Large-scale Hydropower Station Research Articles

Dynamic transformer ODEs for large-scale reservoir inflow forecasting

Forecasting incoming water demand is a critical step in efficient reservoir management and revenue optimization in large-scale cascade hydropower stations. It depends on multiple factors, such as weather conditions, grid dispatch, and electricity demand, and, in turn, facilitates a range of downstream decision-making, from natural hazards control and water ecology protection to power generation plans. Current efforts mainly rely on methodologies from statistical machine learning or deep neural networks to model the hydrological patterns from historical time series for inflow forecasting. However, existing models are restricted by short-term temporal dependencies and are prone to error accumulation issues due to the underlying autoregressive architecture. Meanwhile, most recent self-attention time-series models fail to achieve real-time inflow forecasting because of tremendous parameters and computational bottlenecks on learning long time series with fine granularity. We propose a novel framework, called DTODE (Dynamic Transformer Ordinary Differential Equations), for capturing nonlinear and non-stationary evolving patterns inherent in hydrological time series. Specifically, we present the dynamic self-attention mechanism combining transformer and ordinary differential equations that simultaneously captures long-range dependencies of observations from a dynamic system perspective. DTODE exploits a continuum of self-attention layers (instead of discrete counterparts) to learn the dynamics of multivariate time series while paying attention to the co-evolving time-related factors. Besides, our model is flexible in inferring the complex states at any time step, allowing us to forecast inflows at multiple time horizons. Comprehensive evaluations on real-world datasets show that DTODE significantly reduces forecasting errors compared to state-of-the-art inflow prediction systems.

Reservoir Inflow Forecasting in Hydropower Industry: A Generative Flow-Based Approach

Forecasting the inflow of reservoirs plays an essential role in the hydropower industry. Existing studies are either limited to point estimates or inefficient in capturing higher-order dynamic correlations across data. It is nevertheless necessary to estimate data uncertainty in actual dam operation. This article presents a novel inflow prediction method that exploits generative flows to model complex multivariate hydrological time series. Our flow-to-flow method (F2F) augments the deterministic models with the normalizing flow-based generative networks to explicitly capture the multivariate correlations and approximate the predictive inflow distribution. Besides, F2F can quantify the prediction uncertainty to help interpret model behavior and predicted results while facilitating safety-critical decision-making on real-time reservoir operation. We conduct extensive experiments on real-world datasets collected from large-scale hydropower stations. The experimental results show that our method consistently outperforms existing methods while accounting for uncertain observations and providing tractable multistep ahead inflow forecasts.

Accessing the Time-Series Two-Dimensional Displacements around a Reservoir Using Multi-Orbit SAR Datasets: A Case Study of Xiluodu Hydropower Station

The construction of large-scale hydropower stations could solve the problem of China’s power and energy shortages. However, the construction of hydropower stations requires reservoir water storage. Artificially raising the water level by several tens of meters or even hundreds of meters will undoubtedly change the hydrogeological conditions of an area, which will lead to surface deformation near the reservoir. In this paper, we first used SBAS-InSAR technology to monitor the surface deformation near the Xiluodu reservoir area for various data and analyzed the surface deformation of the Xiluodu reservoir area from 2014 to 2019. By using the 12 ALOS2 ascending data, the 100 Sentinel-1 ascending data, and the 97 Sentinel-1 descending data, the horizontal and vertical deformations of the Xiluodu reservoir area were obtained. We found that the Xiluodu reservoir area is mainly deformed along the vertical shore, with a maximum deformation rate of 250 mm/a, accompanied by vertical deformation, and the maximum deformation rate is 60 mm/a. Furthermore, by analyzing the relationship between the horizontal deformation sequence, the vertical deformation sequence, and the impoundment, we found the following: (1) Since the commencement of Xiluodu water storage, the vertical shore direction displacement has continued to increase, indicating that the deformation caused by the water storage is not due to the elastic displacement caused by the load, but by irreversible shaping displacement. According to its development trend, we speculate that the vertical shore direction displacement will continue to increase until it eventually stabilizes; (2) Vertical displacement increases rapidly in the initial stage of water storage; after two water-storage cycles, absolute settlement begins to slow down in the vertical direction, but its deformation still changes with the change in the storage period.

