Exploiting Weather Forecast Information in Smart Grid Operations

We establish a framework to exploit detailed weather forecast information in smart grid operations. We first discuss how the use of traditional reactive operation strategies that neglect the future evolution of the ambient conditions can result into high operating costs. To overcome this problem, we introduce a supervisory operation strategies that can lead to more proactive and cost-effective operations. The framework is based on the solution of receding-horizon stochastic optimization problems. To obtain the weather forecast information, we employ a state-of-the-art forecasting model initialized with real meteorological data. Spatio-temporal uncertainty information is obtained from a set of ensembles generated by the Weather Research and Forecasting (WRF) model. We present several case studies to demonstrate that the proposed framework can lead to significant reductions in operating costs and energy losses at different levels of the grid decision-making hierarchy.