The development of digital technologies can improve the effectiveness of monitoring and prediction of hydrotechnical structures, enhancing energy security by minimising the risk of blackouts and improving the operation of facilities and their immediate surroundings.
As Machalski et al explain in Energies, the implementation of a digital twin – the technology that integrates a physical object, its digital representation, and the dynamic interactions between them – has become a widely adopted approach. This adaptability allows for rapid responses to operational changes and failures while providing a safe virtual environment for testing new system solutions and optimising processes. Furthermore, data generated by the digital model supports advanced technological and engineering workflows across various stages of the facility’s lifecycle and its components.
Polish hydropower
The Wały Śląskie Hydropower Plant is a run-of-river facility on the Odra River in Poland. Its location on Poland’s second longest river, with a basin covering 124,049 km2 across Poland, the Czech Republic, and Germany, underscores its importance in both the regional energy landscape and the broader river navigation system.
Indeed the Wały Śląskie scheme forms an integral part of the energy and shipping dam at Brzeg Dolny, serving dual functions of navigation support and energy production since its commissioning in 1958.
At the heart of the facility are four Kaplan turbine hydro sets with vertical axes, each with a rotor diameter of 4m. These turbines are comprehensively equipped with speed regulators, pump aggregates, and servomotors. The generators, manufactured by CKD Praha and CKD Blansko, are housed within a monolithic reinforced concrete construction. Each individual turbine operates with a nominal discharge of 60m3/sec under a nominal head of 4.7m, delivering 2.43MW of power. Collectively, the units provide the plant with a total installed capacity of 9.72MW and a maximum installed discharge of 240m3/sec.
Previously the plant relied upon basic monitoring equipment, including vibration sensors, rotational speed sensors, guide vane position sensors, turbine blade position sensors, electrical value meters at generator outputs, and temperature sensors for the hydro units. Despite this instrumentation, the facility faced significant limitations in data management and operational optimisation. Most parameters were not being recorded, and readings were displayed at multiple disconnected locations throughout the plant without unified software. The existing measurement infrastructure required thorough verification due to concerns about service life and operational reliability.
Although the plant’s theoretical production potential is calculated at 47,984.7MWh annually, its actual performance typically falls 5% below this figure, highlighting opportunities for efficiency improvements through the D-HYDROFLEX digital twin implementation.
Creating a digital twin
The process of creating a digital twin for the Wały Śląskie Hydropower Plant was developed as part of the D-HYDROFLEX project.
This project is developing a comprehensive suite of digital tools aimed at enhancing the flexibility and sustainability of hydropower plants across Europe, which are being tested at various demonstration sites to ensure their effectiveness in different operational environments.
The D-HYDROFLEX consortium comprises 17 partners from seven European countries, including five power plant operators/energy producers, six European research institutes and universities, and seven technology providers. This multidisciplinary collaboration ensures that the developed solutions are robust, scalable, and applicable across diverse operational contexts.
Through the D-HYDROFLEX project’s digital twin implementation, the Wały Śląskie plant aimed to optimise operations, enhance maintenance protocols, and maximise energy production from the existing infrastructure without negative environmental impacts.
Development of this digital twin required a systematic approach to address the unique challenges posed by the existing infrastructure, operational demands, and limited initial data availability. The preparation phase included detailed site assessments, stakeholder consultations, and strategic planning to ensure the feasibility and alignment of the digital twin with the plant’s operational goals.
By adopting a scalable, modular approach, this conceptual architecture can be adapted to other hydropower installations, thereby advancing the broader application of digital technologies within hydropower generation. To maximise the effectiveness and replicability of the hydro system digital twin, the authors recommend that future implementations prioritise the early integration of standardised data acquisition systems and documentation practices. Establishing clear protocols for sensor calibration, data validation, and model updates will enhance long-term system reliability and comparability across installations. Additionally, fostering collaboration between hydropower operators, researchers, and technology providers can accelerate the development of modular diagnostic tools. While investment in training for plant personnel on digital twin functionalities and decision-making based on simulation results is also advised to fully realise the operational benefits of the system
Bosnia and Herzegovina
A digital twin approach has also been used to quantify the durability and failure risk of concrete gravity dams by integrating advanced numerical modelling with field monitoring data.