Optimal bidding for large-scale hydropower stations serving multiple power grids in multi-regional monthly electricity markets

Driven by the development of the multi-level and unified electricity market in China, large-scale hydropower stations serving multiple power grids (LHSMPGs) have recently been required to enter into multiple regional electricity transactions. However, due to the multiple uncertainties of runoff and electricity prices, complex operation constraints, and difficulties in quantifying opportunity costs, formulating reliable bidding strategies for multi-regional monthly auction markets to maximize profits presents a challenge for LHSMPGs in China. Accordingly, in this study, we propose a tractable bidding optimization method for LHSMPGs in multi-regional monthly electricity markets. First, a scenario-based method is developed to describe the uncertainties in runoff and multi-regional electricity prices, as well as their cross-correlations. Then, a stochastic programming model is developed to coordinately optimize the piecewise bidding curves in multi-regional auction markets. The model considers the opportunity cost of bid quantities and complex monthly electricity allocation requirements during the whole time horizon of a year. Moreover, a monthly-rolling solution framework is designed to solve the stochastic programming model to ensure that the latest actual data can be dynamically considered to improve the accuracy of declaration. The application of the proposed method is verified through two cases based on the Xiluodu Hydropower Station, which ranks fourth in the world in terms of installed capacity. The results demonstrate that optimizing bidding curves in multi-regional monthly auction markets coordinately can enhance gross revenues and electricity sales. Moreover, a sensitivity analysis reveals that an excessively large or small allocation proportion of electricity between annual and monthly markets would weaken the guiding role of monthly auction markets in optimizing the allocation of hydropower resources and exacerbate the uneven distribution of hydropower generation between flood and dry seasons.

Evaluating the effects of dams and meteorological variables on riparian vegetation NDVI in the Tibetan Plateau

As the third pole of the world, the Qinghai-Tibet Plateau (QTP) has a very special climate and geographical environment. In the past 20 years, with the increasing demand for clean energy, more than ten hydropower stations have been built. The impacts of these hydropower stations on riparian vegetation (RV) have only been described qualitatively in previous studies, while the contribution of dams and meteorological variables to riparian vegetation has not been quantitatively assessed. This study selected eight representative large-scale hydropower stations in the QTP, calculated and analyzed the dynamics of the standardized difference vegetation index (NDVI) of the RV pre-and post the dams construction, combined with the measured temperature and precipitation data to explore the driving factors of RV changes. The results show that the dams promoted the growth of RV and they were the main contributor (>50%) while precipitation and temperature had relatively small impacts. The effect of dams varies for different regions, compared with the sub-cold regions, it was more significant in humid and semi-humid regions of temperate zone. The dams affected RV in an indirect way through regulating the microclimate, promoting precipitation and slowing down the rate of temperature rise and these effects may come from the increase of the upstream water surface area.

HydroFlow: Towards probabilistic electricity demand prediction using variational autoregressive models and normalizing flows

We present HydroFlow, a novel deep generative model for predicting the electricity generation demand of large-scale hydropower stations. HydroFlow uses a latent stochastic recurrent neural network to capture the dependencies in the multivariate time series. It not only utilizes the hidden state of the neural network, but also considers the uncertainty of variables related to natural and social factors. We also introduce an end-to-end approach based on generative flows to approximate the posterior distribution of time series with exact likelihoods. Our model is powerful as adding stochasticity to different factors (e.g., reservoir capacity and water-flow measurements) and thus overcomes the expressiveness limitations of deterministic prediction methods. It also enables trainable latent transformations that can improve the model interpretability. We evaluate HydroFlow on the data collected from the hydropower stations of a large-scale hydropower development company. Experimental results show that our model significantly outperforms the state-of-the-art baseline methods while providing explainable results.

Research on development and utilization of hydropower in Myanmar

Hydropower contributes a lot to electricity generation. Myanmar has enormous potential for water resources which can guarantee domestic electricity supply and export surplus power to neighbouring countries. This paper analyses the status-quo of water resources and obstacles of Myanmar’s hydropower development, and concludes that: (1) Myanmar harbours great potential for hydropower development and utilization; (2) the spatial distribution of hydropower stations and the supply of electricity are unbalanced; (3) main obstacles for hydropower development and utilization include weak governance over water resources, lack of fund and technology, natural disasters and environmentalism resistance. To achieve full electrification in 2030, it is essential for Myanmar to promote the construction of both small-scale and responsible large-scale hydropower stations.

