At a time when vast amounts of data are being collected from the systems around us, the timeliness, or freshness, of information has become increasingly important. This is especially true in environments where data underpins critical and often time-sensitive decisions. In complex physical systems such as smart grids, the availability of real-time data is essential for maintaining operational efficiency, stability, and reliability. The TimeSmart project explored how the Age of Information (AoI), a metric that measures the time elapsed since the most recently received piece of information was generated, can enhance the monitoring and control of power system assets and improve the integration and performance of renewable energy sources.
To lay the foundation, the project developed a formal framework for evaluating timeliness, using queueing theory to model data flows and propose a taxonomy of indicators relevant to grid operations. Building on this, a simulation incorporating real-world energy data was implemented, demonstrating that prioritizing timely information can double the accuracy of five-minute-ahead energy forecasts, thereby improving short-term forecasting and, consequently, enhancing the accuracy of decisions in energy management strategies. Alongside these efforts, TimeSmart contributed architectural building blocks for future time-aware smart grids, including edge-based anomaly detection and lightweight platforms to support AI-driven decision-making at the grid edge.
To translate these insights into more practical solutions, an intelligent energy management system was developed using deep reinforcement learning and linear programming. This system, tailored for smart energy communities with energy storage in the form of community batteries, showed promising results in simulation by optimizing energy use under variable pricing conditions. Its potential was further built upon during a six-month industrial placement. Altogether, TimeSmart demonstrated how improving the freshness of information can lead to more accurate forecasting, smarter control strategies, and ultimately, more efficient and resilient energy systems.