Search Results (81)

Southeast Asia relies heavily on hydropower from dams and reservoir projects, but this dependence comes at the cost of ecological damage and increased vulnerability to extreme events. This dilemma necessitates a choice between continued dam development and adopting alternative renewable options. Concerns over these environmental impacts have already led to halts in dam construction across the region. This study assesses the potential of run-of-river hydropower plants (RHPs) across 199 hydrometric stations in Mainland Southeast Asia (MSEA). The assessment utilizes power duration curves for the historical period and projections from the HBV hydrological model, which is driven by an ensemble of 31 climate models for future scenarios. Energy production was analyzed at four levels (minimum, maximum, balanced, and optimal) for both historical and future periods under varying Shared Socioeconomic Pathways (SSPs). To promote sustainable development, environmental flow constraints and carbon dioxide (CO₂) emissions were evaluated for both historical and projected periods. The results indicate that the aggregate energy production potential during the historical period ranges from 111.15 to 229.62 MW (Malaysia), 582.78 to 3615.36 MW (Myanmar), 555.47 to 3142.46 MW (Thailand), 1067.05 to 6401.25 MW (Laos), 28.07 to 189.77 MW (Vietnam), and 566.13 to 2803.75 MW (Cambodia). The impact of climate change on power production varies significantly across countries, depending on the level and scenarios. At the optimal level, an average production change of −9.2–5.9% is projected for the near future, increasing to 15.3–19% in the far future. Additionally, RHP development in MSEA is estimated to avoid 32.5 Mt of CO₂ emissions at the optimal level. The analysis further shows avoidance change of 8.3–25.3% and −8.6–25.3% under SSP245 and SSP585, respectively. Full article

A novel multi-scale copula-based model predictive control (MPC) method is proposed to address the core regulation challenges of runoff hydropower and distributed photovoltaic systems within high-penetration renewable energy grids. Complex spatio-temporal complementarity under ecological constraints and the limitations of conventional methods were critically analyzed. The core innovation lies in integrating copula theory with MPC, enabling adaptive spatio-temporal optimization and weight adjustment to significantly enhance the efficiency of complementary regulation and overcome traditional performance bottlenecks. Key nonlinear dependencies of water–solar resources were investigated, and mainstream techniques (copula analysis, MPC, rolling optimization, adaptive weighting) were evaluated for their applicability. Future directions for improving modeling precision and intelligent adaptive control are outlined. Full article

Hydropower is the main source of renewable energy and the most feasible for implementation in remote areas without access to conventional energy grids. Therefore, knowledge of actual, potential, and future perspectives of sustainable hydropower projects is decisive for their viability. This study aims to estimate the present and future potential capacity of Peru’s hydropower system and from the potential small hydroelectric plants, specifically Run-of-River class. First, we employed geospatial databases and hydroclimatological products to describe the current hydropower system and potential sites for Run-of-River projects. The findings identified 11,965 potential sites for Run-of-River plants. Second, we executed and validated a hydrological model to estimate historical daily streamflows (1981–2020) and hydropower parameters for actual and potential sites. It was determined there is an installed capacity of 5.2 GW in the current hydropower system and a total potential capacity of 29.1 GW for Run-of-River plants, mainly distributed in the northern and central Andes. Finally, we evaluated future changes driven by ten global climate models under three emission scenarios (SSP1-2.6, SSP3-7.0, and SSP5-8.5), compared with the baseline period of 1981–2010 with two future time slices. The main results about capacity indicated that operational hydroelectric plants (Run-of-River plants) are projected to decrease by 0.5 to −5.4% (−7.2 to −2.2%) during 2036–2065 and by −9.2 to 3.8% (1.8 to −11.9%) during 2071–2100. These outcomes provide relevant information to support policymakers in addressing sustainable development gaps in the coming decades and stakeholders involved in the implementation and mitigation of climate change impacts on hydropower projects in Peru. Full article

This study explores the application of Temporal Fusion Transformers (TFTs) to improve the predictability of hourly potential hydropower production for a small run–of–the–river hydropower plant in Portugal. Accurate hourly power forecasts are essential for optimizing participation in the spot electricity market, where deviations incur penalties. This research introduces the novel application of the TFT, a deep–learning model tailored for time series forecasting and uncovering complex patterns, to predict hydropower production based on meteorological data, historical production records, and plant capacity. Key challenges such as filtering observed hydropower outputs (to remove strong, and unpredictable human influence) and adapting the historical series to installed capacity increases are discussed. An analysis of meteorological information from several sources, including ground information, reanalysis, and forecasting models, was also undertaken. Regarding the latter, precipitation forecasts from the European Centre for Medium–Range Weather Forecasts (ECMWF) proved to be more accurate than those of the Global Forecast System (GFS). When combined with ECMWF data, the TFT model achieved significantly higher accuracy in potential hydropower production predictions. This work provides a framework for integrating advanced machine learning models into operational hydropower scheduling, aiming to reduce classical modeling efforts while maximizing energy production efficiency, reliability, and market performance. Full article

