will lead to the reduction of excess fossil-fuel based electricity generation.