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 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 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 to maximize power output. HYDROGRID Insight supports this short-term optimization, which can lead to a 5-10% increase in power output, given typical turbine efficiency patterns
The second pathway is specific to flexible power plants with storage capabilities. Here, the key to increasing power output is avoiding unnecessary water spillage. We need to manage the water level in reservoirs intelligently and proactively. This means shifting from a reactive approach (e.g., “It’s raining, let’s increase power output”) to a pre-emptive one (e.g., “It’s going to rain tomorrow, let’s drain the reservoir today to catch all that water for later use”). This approach not only benefits power generation but also enhances safety aspects.
Interestingly, these strategies align the interests of power producers, the environment, and consumers. By implementing intelligent production planning and water management, we increase the revenue of power generators, make operations safer, support grid stability, and provide power when it’s most needed by consumers. It’s a rare case where interests are perfectly aligned across all parties.
Some practical examples illustrate these benefits:
- In India, we helped a hydro operator safely manage their hydropower plant during monsoon season. The plant is located upstream from a large city, and any spillage from the dam could damage the surrounding area. Our intelligent production planning not only improved efficiency and revenue but also increased safety for the local population.
- In the UK, we assisted the country’s largest hydro producer in optimizing their maintenance planning. We integrated data from multiple systems into a single source of truth, allowing the entire organization – from control room operators to maintenance personnel – to access unified information. This optimization minimized downtime and revenue loss while supporting grid stability by scheduling maintenance during low-demand periods.
- In Norway, we supported a mid-sized utility with managing water in their complex cascade system. Previously, their shift team needed to be on call 24/7, often waking up at night to manually intervene when water levels were too high or low. By automating much of this intervention management with HYDROGRID Insight, we reduced the number of nighttime wake-ups by about 95%, making it the exception rather than the norm. This greatly benefited the shift team, allowing them to be more refreshed and energized for daytime decision-making.
These examples demonstrate how our solutions can have a significant impact, especially for companies managing complex, multi-plant systems.
Your platform offers real-time optimization. How does machine learning enable this, and what benefits does it bring to hydropower?
In the past, planning hydropower generation was relatively straightforward. Ten or twenty years ago, you could create an annual plan for a hydropower plant with only minor adjustments needed throughout the year. For run-of-river plants, the strategy was simple: generate when there’s abundant water. For storage or pumped storage plants, the pattern was predictable: run during the day, pump at night. This yearly plan would typically remain fairly accurate.
However, the situation has changed dramatically. As we discussed earlier, weather patterns have become much more unpredictable. Additionally, power markets worldwide are liberalizing, leading to increasingly short-term operations. Depending on the region, we now see not just day-ahead markets, but also intraday markets and grid ancillary services. Some of these markets operate on 15-minute intervals or even shorter timeframes.
This new landscape makes it extremely challenging to make optimal decisions in real-time, 24 hours a day, without the support of machine learning. That’s why we developed our proprietary intelligent planning algorithm called HIRO. This algorithm is designed to support users in navigating these complex decisions.
A key feature of our algorithm is its versatility. It can manage everything from simple run-of-river power plants to complex cascades within a single integrated production planning system. We use a divide-and-conquer approach to solve the optimal planning problem, which significantly increases computational speed. For the technical details, there is a comprehensive white paper available on https://www.hydrogrid.ai/.
The increased computational speed is crucial as it enables real-time optimization. When new sensor data is received, we can instantly compute its impact on our inflow forecast and optimal production plan. Within less than 15 minutes, we can generate an updated plan that optimizes for the new situation.
This capability provides hydro operators with a significant competitive advantage. It allows them to react in real-time to events as they unfold, adapting their operations to maximize efficiency and output in an increasingly dynamic environment.
What advancements in AI and machine learning do you foresee having the most significant impact in the coming years?
The most significant trend we’re likely to see in the coming years is the widespread adoption of AI and machine learning in hydropower operations. Currently, if we look at the global picture, not just Europe or the US, I’d estimate that less than 25% of hydropower operators are using these technologies in any significant capacity within their organizations. However, looking 10 to 15 years into the future, I anticipate this figure will rise to around 85%.
The driving force behind this shift is the increasing unpredictability of weather patterns and power markets. Simply put, AI and data science will become necessary for hydropower operators to remain competitive. It’s akin to a technological arms race – those who don’t embrace these new technologies will find themselves at a disadvantage in the market, competing against operators who have already adopted these advanced tools.
Moreover, I believe the use of AI and machine learning will increasingly become a regulatory requirement. Regulators responsible for flood safety management or environmental hydro compliance are likely to demand that hydropower operators use certain types of predictive planning tools. This is because, as we discussed earlier, these technologies not only increase safety and compliance for the hydropower plant itself but also for the surrounding environment.
This trend aligns well with the hydropower industry’s efforts to become more sustainable and meet guidelines set by organizations like the International Hydropower Association (IHA). The implementation of AI and machine learning supports sustainability in multiple ways:
Firstly, hydropower is already a green, nearly carbon-free form of energy generation. By using AI to increase power output from a hydropower plant while using the same amount of water, we’re further contributing to sustainability goals. For instance, increasing power generation by 10% through optimized operations has an immediate positive impact on Sustainabile Development Goal 7 – Affordable and clean energy.
Secondly, better water management through AI can support flood management, irrigation, and provide reliable water sources for local communities. This contributes to Sustainable Development Goals 2, 6, 11, and 14 by ensuring access to clean drinking water, enabling agriculture, safeguarding communities downstream and supporting local ecosystems.
In essence, the adoption of AI and machine learning in hydropower isn’t just about improving operational efficiency – it’s about creating a more sustainable and responsible industry that can better serve both environmental and community needs.
What strategy is HYDROGRID employing to expand its global presence and adapt to the different regional markets?
HYDROGRID is currently active on three continents: Europe, Asia, and South America, with plans to enter North America soon. The company understands that the needs of hydropower operators vary significantly across different regions, influenced by factors such as market structure and regulatory requirements. For instance, some areas have liberalized power markets, while others operate under central or demand-driven dispatch systems. Additionally, the obligations to notify grid operators and the environmental regulations can differ widely from one region to another.
Despite these variations, hydropower operators around the world face common challenges. Weather uncertainty and inflow forecasting are universal concerns, whether it involves snowmelt, monsoons, or El Niño events. Similarly, optimizing maintenance planning to minimize revenue loss is a priority shared by operators globally.
To address the diverse needs of this global market, HYDROGRID has developed a modular product strategy. At its core is a basic data platform, which serves as the foundation for specialized modules focused on inflow planning, maintenance planning, and production planning. This modular approach allows hydropower operators to customize their solutions by selecting the modules that best fit their specific requirements while seamlessly integrating them into their existing IT infrastructure.
The beauty of this design is that, although the modules can be used independently, they work together as a cohesive unit. For example, when a maintenance event is scheduled, the production planning module automatically adjusts to reflect this change. Conversely, if operators need to determine the optimal timing for maintenance to minimize revenue loss, the system can utilize the existing production plan to provide precise financial impact estimates.
Through this strategy, HYDROGRID effectively addresses the diverse needs of hydropower operators worldwide while maintaining a cohesive and efficient product offering.