The sediment accumulation behind dams and cross-river structures reduces storage capacity, increases pool water level, reduces hydropower production, and causes damage to the blades of turbines. The operation of the impoundment hydropower and run-of-river plants is affected by the sediment accumulation in the vicinity of their water intake. In this study, the effectiveness of sediment removal through an outlet in a model of cross-river structure was experimentally investigated. The model was fixed tightly at the end of a 2 m working section in a laboratory flume with a length of 12 m, a width of 0.3 m, and a depth of 0.45 m. To study the impact of main variables on scour volume (V_s), a total of 27 experiments were conducted. The studied variables were flow rate (Q), area (A_o), location of outlet centerline outlet from the bed (h_s), and uniformity of the sedimentation used in the mobile bed of the working section. For the same outlet area (A_o = 47.5 cm²), results show that when the flow rate increased from 3.2 to 6.3 l/s, the scour volume in nonuniform sediment was increased by twofold. However, the above increment caused the scour volume in uniform sediment to increase by only 170%. In addition, the scour volume in the mobile bed of uniform sediment was found to be greater than that in nonuniform sediment by an average of 17%. For a flow of 3 l/s and when the outlet area was reduced by either 25% or 50%, the scour volume in both uniform and nonuniform sediment was reduced by 46%. The accuracy of the proposed dimensionless multiregression model was statistically tested by calculating the Nash efficiency coefficient (NEC) and found to be 0.91, which confirmed the accuracy of the model prediction. The outcomes of the present study are useful to engineers involved in dam design and management. Full article

Wind and solar energy sources, while sustainable, are inherently variable in their power generation, posing challenges to grid stability due to their non-dispatchable nature. To address this issue, this study explores the synergistic optimization of wind and solar photovoltaic resources to mitigate power output variability, reducing the strain on local grids and lessening the reliance on balancing power in high-penetration renewable energy systems. This critical role of providing stability can be effectively fulfilled by run-of-river hydropower plants, which can complement fluctuations without compromising their standard operational capabilities. In this research, we employ a straightforward energy balance model to analyze the feasibility of a 100 MW virtual hybrid power plant, focusing on the northern region of Portugal as a case study. Leveraging actual consumption and conceptual production data, our investigation identifies a specific run-of-river plant that aligns with the proposed strategy, demonstrating the practical applicability of this approach. Full article

Spatially and temporally resolved data on electricity production from run-of-river plants are very useful to study various aspects of this renewable energy at both the local and regional scale. In the absence of disaggregated feed-in data from such power plants in Germany, it is necessary to apply numerical simulations to determine their electricity production for a desired region and time period. We show how a simulation model can be created using publicly accessible power plant data and information from transmission system operators as model input. The developed physical model is applied to an ensemble of 7974 run-of-river plants in Germany, including those with and without water storage facilities, to simulate their electricity production for the year 2021. The resulting and spatially aggregated simulation results correlate well with the official total electricity feed-in from run-of-river plants in Germany, as well as on smaller spatial scales such as the city of Hamburg. Such disaggregated time series can be used to assess the renewable hydropower generation at different spatial and temporal levels, as each power plant is simulated with its geographical and technical data. Moreover, this study presents the electricity production landscape of run-of-river power plants in Germany as a highly resolved map and at the federal state level with related energy indicators, which enables a better monitoring of this renewable energy. The obtained results also support the expectation that the existing run-of-river plants will play an important role in the future transformation and decarbonization of the German power sector. Full article

