The notion of digital twins is no longer confined to aerospace, manufacturing or smart factories. In hydropower, digital twins are emerging as a key tool for modernising ageing assets, optimising energy production, improving maintenance, and even supporting environmental and water-management planning. At its core, a digital twin couples detailed engineering models of a hydropower facility with real-time data from sensors. That virtual counterpart then tracks actual operation, highlights deviations, predicts failures, or even simulates alternative operating scenarios without touching the real facility.
Such capabilities are becoming especially valuable given the age of many hydropower fleets. In the US, the average hydropower plant is over 60 years old.
Digital twins offer a cost-effective way to modernise those assets, often without major modifications to physical infrastructure.
Early adoption: success stories from Norway and the US
One of the most recent milestones in European hydropower digitalisation comes from the ReHydro project, an EU-funded initiative involving utilities, researchers and industry partners. In April 2025, ReHydro announced the launch of a real-time “Virtual Powerplant” for Røldal-Suldal Kraftverkene in Norway. The system comprises two parallel digital twins: one mirrors the plant’s real-time performance (tracking flow, head loss, overall efficiency), while the other allows operators to run “what-if” simulations – for example, preparing staff for emergency scenarios without risking real-world systems.
Importantly, ReHydro’s implementation relies on a cloud-based data architecture using an Azure database rather than traditional industrial I/O protocols. The system collects data from many datapoints across the hydropower complex, offering a rich, plant-wide perspective. Latency is reportedly kept to a manageable 15–60 seconds – a trade-off for the broad flexibility cloud-based infrastructure provides.
Across the Atlantic, the Pacific Northwest National Laboratory (PNNL) and Oak Ridge National Laboratory (ORNL) have also developed custom digital twins for hydropower facilities, with promising results. In projects such as Alder Dam (Washington state) and Rocky Reach Dam, operators now use physics-informed neural networks trained on real operational data to monitor performance, simulate scenarios, and help plan maintenance, all via web-accessible dashboards.
Key benefits have already been recorded: high-resolution, real-time monitoring; early detection of deviations and potential failures; predictive maintenance that can reduce unplanned outages; and capacity to simulate alternative operating strategies before implementing changes.
From hydropower to watersheds
While hydropower has embraced digital twins at the plant level, other sectors are applying the same concept to larger systems, such as watersheds, rivers, and national water-resource networks.
In China, for example, the government is integrating digital twin technology into its water governance and conservancy infrastructure. According to a 2024 report by Swissnex in China, China has launched dozens of pilot digital twin programmes across reservoirs, river basins, flood defenses, and irrigation systems. These virtual water systems integrate data from hydrological stations, precipitation sensors, satellites, radars and more, creating a real-time, dynamic “digital twin” of the water network’s state.
By 2025, the plan is to have a comprehensive, continually updated national water-conservation map capable of running simulations for flood control, drought mitigation and resource allocation across potentially thousands of water bodies. One flagship example is the Xiaolangdi Water Conservancy Project on the Yellow River. There, a digital twin dam model was used to run flood-control drills, amplifying a known 2021 flood scenario by 10 % to stress-test the system and develop emergency response proposals for operators.
Why digital twins matter and what they deliver
- Improved operations and asset optimisation: For ageing hydropower plants, digital twins offer a way to modernise operations without major physical upgrades. Operators can monitor performance in real-time, spot inefficiencies, test alternative regimes (e.g. different flow rates or turbine settings) and predict which maintenance tasks are critical, minimising downtime and maximising output.
- Predictive maintenance and reliability: By integrating sensor data, control-system outputs and detailed models of mechanical and hydraulic behaviour, digital twins can detect early signs of wear, vibration anomalies, abnormal flows or cavitation risk, long before it leads to equipment failure or downtime. This not only reduces maintenance costs but avoids unplanned outages and improves safety.
- Simulation and training without risk: Simulating “what-if” scenarios in a virtual twin enables operators to rehearse procedures, optimise responses, and train staff, all without endangering people or equipment, or interrupting production. The dual-twin setup at Røldal-Suldal enables exactly that.
