Search Results (184)

Developing digital twins for legacy hydropower units is difficult when turbine documentation, calibrated performance data, and integrated measurements are incomplete. This study presents a Computational Fluid Dynamics (CFD)-assisted reconstruction workflow for a Kaplan turbine at the Wały Śląskie Hydropower Plant and evaluates its use as a physics-informed foundation for a digital twin. The flow passage was reconstructed from archival documentation, direct measurements, and optical 3D scanning of the runner. A steady-state Reynolds-averaged Navier–Stokes model was then prepared in OpenFOAM v2506 for selected head levels, guide-vane openings, and runner-blade angles. The simulations determined hydraulic performance, flow-field structures, and combinatory characteristics of the double-regulated turbine. The computed hydraulic efficiency reached approximately 85% in the nominal-head range, and the highest-efficiency region formed a broad plateau rather than a sharp optimum. CFD-derived and measurement-derived combinatory trends were consistent, although absolute values remain limited by relative field measurements and uncalibrated Winter–Kennedy flow estimation, a differential-pressure-based method. The CFD results were reduced to compact response surfaces and integrated with reconstructed geometry into an advisory digital twin for operating-point assessment, visualization, documentation, and training. This study establishes a robust workflow for this specific Kaplan turbine case where reverse engineering, integrated with CFD analysis, generates high-fidelity surrogate models for hydropower digital twins, effectively addressing the challenge of incomplete legacy documentation. Full article

Reliable short-term hydropower forecasting is essential for dispatch planning and early electricity procurement in snowmelt-influenced power systems. This study develops a leak-free operational forecasting framework using quality-controlled hourly generation and hydro-meteorological records from eight hydropower plants in Kazakhstan. Two tasks are addressed: deterministic multi-step forecasting for D+1–D+7 and uncertainty-aware envelope forecasting for D+8–D+14 using MIN and Q90 targets. The benchmark uses Persistence as the primary baseline, against which RIDGE, SARIMAX, Random Forest, HistGradientBoosting, MLP, and LSTM are compared using Nash–Sutcliffe efficiency (NSE), root mean squared error (RMSE), and mean absolute error (MAE). For D+1–D+7, the results reveal strong cross-station heterogeneity and the expected decline in skill with increasing lead time. In the aggregated comparison, SARIMAX achieves the highest mean NSE at D+1 (0.903), while RIDGE becomes strongest by D+7 (0.625), both outperforming Persistence (0.534 at D+7). At the station level, SARIMAX performs best for Kapch, Kask, Moin, Bukh, and Ustk, RIDGE is best for Shar and Lenin, and LSTM is best for Shulb. The strongest stations, Kapch and Kask, reach mean NSE values of 0.941 and 0.933, respectively, whereas Ustk and Bukh remain the most difficult cases. A central methodological contribution is a flood-sensitive switched hybrid strategy for Ust-Kamenogorsk based on an observed-generation high-flow window selected by a regime-score procedure. This strategy improves robustness at medium lead times: for SARIMAX, NSE increases from 0.587 to 0.739 at D+2 and from 0.161 to 0.559 at D+7, while for RIDGE, NSE increases from 0.549 to 0.701 at D+2 and from 0.109 to 0.435 at D+7, together with substantial RMSE and MAE reductions. For D+8–D+14, envelope forecasting remains informative, but model ranking becomes target-dependent: SARIMAX and RIDGE provide the strongest mean performance for MIN (0.664 and 0.658), whereas LSTM and RIDGE are strongest for Q90 (0.746 and 0.743). Overall, the results show that hydropower forecasting in Kazakhstan is best approached as a station-wise, regime-aware, and horizon-specific problem. Full article

