Task-Oriented Data Communication and Compression for Timely Forecasting and Control in Smart Grids

Advances in sensing, communication, and intelligent control have transformed power systems into data-driven smart grids, where forecasting and intelligent decision-making are essential components. Modern smart grids include distributed energy resources (DERs), renewable generation, battery energy storage systems, and large numbers of grid-edge devices that continuously generate time-series data. At the same time, increasing renewable penetration introduces substantial uncertainty in generation, net load, and market operations, while communication networks impose bandwidth, latency, and reliability constraints on timely data delivery. This dissertation addresses how time-series forecasting, data compression, and task-oriented wireless communication can be jointly designed for smart grid applications.

First, we study weather-aware distributed energy management in prosumer-centric microgrids and show that incorporating day-ahead weather information into decision-making improves battery dispatch and reduces the impact of renewable uncertainty. Second, we introduce forecasting-aware energy management in both wholesale and retail electricity markets, highlighting how renewable generation forecasting affects pricing, scheduling, and uncertainty mitigation. Third, we develop and evaluate deep learning methods for renewable generation forecasting, showing that Transformer-based models outperform recurrent baselines such as RNN and LSTM for wind and solar prediction tasks.

Building on this forecasting foundation, we develop a communication-efficient forecasting framework in which high-dimensional smart grid measurements are compressed into low-dimensional latent representations before transmission. This framework is extended into a task-oriented communication system that jointly optimizes data relevance and information timeliness, so that the receiver obtains compressed updates that remain useful for downstream forecasting tasks. Finally, we extend this framework to a distributed multi-node uplink setting, where multiple grid sensors share a bandwidth-limited channel, and develop scheduling policy that improves both the timeliness and task-relevance of received updates.