AI & machine learning | 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
Below: Cockpit view of HYDROGRID Insight - Enabling proactive management and strategic decision-making 24/7
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.
hydrogrid.ai/
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.
12 | September 2024 |
www.waterpowermagazine.com
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