A large number of reservoirs (or hydropower plants) have been constructed for flood control and energy production in the past several decades in the Yangtze River basin in China. The conventional scheduling rule curves (Scheme A) were designed in the reservoir construction period and did not consider river flow alternation, which needs to be modified to increase comprehensive benefits in the reservoir operation period. In this study, six large-scale cascade reservoirs or mega hydropower systems constructed and operated by the China Yangtze Three Gorges Corporation were selected for this case study. The current joint scheduling plans of cascade reservoirs (Scheme B) were introduced, and a joint scheduling and multi-objective coordinating operation model (Scheme C) was proposed for this mega hydropower system. The Gaussian radial basis functions (GRBFs) were used to fit operation policies of each reservoir, and the Borg multi-objective evolutionary algorithm was selected to optimize three-objective functions for Scheme C. The observed daily flow data series at main hydrometric stations from 2003 to 2025 were used to simulate and compare different operation scheduling schemes. The results show that the performance of joint scheduling of cascade reservoirs (both Schemes B and C) is much better than the single-reservoir scheduling (Schemes A) with overall benefit; Scheme C-best achieves a comprehensive target of decreasing average annual spillway wastewater by 12.82 billion m³ (or a decrease of 28.5%), increasing average annual power generation by 31.02 billion kWh (or an increase of 10.7%), and improving average annual impoundment efficiency rate by 5.0%. The GRBFs can fit reservoir operation policies well, while the Borg multi-objective evolutionary algorithm can quickly converge with high-precision non-dominated solution sets. The proposed joint scheduling and multi-objective coordinating operation model will provide a scientific basis for achieving maximum benefits in flood protection and hydropower generation for the mega hydropower system. Full article

The objective of this work is to present sustainability analysis and performance evaluation of six hydropower units through exergy-based indices. The method of exergy analysis, based on the first and second laws of thermodynamics, was utilized to evaluate system irreversibility and environmental impact. The Exergy Efficiency, Sustainability Efficiency Index (SEI), and Exergy Ecological Index (ECEI) were determined and plotted in MATLAB. The efficiency and exergy performance results show that Unit 6 had the highest exergy efficiency at 89.3%, and Unit 1 had the least at 82.1%. The values of SEI and ECEI showed that elevated exergy efficiency contributes to increasing sustainability and ecological performance in parallel. The results demonstrate that exergy analysis can provide a broader and more accurate measure of system performance than energy analysis in hydroelectric power systems. The approach shows that local reference environmental conditions must be incorporated to establish system equilibrium. It also suggests that exergy analysis should be used as a standard tool for the optimization and performance management of hydropower plants. Its integration would help the operators take measures against malfunction, minimize losses and improve the environmental and thermodynamic sustainability of energy systems. Full article

Forecasting hydropower plant power production is a great challenge in the context of maintaining power system stability, reliability and efficiency, especially in an age with variable renewable energy sources when demand for electricity is steadily rising. Accurate forecasting methods are a crucial enabler for the operational existence of power systems that rely on renewable sources. And while in the pursuit of increased accuracy of predictions, many recent research works rely on artificial intelligence and machine learning techniques, this study proposes and adopts a more conventional approach with standardized mathematical models to address the problem of hydropower production forecasting. The model predicts the runoff–power relationship. It starts with the normalization of different rain phenomena as a part of the statistical characterization of runoff events. The system transforms rain occurrence to runoff events via the USDA SCS CN model and then feature vectors are composed, which are used to generate kernel coefficients via interpolation. Contrary to models based on artificial intelligence, the proposed approach has several practical advantages requiring a minimal set of input parameters, which significantly reduces data preprocessing demands and allows for a straightforward integration into existing systems, thereby lowering the cost and the implementation and deployment time. Furthermore, the simplicity and universality of the model make it so that it can be adapted across a wide range of hydropower plants of varying scales and with diverse hydrological and meteorological conditions. The model’s performance and prediction accuracy are evaluated using empirical data records of time series over a five-year period for the meteorological parameters and production of an existing real-life hydropower plant in Bulgaria. The performance of the newly proposed model is assessed using widely accepted statistical error metrics, namely, Root Mean Square Error (RMSE), Mean Absolute Error (MAE), the Nash–Sutcliffe Efficiency (NSE) coefficient, and the Pearson correlation coefficient (R). These metrics provide a comprehensive assessment of the forecasts’ precision and effectiveness. The results show that the proposed model offers admissible accuracy with low computational effort. Thus, it can be successfully implemented in practice in a number of hydropower plant production forecasting applications. Full article