Building on a previously developed finite element model for dam-reservoir interaction analysis, Emina Hadzalic and Adnan Ibrahimbegovic’s research published in Computation, extends to the assessment of existing, fully operational dams by using digital twin technology. The Salakovac dam in Bosnia and Herzegovina is used as a case study.
Construction of the Salakovac Dam began in early 1977. It was completed in December 1981 and became fully operational in 1982. Since then, the 70m high concrete gravity dam has been consistently operating at full capacity.
With an average annual energy production of 356.6GWh, the installed capacity of the Salakovac Dam is 208.5MW. Along with the Grabovica Dam, Jablanica Dam, and Mostar Dam, the Salakovac Dam forms a chain of hydropower facilities on the Neretva River that play a significant role in the country’s electrical energy production.
With construction of the Salakovac Dam dating back to 1977, rehabilitation and modernisation of the monitoring system was undertaken in 2012, which included the installation of new measurement equipment. This includes thermometers for monitoring the concrete temperature; one thermometer for monitoring the water temperature in the reservoir installed upstream at an elevation of 102.33masl; one thermometer for monitoring the air temperature; as well as automatic monitoring of horizontal displacements of the dam.
The air temperature, water temperature, concrete temperature, and horizontal displacement of the dam are recorded automatically every two hours, whereas the horizontal displacements of the dam at the alignment points are measured manually once a month.
Evaluation
Integrating advanced finite element modelling and monitoring data to construct Salakovac Dam’s digital twin enabled evaluation of the dam’s response under both long-term operational and transient extreme conditions. Despite the limited availability of monitoring data, the authors say obtained error metrics confirm a good overall agreement between the modelled and observed responses, reinforcing the reliability of the model. Furthermore, the ability of the model to evaluate the failure threshold and capture the associated failure mode under increasing lateral loads is also demonstrated, illustrating its potential for safety assessments under earthquake events.
The authors say their approach presented in this study can be adapted and applied to other dams, serving as a learning tool for further refinement and enhancement of the proposed approach based on the specific characteristics and requirements of each case. This adaptability ensures that the numerical framework can be continuously improved, making it a versatile tool for assessing current condition and predictive analysis across various dam structures and conditions. For instance, future research will aim to apply the model to analyse the performance of the dam under various representative earthquake ground motions, accounting for cyclic behaviour, high-frequency excitation, and potential resonance effects.
Overall, Emina Hadzalic and Adnan Ibrahimbegovic believe their study provides a robust numerical framework for dam–reservoir interaction modelling for use in monitoring structural health, safety assessment, and the predictive analysis of ageing dams.
References
Machalski,A.;Szulc, P.; Błonski, D.; Nycz, A.; Nems, M.; Skrzypacz, J.; Janik, P.; Satława, Z. The Concept of a Digital Twin for the Wały Slaskie Hydroelectric Power Plant: A Case Study in Poland. Energies 2025, 18, 2021. https://doi.org/10.3390/ en18082021
Hadzalic, E.; Ibrahimbegovic, A. Quantifying Durability and Failure Risk for Concrete Dam–Reservoir System by Using Digital Twin Technology. Computation 2025, 13, 118. https://doi.org/10.3390/ computation13050118
Important questions
As Mark Macaulay and Alec Cameron from Dentons discuss, the use of digital twins also raises important legal and contractual questions and may even increase the need for robust legal frameworks. Areas of potential concern include ownership of the model, liability when predictions fail, and admissibility of digital records in disputes.
They give the example of the Punt dal Gall Dam which straddles the borders of Switzerland and Italy. Although the dam’s digital twin improved earthquake resilience and extended asset life, researchers noted regulatory conflict between Switzerland and Italy over data use. The case shows how cross-border infrastructure requires alignment not just of engineering standards, but also of data law and liability regimes.
Source: Dentons – Smart dams and digital twins: re-imagining water infrastructure in the age of AI