Research on the Hydropower Generation Optimal Scheduling of Larger-Scale Cascade Hydropower Stations on Jinsha River and Lancang River

There are some characteristics, such as multi-dimensional, stochastic inflow, non-uniform service and scheduling subjects, in optimal scheduling of power generation. The problem is also subjected to hydrological and meteorological conditions, operation control, water demand, and loads of the power grid. Aiming at the electric power and energy balance problem in the high hydropower system, this paper uses the typical annual load curve to analyse the electricity power and energy balance, so as to better consider the influence of seasonal differences in hydrological conditions on the storage capacity of adjustable hydropower stations and the start-up of thermal power stations, and establish a mid-long term model of hydropower generation optimal scheduling of large-scale cascade hydropower stations. Taking the cascade hydropower stations in the Jinsha River and Lancang River as examples for simulation analysis, and using GUROBI to solve the model, the correctness and effectiveness of the proposed optimal scheduling model are verified.

Research On Energy Distribution of Multi-Scene Hydropower System Through Improved Power Load Optimization Strategy

Taking the minimization of coal consumption in hydro-thermal power system as the objective function, this paper focuses on the static water abandonment strategy and improved dynamic water abandonment strategy, given the constraint conditions of hydropower station generation flow, reservoir water quantity, load balance constraint and power constraint. in this paper, the penalty cost of abandoned water is introduced into the existing model, and a short-term electric power balance model considering the cost of abandoned water is established. In view of the deficiency that it is difficult to effectively obtain feasible solutions that meet large-scale and complex constraints in the process of solving the model, the premature convergence and local optimization of the algorithm are avoided. An optimization algorithm is proposed to provide reference for solving the problem that the calculation efficiency of optimal dispatching of large-scale hydropower stations is the problem faced by hydropower and power system operation.

Dynamic programming with successive approximation and relaxation strategy for long-term joint power generation scheduling of large-scale hydropower station group

The joint optimal operation of large-scale hydropower station group (LHSG) is faced with the higher dimension than that of cascade hydropower station, the demand for the efficient optimization techniques of the above problem is urgent. Integrating the characteristics of problem into optimization techniques is an effective way. Therefore, based on some previous research results, the approximate concavity and monotonicity characteristics of power generation utility function of dynamic programming with successive approximation (DPSA) in each stage is analyzed. Then, an improved DPSA with relaxation strategy (named DPSARS) based on the above mathematical derivations is proposed to solve the long-term joint power generation scheduling (LJPGS) of LHSG. Compared with DPSA, the time complexity exhibits quadratic increase with the number of discrete states, while DPSARS only exhibits linear increase. Then, in order to further test the convergence accuracy and efficiency of the proposed DPSARS, the model of the LJPGS problem of LHSG, composed of 61 hydropower stations in the upper reaches of the Yangtze River, is established. The experimental results show that DPSARS represents its competitive performance in solving the LJPGS problem of LHSG compared with other methods.

Practices of Hydro-Meteorological Support During Construction Period of Largehydropower Project

The hydrological and meteorological hydro-meteorological support is one important part of the hydropower construction. Accidents caused by flood and rainstorm during construction will be reduced effectively with the help of reliable hydrological and meteorological forecast which also provides critical technical support for flood prevention and construction organization of projects under construction. Based on the practice of the hydro-meteorological support during the construction period of Three Gorges and Jinsha River cascade hydropower station, this article discussed the methods to support and serve hydro meteorology of large-scale cascade hydropower station construction. Meanwhile, this paper also builds up a hydro-meteorology supportive system for hydro-power station under construction which is led by a hydropower development company and associated by local professional institutions. And this research can also be utilized as a reference for other large-scale hydro-power station construction and to support the hydro-meteorology safeguard during the operation period.

Influence of Unsteady Flow Induced by a Large-Scale Hydropower Station on the Water Level Fluctuation of Multi-Approach Channels: A Case Study of the Three Gorges Project, China

Unsteady flow induced by hydropower stations exerts a significant impact on the water level in multi-approach channels, which directly threatens the safe passage of ships. In this study, a one-dimensional and a two-dimensional hydrodynamic model are adopted to simulate the water level fluctuations at the entrance of multi-approach channels and the lower lock head of a ship lift with consideration of initial water surface elevation, base flow, flow amplitude, regulation time, and locations of hydropower stations, unfavorable conditions are successfully identified; and the fluctuations at the approach channel entrance and the lower lock head of a ship lift under single-peak and double-peak regulating mode are analyzed considering the flow regulating of the Gezhouba Hydropower Station (GHS), thus, the water level oscillation process in the multi-approach channels is presented. Results show that the largest wave amplitude in the multi-approach channels manifests under unfavorable conditions including lower initial water surface elevation, smaller base flow, larger flow variation, and shorter regulation time; and water level fluctuation in the multi-approach channel is primarily induced by flow amplitude and net flow between the Three Gorges Hydropower Station (TGHS) and the GHS, with consideration of the counter-regulation process of the GHS. This research contributes to providing a reference for a similar large-scale cascade hydropower station regarding regulation and control of navigation conditions.