This article devises the Artificial Intelligence (AI) methods of designing models of short-term forecasting (in 12 h and 24 h horizons) of electricity production in a selected Small Hydropower Plant (SHP). Renewable Energy Sources (RESs) are difficult to predict due to weather variability. Electricity production by a run-of-river SHP is marked by the variability related to the access to instantaneous flow in the river and weather conditions. In order to develop predictive models of an SHP facility (installed capacity 760 kW), which is located in Southern Poland on the Skawa River, hourly data from nearby meteorological stations and a water gauge station were collected as explanatory variables. Data on the water management of the retention reservoir above the SHP were also included. The variable to be explained was the hourly electricity production, which was obtained from the tested SHP over a period of 3 years and 10 months. Obtaining these data to build models required contact with state institutions and private entrepreneurs of the SHP. Four AI methods were chosen to create predictive models: two types of Artificial Neural Networks (ANNs), Multilayer Perceptron (MLP) and Radial Base Functions (RBFs), and two types of decision trees methods, Random Forest (RF) and Gradient-Boosted Decision Trees (GBDTs). Finally, after applying forecast quality measures of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R²), the most effective model was indicated. The decision trees method proved to be more accurate than ANN models. The best GBDT models’ errors were MAPE 3.17% and MAE 9.97 kWh (for 12 h horizon), and MAPE 3.41% and MAE 10.96 kWh (for 24 h horizon). MLPs had worse results: MAPE from 5.41% to 5.55% and MAE from 18.02 kWh to 18.40 kWh (for 12 h horizon), and MAPE from 7.30% to 7.50% and MAE from 24.12 kWh to 24.83 kWh (for 24 h horizon). Forecasts using RBF were not made due to the very low quality of training and testing (the correlation coefficient was approximately 0.3). Full article

The study quantifies the benefits of expanding electric cooking in the residential sector in replacement of liquefied petroleum gas (LPG), including economic savings and the avoided emissions resulting from this transition, viewed through the perspective of a long-range optimal energy system model developed for the Ecuadorian energy system under the LEAP (Long-range Energy Alternative Planning) framework. In Ecuador, electricity generation is predominantly based on hydropower obtained from run-of-the-river schemes. The model results indicate that a sectorial-level policy to promote electric cooking reduces the use of LPG per annum, which consequently leads to reductions in greenhouse gas emissions. Additionally, the electric cooking scenario also complements the Ecuadorian vision of reducing deforestation and reaching carbon neutrality. Furthermore, the subsidies to LPG will be reduced, improving energy sovereignty. Finally, the paper discusses the effects and implications of this policy implementation over the nationally determined contributions (NDC). Full article

The highly temporal variability of the hydrological response in Mediterranean areas affects the operation of hydropower systems, especially in run-of-river (RoR) plants located in mountainous areas. Here, the water flow regime strongly determines failure, defined as no operating days due to inflows below the minimum operating flow. A Bayesian dynamics stochastic model was developed with statistical modeling of both rainfall as the forcing agent and water inflows to the plants as the dependent variable using two approaches—parametric adjustments and non-parametric methods. Failure frequency analysis and its related operationality, along with their uncertainty associated with different time scales, were performed through 250 Monte Carlo stochastic replications of a 20-year period of daily rainfall. Finally, a scenario analysis was performed, including the effects of 3 and 30 days of water storage in a plant loading chamber to minimize the plant’s dependence on the river’s flow. The approach was applied to a mini-hydropower RoR plant in Poqueira (Southern Spain), located in a semi-arid Mediterranean alpine area. The results reveal that the influence of snow had greater operationality in the spring months when snowmelt was outstanding, with a 25% probability of having fewer than 2 days of failure in May and April, as opposed to 12 days in the winter months. Moreover, the effect of water storage was greater between June and November, when rainfall events are scarce, and snowmelt has almost finished with operationality levels of 0.04–0.74 for 15 days of failure without storage, which increased to 0.1–0.87 with 3 days of storage. The methodology proposed constitutes a simple and useful tool to assess uncertainty in the operationality of RoR plants in Mediterranean mountainous areas where rainfall constitutes the main source of uncertainty in river flows. Full article

Accurate forecasting of the tail water level (TWL) is of great importance for the safe and economic operation of hydropower stations. The prediction accuracy is significantly influenced by the backwater effect of downstream tributaries and the operation of adjacent hydropower stations, but the explicit quantification method of the backwater effect is lacking. In this study, a deep-learning-model-based forecasting method for TWL predictions under the backwater effect is developed and applied in the Xiangjiaba (XJB) hydropower station, which is influenced by the backwater effect of downstream tributaries, including the Hengjiang River (HJR) and the Minjiang River (MJR). Firstly, the random forest algorithm was used to analyze the influence of HJR and MJR flows with different lag times on the TWL prediction error of the XJB hydropower station. The results show that the time lags of the backwater effect of HJR and MJR run offs on the TWL of the XJB are 5~7 h and 1~2 h, respectively. Then, the run off thresholds of the HJR and MJR for impacting the TWL of the XJB station are obtained through scenario comparison, and the results show that the run off thresholds of the HJR and the MJR are 700 m³/s and 7000 m³/s, respectively. Finally, based on the analysis of the time lag and the threshold of the backwater effect, a deep learning model (LSTM)-based TWL forecasting method is established and applied to predict the TWL of the XJB station. The results show that the forecasting model has a good predictive performance, with 98.22% of absolute errors less than 20 cm. The mean absolute error over the validation dataset is 5.27 cm, and the maximum absolute error is 63.35 cm. Compared with the LSTM-based prediction model without considering the backwater effect, the mean absolute error decreased by 31%, and the maximum absolute error decreased by 71%. Full article

