How does AES’s use of AI and machine learning enhance grid-scale energy storage

AES enhances its grid-scale energy storage through advanced use of artificial intelligence (AI) and machine learning (ML) in several impactful ways:

AI/ML for Predictive Maintenance and Reliability

AES employs AI/ML to analyze massive datasets from its assets, enabling early prediction of equipment failures. This predictive maintenance approach shifts the utility from reactive repairs to preventive actions, thereby increasing asset uptime and system reliability essential for grid-scale storage operations. This reliability is crucial for ensuring stable energy availability from storage systems.

Optimization of Energy Production and Asset Operation

By leveraging machine learning models that incorporate weather forecasts and real-time operational data, AES can optimize the output and operation of its storage assets alongside renewable energy sources like wind and solar. For example, AES achieved a 15% improvement in wind forecasting accuracy at its Valcour Wind site, supporting more efficient energy dispatch and storage utilization.

Enhancing Energy Forecasting for Storage Dispatch

Machine learning models run on platforms like AES’s enterprise Google Cloud environment process actual generation and weather data to produce daily renewable energy forecasts. These forecasts feed into decision-making tools (e.g., PowerBI dashboards) used by commercial operations teams and dispatchers to schedule energy storage charge and discharge cycles more effectively. This leads to better grid balancing and maximizes the value of stored energy during peak demand or grid stress periods.

Integration and Virtualization of the Grid

AES’s AI-driven integration supports a dynamic and optimized “virtualized” grid infrastructure where utility-scale battery energy storage systems provide a critical buffer. This flexibility reduces overall system costs, minimizes outages, and enables rapid deployment of clean energy storage that adapts to fluctuating demand and supply.

AI-Enabled Revenue Maximization and Market Participation

Using AI models operated on platforms like the H2O AI Cloud, AES optimizes bids for power generation and storage assets based on predicted water availability, demand, and operational costs. This capability increases revenue for AES’s power plants while supporting a reliable, carbon-free grid. It indirectly enhances grid-scale storage economics by ensuring energy resources are dispatched and priced optimally.

In summary, AES’s strategic use of AI and machine learning enhances grid-scale energy storage by improving maintenance reliability, optimizing generation forecasting and dispatch, enabling more flexible and resilient grid operations, and maximizing economic returns from energy assets. These innovations jointly help AES deliver secure, affordable, and sustainable energy at grid scale.

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