In pico-hydropower, the use of pumps as turbines is a cost-effective solution, especially for remote areas. The abundant seawater makes it a good fluid for pumped storage. The operation of centrifugal pumps in normal and reverse modes involves thickness loss because of solid particle concentration and vapor cavitation. Some research has been performed to predict cavitation in centrifugal pumps, but this issue still exists in several pico-hydropower plants. Therefore, to analyse the synergy between erosion and cavitation in a seawater centrifugal pump, we performed a CFD analysis to compute the effect of material mass loss due to erosion on cavitation risk. The Euler–Lagrangian method was used to track the released particles combined with the RNG k-ε turbulence model. The first part studied the effect of the surface mean roughness height (R_a) on the performance of the centrifugal pump. Increasing R_a from 0 to 15 μm decreases the pump hydraulic efficiency from 93% to 91%, respectively. The second analysis focused on the distribution of erosion thickness and its temporal evolution for 40 μm and 50 μm particles. For both the pump mode and the turbine mode, the erosion thickness is a polynomial function of power 2 with time. The most eroded regions are the blade leading edge (LE) and the blade trailing edge in pump and turbine mode, respectively. The last section focuses on analysing the effect of erosion thickness on cavitation damage. As the surface roughness increases, the cavitation damage power increases. The cavitation power risk increases from 111 kW to 156 kW in pump mode. In turbine mode, when the erosion thickness is between 0.0011 μm and 0.0022 μm, the cavitation damage is the same, approximately 170 kW, whereas the total gas distribution is uniformly distributed in the blade channel. With respect to seawater, the NPSHr increased compared with that with freshwater, from 3.35 m to 3.67 m. Full article

Hydropower remains one of the primary sources of power generation. It can be operated as either a base-load or peak-load plant due to its rapid, easy start-up and stop-down capability. However, power plants, old or new, need to be operated and maintained optimally to meet energy demand and maximise economic returns. While the older plants without digital controls such as the Supervisory Control and Data Acquisition (SCADA) system are unable to leverage the evolving technology including big data and Artificial Intelligence (AI), the newer plants or plants that already have some form of data acquisition system have the advantage of leveraging the newer platforms for efficient operation, monitoring and fault diagnosis. Thus, an Artificial Neural Network (ANN), a machine learning (ML) algorithm, was chosen for this case study to predict the generator’s operational stator temperature by selecting six parameters that could potentially affect it. Real data from the 336 MW Chhukha Hydropower Plant (CHP) in Bhutan were used to train the ANN. The prediction of temperature using an ANN in MATLAB^® yielded an R² (correlation coefficient) of 96.8%, which is impressive but can be further improved through various optimisation and tuning methods with increased data volume and complexity. The performance of ANN prediction was validated against other regression models, and the ANN was found to outperform them. This demonstrated its capability to predict and detect generator temperature faults before failures, thereby enhancing hydropower operation and maintenance (O&M) efficiency. The model’s interpretation was also done through Shapley Additive ExPlanations (SHAP). Full article

This paper investigates the optimization of maintenance strategies to improve the reliability of equipment at the Kapshagay Hydropower Plant (HPP), located in Kazakhstan. Operational data for the period 2020–2025 were analyzed to evaluate the effectiveness of existing maintenance systems. The analysis showed that the failure frequency of the main equipment averaged 3.8–4.2 events per year, while annual unplanned downtime reached 80–100 h, resulting in electricity generation losses of 2.5–3.2%. In addition, total maintenance costs were approximately 150 million KZT per year, with about 40% related to unplanned repairs. A reliability-centered maintenance model was developed using mathematical modeling and simulation tools such as Python 3.11 and SMath Solver 0.99.7920. The study integrates reliability theory, exponential failure modeling, and statistical performance analysis based on operational data from the Kapshagay HPP. Simulation-based validation was performed to compare baseline and optimized maintenance strategies under real operating conditions. After implementing the proposed model, equipment failure probability decreased by 15%, failure rate decreased by 28%, the mean time between failures increased from 120 days to 165 days, and repair duration decreased from 6 days to 4 days. Additionally, failure probability decreased from 0.10 to 0.07, while annual downtime decreased from 6.2 days to 4.1 days. Electricity generation losses decreased by approximately 18–22 GWh per year, while the annual economic benefit was estimated at 320–480 million KZTn. The results demonstrate that reliability-centered maintenance can increase equipment reliability by 20–30%, reduce maintenance costs by 10–12%, and improve electricity generation efficiency by 1.8–2.4%. The obtained results have practical significance for improving the technical and economic performance of hydropower plants. Full article