We developed a Parsimonious Multi-dimensional Moving Window (PMMW) algorithm that only requires Digital Elevation Model (DEM) data of a watershed to efficiently locate potentially optimal hydropower sites. The methodology requires only open source DEM data; therefore, it can be used even in remotest watersheds of the world where in situ measurements are scarce or not available at all. We used three parameters in this algorithm, and tested the method using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Shuttle Radar Topography Mission (SRTM) derived DEMs. Our case study on the Morony Watershed, Montana, USA shows that (1) along with 6 out of the 7 existing hydropower plants being successfully located, 12 new potential hydropower sites were also identified, using a clearance of 1 km, diversion of 90 m, and Hydropower Index (HI) threshold of 10⁹ m as the criteria. For the 12 new potential hydropower sites, 737.86 Megawatts (MW) ± 84.56 MW untapped hydropower potential in the Morony Watershed was also derived; (2) SRTM DEM is more suitable for determining the potential hydropower sites; (3) although the ASTER and SRTM DEMs provide elevation data with high accuracy, micro-scale elevation differences between them at some locations may have a profound impact on the HI. Full article

In response to the issue of the hydropower consumption of run-of-river hydropower stations in Southwest China, the district cooling system can provide regulation capacity for hydropower utilization and suppress fluctuations caused by the uncertainty of hydropower. The innovative method is to utilize the thermal characteristics of pipelines and buildings, as well as the thermal comfort elasticity to shift the cooling and electricity loads, which helps to consume the surplus hydroelectric power generation. Taking the minimum total cost of coal consumption in thermal power units, hydropower abandonment penalty, and the carbon trading cost as the objective function, models were established for power supply balance constraints, heat transport constraints, and unit output constraints. The hybrid integer linear programming algorithm was used to achieve the low-carbon economic dispatch of the electric-cooling system. The calculation examples indicate that compared to the traditional real-time balance of cooling supply, the comprehensive consideration of thermal characteristics in a cooling system and flexible thermal comfort have a better operational performance. The carbon trading cost, coal consumption cost, and abandoned hydropower rate of a typical day was reduced by 4.25% (approximately CNY 7.55 × 10⁴), 4.47% (approximately CNY 22.23 × 10⁴), and 3.66%, respectively. Therefore, the electric-cooling dispatch model considering the thermal characteristics in cooling networks, building thermal inertia, and thermal comfort elasticity is more conducive to the hydropower utilization of run-of-river stations. Full article

Periodic flushing operations during moderate flood events (≤annual flood flow HQ${}_1$) are an approach to counteract problems caused by disturbed sediment continuity in rivers, which is possibly an effect of run-of-river hydropower plants (RoR-HPPs). Considering ecology, flood risk, technical, and economical reasons, discharge values of 0.7 × HQ${}_1$ are a good reference point for the initiation of gate operations. This work aimed to investigate the role of different gate opening actions on the effectiveness of such flushing measures. Physical model tests were performed, to capture bed load rates, together with 2D velocity measurements in the vicinity of two movable radial gates above a fixed weir. The length scale of the idealized model arrangement was 1:20, and a conveyor-belt sediment feeder was used to supply a heterogeneous sediment mixture. Velocities were acquired using 2D laser doppler velocimetry (LDV). Based on the LDV measurements, mean velocity profiles and Reynolds stresses were derived. The full opening of both radial gates led to the highest bed load mobility. While the flushing efficiency drastically decreased, even for slightly submerged gates, an asymmetrical gate opening initially led to the formation of a flushing cone in the vicinity of the weir, accompanied by temporarily high flushing efficiency. In conclusion, our results stress the importance of full drawdowns in successfully routing incoming bed load downstream of the HPP. However, the combination of an asymmetric gate opening followed by a full drawdown could be a promising approach to further improve the flushing efficiency of RoR-HPPs. Full article

In the XFLEX HYDRO Vogelgrun demonstrator, a run-of-river hydropower plant, the hybridization of one turbine-generator unit with a battery energy storage system is being investigated. This paper describes the integration methodology of the hybrid control algorithm without replacing the existing speed governor of the unit. Furthermore, the comparison of the performances of a non-hybrid and hybrid unit is discussed, and first experiences gained during the operation and monitoring of the hybrid operating mode are presented. Full article