AI-Enhanced Hydropower Systems: Smart Dams for a Resilient Future

This paper focuses on water-energy, using AI for smart, holistic hydropower operations to enhance water for energy resilience.

The existing challenges include (a) increasing demand for water and electricity, requiring a shift to flexible, real-time hydropower operations due to changing demand and weather. (b) Short-term resilience is impacted by hydrometeorological extremes, while long-term vulnerabilities are driven by natural and human factors, leading to issues like low reservoir levels, increased water temperatures, and aging infrastructure. (c) Optimizing for one function in reservoir operations can compromise others. (d) Opportunity to use Artificial Intelligence (AI) to optimize and adapt reservoir operations for short and long-term resilience. Low AI adoption in the hydropower sector due to concerns about unproven technologies, data access barriers, data ownership, cybersecurity, and lack of clear standards or metrics for performance.

Near-Term Opportunities (using AI/Machine Learning) include (a) short-term predictions: AI models can forecast energy production, streamflow, and water quality for low-latency decisions in forecast-informed reservoir operations (FIRO). (b) Longer-term outlooks: AI can learn from complex datasets to provide seasonal-to-decadal inflow projections, capturing coupled atmospheric science, hydrology, ground motion, and energy dynamics. (c) Autonomous, self-guiding observations: Smart dams can leverage IoT networks, drones, robotics, and remote sensing for comprehensive monitoring. (d) Advanced Data Management: AI tools can automate data management, quality assurance, and integration of large data streams, with Large Language Models extracting information from historical documents. (e) Digital Twins and Reinforcement Learning: Digital twins, agentic systems enable smart dam operations by assimilating monitoring data. Reinforcement learning optimizes multi-objective dam operations for real-time decision support.

Success measures would be that AI research in hydropower should inform operations for energy security, dam operators, other agencies, and industry partners, extending to other types of reservoirs, as well as specific measures include developing a framework for assessing AI methods with benchmarks and demonstrating superior AI use for inflow forecasting and early warning.