Hydropower plants remain strategic assets for grid stability and decarbonization, with hydraulic turbines governing conversion efficiency, reliability, and environmental performance. This systematic review synthesizes recent methodologies for evaluating and optimizing turbine operation and maintenance to enhance efficiency, reduce impacts, and extend service life. Following a PRISMA-aligned protocol, studies published between 2020 and 2025 were screened across Web of Science and Scopus, using predefined eligibility criteria and a two-stage selection process. The resulting evidence was thematically analyzed across three domains: lifecycle and circular-economy-oriented refurbishment strategies; digitalization and predictive maintenance approaches; and environmentally optimized operating regimes. Of the 115 screened records, 37 met the inclusion criteria. Findings indicate that predictive monitoring, data-driven maintenance, and turbine selection tailored to local hydrology can significantly improve energy performance while reducing operation and maintenance costs. The literature also highlights the importance of ecological flow compliance and reduced aquatic impacts. Complementary case studies from Nepal, Switzerland, Germany, Portugal, and Romania illustrate regional challenges and modernization pathways. Overall, the review underscores the need for integrated, multi-objective turbine management that aligns techno-economic, lifecycle, and ecological considerations to support hydropower competitiveness within the energy transition. Full article

This research evaluates the technical and financial feasibility of green hydrogen production in Colombia using Small Hydropower Plants (SHPs), positioning them as a strategic complement to intermittent sources such as solar and wind. To address an underexplored niche in the national hydrogen roadmap, the study applies a Real Options framework, specifically using a binomial tree model, and incorporates the Weibull distribution to estimate risk-adjusted discount rates. This methodological combination allows for the modeling of operational flexibility under uncertainty, particularly through the analysis of an American-style abandonment option. The results indicate that SHPs provide continuous power generation, enhance electrolyzer efficiency, lower the Levelized Cost of Hydrogen (LCOH), and improve cash flow. However, fiscal incentives and high initial capital costs remain limiting factors. The study proposes extending the evaluation horizon to 15 years and implementing mechanisms such as Capital Expenditures (CAPEX) subsidies to improve project viability. Overall, the research contributes to the diversification of Colombia’s energy matrix, encourages regional development, and supports the positioning of green hydrogen as a viable financial asset within the country’s energy transition framework. Full article

Managing frequency stability in modern interconnected power systems is a critical challenge, particularly under continuous load variations and increasing system complexity. In response to these challenges, this study introduces an Improved Grey Wolf Optimizer (IGWO)-based Proportional–Integral–Derivative (PID) controller as a solution for effective Load Frequency Control (LFC). The proposed method is tested on interconnected power systems integrating thermal (reheat and non-reheat) and hydropower plants. The simulations focus on continuous load variation and nonlinearity cases, where the GRC block is added in the model to closely mimic real-world operating conditions. The findings demonstrate that the IGWO-PID controller outperforms by achieving faster stabilization, minimizing frequency deviations, and ensuring robust performance compared to the Particle Swarm Optimization (PSO) algorithm. These results highlight the controller’s adaptability and scalability, offering a reliable approach to maintaining stability and operational efficiency in interconnected power systems. Full article

