Hydropower producers face various challenges. These include fluctuating water resources due to irregular rainfall and drought periods, as well as the resulting greater energy price fluctuations and necessary trading adjustments.

Reactive and manual power plant scheduling that relies on traditional assumptions instead of advanced data-driven decision-making reflects an outdated structure.

AI models and learning algorithms help energy producers reliably supply their services amidst environmental and market fluctuations by predicting and responding to weather and grid requirements. This improves decision-making and enables a more efficient energy supply.

Discover how Austria's largest hydropower producer improved power plant scheduling in collaboration with craftworks through a data-driven approach, achieving greater efficiency and flexibility.

Agentic AI improves data-driven power plant scheduling by adjusting generation plans based on real-time data using advanced machine learning techniques.

By using real-time weather data and integrating the models developed for Agentic AI with satellite and IoT sensor data, water inflows for power plants can be accurately predicted. Agentic AI optimizes reservoir management and coordination with storage systems, ensuring more efficient use of water resources while maintaining grid stability.

The system learns from historical patterns and real-time data, coordinating the operation of individual as well as multiple dams, thereby maximizing energy yields.

Unused energy generation potential is released through faster adjustments to market fluctuations

Traditional upstream water storage and inflow predictions which are based on static calculation models often lead to suboptimal energy generation. However, as the number of electronic devices and power consumption in homes, offices and industry increases, along with environmental risks, it is essential to optimize the time schedules for energy productions. The efficient use of water resources reduces operating costs and supports water-saving measures, contributing to sustainability.

AI models and learning algorithms make predictions about market price developments based on predictive resource availability analyses. This enables a predictive response for energy production in alignment with expected demand shifts.

Insufficient coordination between production plans and trading strategies, slow response times to market dynamics, and manual plan adjustments lead to increased market price fluctuations.

Without improved integration of production and trading, financial risks grow. AI provides powerful tools that synchronize energy production and trading end-to-end, largely leveling out outliers in pricing and demand spikes. Improved planning accuracy minimizes trading losses and increases profitability.

Agentic AI for Downstream Market Trading Optimization

Agentic AI optimizes upstream energy generation potential by enhancing water storage management and inflow predictions. By aligning energy production schedules more effectively with trading strategies, AI brings out the best revenues during market fluctuations.

Agentic AI systems monitor energy markets on their own

Agentic AI predicts price trends by applying advanced machine learning models. It adjusts trading strategies by simulating market scenarios to identify profitable opportunities and mitigate risks.

Such intelligent software goes beyond automation – it learns and adapts. Through high-quality predictions and decisions based on complex data, Agentic AI can coordinate and adjust sophisticated systems and workflows. By learning from past outcomes, it improves its strategies without interruption, helping it stay flexible in a changing environment.

Values and Benefits of an AI-supported energy value chain

The values and benefits of an AI-powered energy value chain include more efficient energy trading and reduced financial risks. Water resources are better balanced despite changing precipitation levels and drought periods, helping to mitigate energy price fluctuations caused by resource variations.

Traditionally, the management of energy production, water resources, and trading operations have formed functional silos. These silos often lead to waste, missed opportunities, and suboptimal outcomes. For example, hydropower plant operations may not align with upstream water forecasts or downstream trading. This mismatch can create bottlenecks that hinder overall performance.

This is one example of how Agentic AI comes into play, solving complex tasks by connecting systems and ensuring high-quality, data-driven decisions. It synchronizes information, optimizes the energy value chain, enhances efficiency, and minimizes unnecessary delays.

Agent-based AI integrates upstream water forecasting, real-time power plant operations, and downstream trading activities. This seamless system continuously optimizes itself through learning across the entire value chain. Agentic AI coordinates data exchange between systems and adapts operations based on changing conditions and market signals.

Embracing Agentic AI for a Smarter Energy Future

Adopting Agentic AI brings a range of benefits specifically tailored to the energy sector:

• Holistic Efficiency Gains: Integrating Agentic AI across the entire operation ensures that each element works together, improving efficiency and reducing waste.

• Optimized Energy Yield: The system consistently adjusts to ensure energy production meets peak demand, even with changing conditions.

• Increased Profitability: Streamlined processes and real-time adjustments enhance market positioning, driving higher returns.

• Reduced Complexity: Self-regulating processes monitored by humans enable operators to focus on strategic decisions. AI can also take over routine work so that employees can concentrate on more demanding tasks.

Are you ready to harness the power of this revolutionary technology? Discover how Agentic AI can transform your operations and help you lead in energy management. Reach out to learn how we can support your success.