- Better water and environmental management: Extending digital twin applications beyond the plant, into water basins, river systems, reservoirs etc, opens the door to integrated water resources management. As demonstrated in China, real-time, system-wide digital twins enable flood risk management, drought response, water allocation planning, and climate-resilience modelling, functions that complement hydropower operations.
- Flexibility, scalability and modernisation pathway: Digital twins don’t necessarily require replacing hardware; many implementations leverage existing infrastructure and simply augment it with sensors, data collection, and modelling. This makes them particularly attractive for operators of older dams who may lack the capital for large-scale refurbishments, but need smarter, more reliable, data-driven operations.
Challenges and practical constraints
Of course, like any technology, digital twins for hydropower face several challenges.
- Data infrastructure and latency: As seen at Røldal-Suldal, using cloud-based databases can introduce latency (15–60 seconds). For many monitoring and control tasks this may be acceptable – but for real-time safety-critical operations, it could pose a risk.
- Complexity of modelling: A full hydropower plant includes hydraulic, mechanical, electrical and control subsystems, each requiring accurate physics-based models. Some academic work shows that building comprehensive digital twin models remains difficult, especially for complex plants.
- Sensor reliability and data integration: A digital twin is only as reliable as the data fed into it. Sensor malfunctions or mis-calibration can compromise output. Some research uses the twin itself to detect sensor anomalies, but this requires careful calibration and validation.
- Scaling beyond individual plants: Extending the twin concept from single hydropower plants to entire watersheds or national water systems introduces significant complexity: interoperability, data governance, consistent sensor networks, cross-agency collaboration, and often very high computational demands.
- Resource requirements and institutional inertia: Despite long-term benefits, initial investments (in sensors, data platforms, modelling effort)and the challenge of adapting legacy systems may deter some operators. The institutional change required to integrate digital twins into routine operations also should not be underestimated.
The road ahead
Emerging literature and recent projects suggest that digital twins will not only stay relevant but increase in importance, as hydropower systems evolve and integrate with other renewables, storage and grid management.
A 2025 review of digital twins in renewable energy systems notes a growing interest in using DTs across solar, wind, hydro and hybrid plants, covering all phases from design and commissioning, through operations and maintenance, to end-of-life optimization. In addition, the combination of digital twins with advanced control algorithms – such as reinforcement learning – could enable smarter, adaptive management of hydropower assets and their integration with other grid resources.
For example, a future-proof “standard architecture” for power-system digital twins might support not only plant-level monitoring but also grid-wide coordination, hybrid energy mix planning, environmental management, and long-term sustainability assessments.
Finally, by coupling hydropower digital twins with water-management twins at basin or national scale, as demonstrated in China, operators and regulators could gain a holistic view: generation, resource availability, flood and drought risks, and climate-driven variability.
Digital twin technology is proving to be more than just a buzzword in hydropower, it is rapidly becoming a practical tool for enhancing efficiency, reliability, resilience and environmental stewardship.
Whether at a single plant like Røldal-Suldal, or across entire river basins and water infrastructures as in China, or as part of modernisation efforts in the US, digital twins offer operators the chance to see and manage their systems in a whole new way. They combine real-time monitoring, anomaly detection, predictive maintenance, and scenario simulation, enabling safer, smarter, and more adaptive hydropower operations. For an industry facing aging infrastructure, climate uncertainty and growing demands, digital twins may be one of the most powerful tools in the modernization toolbox.
But implementation must be thoughtful: ensuring reliable sensors, robust data architecture, accurate modelling, and operational integration. As research and pilot projects continue, the hope is that digital twins will evolve from pilot tools into standard practice – helping to usher hydropower into a more sustainable, efficient and flexible future.
References
Virtual Powerplants for Monitoring and Control – A digital twin for Røldal-Suldal Kraftverkene
How Digital Twins Can Transform Hydropower Operations
How digital twins are transforming China’s water management