Analyses of Runoff and Sediment Transport and their Drivers in a Rare Earth Mine Drainage Basin of the Yangtze River, China

A comprehensive analysis of the effects of major climate conditions such as El Nino Southern Oscillation (ENSO) and precipitation on changes in runoff and sediment transport in a basin may provide a scientific basis and technical support for regional water resource management and protection of the aquatic ecology. Taking the Taojiang River as an example, a large set of hydrogeographic data on runoff and sediment transport measured on a monthly basis from 1957 to 2015 was analyzed to study the impacts of various correlation factors on runoff and sediment transport in the river, which is located in the middle and lower reaches of the Yangtze River. Besides the conventional Mann–Kendall (M-K) method, cross-wavelet and wavelet coherence analysis methods were also applied in the data analysis. The results showed that: (1) From the M-K mutation tests conducted for the runoff volume and the sediment transport rate from 1957 to 2015, there were no significant changes in runoff. However, a mutation occurred in the sediment transport rate in 2005 and the average annual decrease reached 88.2237 million tons. (2) Precipitation was a dominant factor that controlled the changes in runoff volume and sediment transport rate. It directly influenced the changes in runoff volume, which subsequently caused the changes in sediment transport rate in the study area. Since the year 2005, sediment transport rates have been heavily influenced by the construction of large-scale hydro-power stations (Julongtan), causing a significant rate decline. A comparison between the sediment transport volume during 2005 to 2015 and that during 1980 to 2004 revealed that the annual sediment transport decrease reached 84.4079 million tons, accounting for 95.7% of the total decrease in sediment transport volume. (3) The significant resonance cycle between the sea surface temperature (SST) and the precipitation, runoff volume and sediment transport mainly occurred with a cyclic period between 1.33 and 5.33 years. During an ENSO event, the precipitation, runoff, and sediment transport rates all decreased at the beginning, then increased and reached their maximums, followed by a decline at the end.

Maximizing both the firm power and power generation of hydropower station considering the ecological requirement in fish spawning season

The construction and operation of large-scale reservoirs changes the flow regimes downstream, and thus has negative impact on the aquatic organisms. As an important indicator species, fish is sensitive to flow regimes, which provide stimulus for physiological activity, such as spawning and immigration. On the other hand, the grid-connected large-scale hydropower stations often carry out multiple functions, such as power generation, peak shaving, flood control, and navigation, involving multiple subjects including power plant, power grid, etc. In this study, a multi-objective optimization model is proposed considering the power generation requirement of the power plant, the firm power requirement of the power grid, and the ecological requirement of the fish spawning downstream, simultaneously. The flow regimes is firstly identified and characterized, and the reservoir discharge similar to the natural status is preferred. The proposed model is solved by Non-dominated Sorting Genetic Algorithm II (NSGA-II), and applied to a large-scale reservoir in planning on the upper reaches of the Yellow River in China, where the spawning ground of the plateau cold water fishes will be influenced. The results indicate that the proposed methodology can achieve some balance among different objectives from different subjects, and can be generalized to other large-scale reservoirs that has great impact on the flow regimes downstream.

Economic load dispatch of renewable energy-based power systems with high penetration of large-scale hydropower station based on multi-agent glowworm swarm optimization

In this study, we propose a multi-agent glowworm swarm optimization (MAGSO) algorithm for the economic load dispatch (ELD) problem of a large-scale hydropower station. The MAGSO integrates the idea of the evolution of glowworm swarm optimization (GSO) and the interindividual cooperation of multi-agent system (MAS). In this structure, each glowworm of the GSO is treated as an agent cooperating in the MAS before entering the evolutionary process. The proposed mechanisms enable the algorithm to escape from local extremum, capture the optimal solution and expand the diversity of solution space. The test results of four benchmark functions verify the advantages of the algorithm in global search, convergence speed and avoiding prematurity. In comparison of two case studies with the real genetic algorithm (RGA), the evolution programming (EP), the particle swarm optimization (PSO), the ant colony optimization (ACO), and the standard glowworm swarm optimization (GSO), the MAGSO provides significantly robust solutions of ELD problem of a large-scale hydropower station.

