Optimization of Energy Infrastructure Through High-Resolution Weather Forecasting

In recent years, one of the key challenges of digital transformation is to provide the tools to monitor critical infrastructures concerning safety and proper working. In this context, the IoT paradigm allows real-time monitoring thanks to hyperlocal sensors. Particularly, the measurements of meteorological variables are essential to regulate and optimize critical infrastructure parameters like the ampacity in electrical transmission or the maximum temperature supported by overhead lines. However, covering the entire area of interest with sensors is not always possible, and it requires excessive IoT weather stations to ensure proper operation, which is not feasible. Also, it is useful to have an additional layer to reconstruct data losses due to failure. Normally, electricity companies such as Transmission System Operators use conservative values to ensure the proper functioning of the infrastructure, this makes them inefficient when it is necessary to cover higher demand, which is possible due to higher renewable generation ratios, forcing the application of the well-known curtailments. On the other hand, in certain situations such as summer with high temperatures and minimal winds, the power grid may operate above the safe operating threshold, eventually causing maintenance issues. Thus, it is crucial to complement hyperlocal measurements with numerical real-time forecast models. This paper proposes an innovative approach to enhance the quality of predictions using the Weather Research and Forecasting (WRF) model, improving the performance and reliability of critical infrastructure monitoring beyond conservative estimates. Integrating high-resolution weather forecasts from the WRF model, it is possible to provide more accurate and localized meteorological data, which can significantly influence the operational parameters of high-voltage power lines estimating the ampacity (maximum intensity supported by the line). Unlike the traditional conservative estimates that tend to overcompensate for safety, leading to inefficiencies and renewable power losses, WRF forecasts enable dynamic adjustments based on real-time weather conditions.