To enhance the sustainable power regulation capability of hydroelectric unit regulation systems (HURS) under modern power system requirements, this paper proposes an optimal linear quadratic regulator (LQR)-based power control strategy optimized using an improved Salp Swarm Algorithm (ISSA). First, comprehensive mathematical models of the hydraulic, mechanical, and electrical subsystems of HURS are established, enabling a unified state-space representation suitable for LQR controller design. Then, the weighting matrices of the LQR controller are optimally tuned via ISSA using a hybrid objective function that jointly considers dynamic response performance and control effort, thereby contributing to improved energy efficiency and long-term operational sustainability. A large-scale hydropower unit operating under weakly stable conditions is selected as a case study. Simulation results demonstrate that, compared with conventional LQR tuning approaches, the proposed ISSA-LQR controller achieves faster power response, reduced overshoot, and enhanced robustness against operating condition variations. These improvements effectively reduce unnecessary control actions and mechanical stress, supporting the reliable and sustainable operation of hydroelectric units. Overall, the proposed method provides a practical and effective solution for improving power regulation performance in hydropower plants, thereby enhancing their capability to support renewable energy integration and contribute to the sustainable development of modern power systems. Full article

Electricity generation is the largest contributor to anthropogenic greenhouse gas (GHG) emissions. This review synthesizes life cycle assessment (LCA) evidence for major power generation technologies published from 2015 to 2025. Using a structured screening approach, it identifies consistent cross-technology patterns and the methodological factors driving variation in reported results. Unabated coal and oil show the highest life cycle intensities; natural gas varies widely with methane management; and nuclear, geothermal, hydropower, wind, and solar power generally fall one to two orders of magnitude lower. Differences arise mainly from upstream processes, siting conditions, and system boundary definitions. Key mitigation levers include plant efficiency improvements, methane abatement, carbon capture and storage (CCS), and low-carbon manufacturing. The review also highlights how emerging policies—including the EU Carbon Border Adjustment Mechanism (CBAM) and China’s carbon-footprint standards—are integrating life cycle and Scope-2 accounting. Standardized, AR6-aligned LCA practices and transparent upstream data remain essential for credible, comparable electricity-sector decarbonization. Full article

The transition toward renewable-dominated power systems is increasingly constrained by the shortage of flexible regulation resources. Hydropower, with its rapid response and strong load-adjustment capability, remains a cornerstone for enabling large-scale integration of intermittent wind and solar energy. Splitter-blade runners are widely employed in medium- and high-head conventional hydropower plants and pumped-storage stations due to their broad high-efficiency operating range and superior stability. In this study, based on a runner replacement project at an existing hydropower station, refined computational fluid dynamics (CFD) simulations were carried out to design a splitter-blade runner under strict dimensional constraints. The optimized runner expanded the unit’s stable operating range from 50–100% to 0–100% rated power, while also improving overall efficiency and reducing pressure pulsations. The optimized splitter-blade runner improved efficiency by 1–2%, reduced pressure pulsations in the draft tube by ≈25%, and decreased the runner radial force by ≈12% compared with the baseline configuration. Importantly, this work demonstrates for the first time that splitter-blade runners can be successfully applied at head ranges below 100 m, thereby extending their applicability beyond traditional limits. The results provide both theoretical and practical guidance for flexibility retrofits of existing Francis turbine units in China, offering a feasible pathway to support the adaptability of future renewable energy systems. Full article

The lack of detailed design information in legacy hydropower plants creates challenges for modernising their ageing turbine components. This research advances a digitalisation approach which combines inverse design methodology (IDM) with multi-objective genetic algorithms (MOGA) and computational fluid dynamics (CFD) to digitally reconstruct and optimise the Bérchules Francis turbine runner and guide vane geometries using limited available legacy data, avoiding invasive techniques. A two-stage optimisation process was conducted. The first stage of runner blade optimisation achieved a 22.7% reduction in profile loss and a 16.8% decrease in secondary flow factor while raising minimum pressure from −877,325.5 Pa to −132,703.4 Pa. Guide vane optimisation during Stage 2 produced additional performance gains through a 9.3% reduction in profile loss and a 20% decrease in secondary flow factor and a minimum pressure increase to +247,452.1 Pa which represented an 183% improvement. The CFD validation results showed that the final turbine efficiency reached 93.7% while producing more power than the plant’s rated 942 kW. The sensitivity analysis revealed that leading edge loading at mid-span and normal chord proved to be the most significant design parameters affecting pressure loss and flow behaviour metrics. The research proves that legacy turbines can be digitally restored through hybrid optimisation and CFD workflows, which enables data-driven refurbishment design without needing complete component replacement. Full article