AI & machine learning |
Harnessing AI to transform hydropower
Janice Goodenough, CEO of HYDROGRID, offers valuable insights into the transformative role of AI and machine learning in hydropower management. Her expertise sheds light on how these technologies are revolutionizing the industry, addressing key challenges, and promoting sustainability
What are the main challenges in hydropower management that AI and machine learning help address? That’s an important question, and to answer it, we first need to demystify what AI really is. Many people fear AI because it sounds complex or intimidating, but it’s essentially about using data and algorithms to support or simulate human decision-making. The hydropower industry faces challenges like
Above: Janice Goodenough, CEO of HYDROGRID
production planning, maintenance scheduling, and water management. These decisions often need to be made quickly, sometimes within hours or even minutes. AI and data science help by providing tools that make these processes faster and more accurate. Going back to the initial question, inflow forecasting is one of the biggest challenges in hydropower. Traditionally, hydropower operators have relied on historical data and basic models to predict inflows. But with the increasing variability in weather patterns, these traditional methods are becoming less reliable. AI can process large amounts of data from various sources – like weather forecasts, snowmelt predictions, and historical inflow records – to create more accurate and timely inflow forecasts. This allows operators to manage reservoirs better, optimizing the balance between power generation and water conservation. Another practical application is in maintenance
planning and maintenance timing optimization. By using predictions of inflows as well as power market prices or feed-in tariffs, operators can plan maintenance work in an optimal way that minimizes revenue loss. It also helps them make informed decisions about typical trade-offs. For example, maintenance timing optimization can help operators to make a decision on if it is worthwhile to schedule a maintenance during a weekend – when there will be higher labour costs – in order to reduce downtime costs (since power prices and inflow may be lower on the weekend). By seeing the cost of both options clearly side- by-side, such decisions can be made in a fully informed and quantitative way. Production planning is also a critical area where AI
makes a difference. For hydropower plants with storage capabilities, it’s essential to plan production not just for the immediate future but also weeks or months ahead. AI helps by optimizing these plans, taking into account factors like expected inflows, market prices, and environmental requirements.
Can you explain the concept of digital hydro control room? The hydro control room concept is something that has been around for many years. Traditionally, these control
10 | September 2024 |
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rooms were filled with analogue dials and gauges that operators used to monitor and control plant operations. While the technology has evolved, the basic concept remains the same: it’s the central hub for real-time management of hydropower plants. What we’re doing now is bringing this concept into the digital age. The digital hydro control room is not just about replacing those analogue dials with digital screens; it’s about adding layers of predictive intelligence. This means that operators can not only see what is happening right now but also get insights into what is likely to happen in the near future. With AI integration, we can move from reactive management – responding to problems as they occur – to proactive management, where we can foresee potential issues and optimize operations before they become problems. This shift significantly improves operational efficiency and reduces the likelihood of human error, making the entire process more streamlined and effective.
How does HYDROGRID’s platform contribute to improving environmental compliance and sustainability? Hydropower is a unique energy source because it’s so closely tied to natural water resources. The water used for power generation is also needed for drinking water, irrigation, and flood control, so there is a responsibility to manage it wisely. Regulatory bodies impose various environmental requirements to ensure that hydropower plants operate in a way that minimizes their impact on the environment.
HYDROGRID’s platform helps operators meet these
environmental compliance requirements by offering advanced tools for planning and real-time management. For instance, during extreme weather events such as heavy snowmelt, monsoons, or El Niño conditions, our platform can predict the impact on water inflows and help operators plan accordingly. This ensures that water is managed efficiently, minimizing spillage and environmental impact, while also maximizing power generation.
It’s been noted that AI and machine learning can increase power generation by up to 10%. Can you explain how this is achieved? There are two main pathways through which data science and machine learning can contribute to increasing power output in hydropower plants. The first pathway involves optimizing turbine efficiency. With changing inflow patterns, especially for power plants with multiple parallel turbines, there’s an optimal way to distribute water across turbines at any given time
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