Wind-Solar-Hydro power optimal scheduling model based on multi-objective dragonfly algorithm

Wind power, solar power and hydropower can form a good complementary effect in space and time. Research on Wind-Solar-Hydro power generation system has gradually become a hot research topic. In this paper, a large-scale hydropower station in southwestern China and its surrounding wind fields and solar fields are taken as examples to establish an optimal scheduling model with the objectives of maximizing system total power generation, maximizing the minimum monthly joint output of the whole scheduling period, and minimizing the ecological over-and-short discharge. The paper presents a multi-objective dragonfly algorithm base on reference point to obtain the non-dominated solutions in the wet year, normal year and dry year. Meanwhile, the scheduling processes of four representative plans on the obtained Pareto front are selected to analyze its certain practical significance. It is found that the established multi-objective optimal model and the proposed algorithm have certain reference value for the optimal operation of Wind-Solar-Hydro power generation system.

Analysis and Treatment of Typical Problems of GIS Circuit Breaker

With the development and progress of society, the demand for electric energy is increasing day by day. The number of hydropower stations has increased dramatically. In the large-scale hydropower stations and substations currently under construction, GIS high-voltage switch stations have gradually replaced traditional high-voltage switch stations due to their advantages of arc extinguishing and insulation performance, low floor space and high reliability. The GIS breaker body and the operating mechanism of the GIS breaker, or other components of the GIS switch station, are subject to various reasons such as design, manufacture, installation, commissioning, operation and maintenance. Frequently, the GIS breaker leaks, the circuit breaker refuses to move, the operating mechanism cannot store energy normally, and the closing time is not easy to measure. These phenomena pose a serious threat to the safe and stable operation of the power system. Such typical phenomena of GIS circuit breakers deserve our consideration of inquiry, analysis and summary. Find the root cause of the failure and propose the most effective improvement. This paper proposes and solves several typical faults during the installation, commissioning, operation and maintenance of GIS circuit breakers. Provide reference for the fault analysis and processing of GIS circuit breakers in the future.

Experimental Optimization of Gate-Opening Modes to Minimize Near-Field Vibrations in Hydropower Stations

Multi-Horizontal-Submerged Jets are successfully applied to dissipate energy within a large-scale hydropower station. However, notable near-field vibrations are generated when releasing high discharges through the gates, which is generally typical in a flooding case scenario. Under these conditions, the magnitude of the vibrations varies when applying different gate-opening modes. To investigate and find optimized gate-opening modes to reduce the near-field vibration, multiple combinations were tested by varying gate-opening modes and hydraulic conditions. For each of the tests conducted, fluctuating pressures acting on side-walls and bottoms of a stilling basin were measured. The collected datasets were used to determine the maximum and minimum fluctuating pressure values associated with the correspondent gate-opening mode and a detailed comparison between each of the gate-opening modes was completed. The paper presents the quantitative analysis of the discharge ratio’s effect on fluctuating pressures. It also investigates the influence of different gate-opening modes by including side to middle spillways and upper to lower spillways configurations. The flow pattern evolutions triggered by each different gate-opening mode are discussed and optimal configurations that minimize near-field vibrations at high discharges are recommended to support both the design of new systems and assessment of the performance of existing ones.

Analysis and Measures of Ultralow-Frequency Oscillations in a Large-Scale Hydropower Transmission System

The Yunnan power grid has a large-scale hydropower station, whereby the power generated at the station is transmitted via HVdc lines under an asynchronous operation mode. In the system performance test of the Yunnan asynchronous operation, a 0.05-Hz ultralow-frequency oscillation occurred in the grid. This ultralow-frequency oscillation is a frequency stability problem caused by instability of hydraulic turbine governing system, which is quite common in the islanded system and very rare in the normal synchronous system. The relationship between the speed governing system stability of one unit and that of the system with parallel operation units was proposed to explain that the high proportion of units with an unstable governing system will lead to system frequency instability and the reduction in loads in the traditional sense was proven to further worsen the frequency stability of the Yunnan asynchronous system. By using related measures, the ultralow-frequency oscillation of the Yunnan asynchronous system has been curbed effectively. System performance testing verified the effectiveness of the analysis and the measures.

Examining the Effects of Hydropower Station Construction on the Surface Temperature of the Jinsha River Dry-Hot Valley at Different Seasons

On the completion of a large-scale hydropower station, the change of the water area can cause a corresponding change of local weather. To examine such changes, this paper analyzed the effect of the reservoir in the head area of the Xiluodu hydropower station based on the temperature data of MODIS MYD11A2. The temperature differences (TD) between various locations in the study area and the reservoir were calculated to explore the TD in different seasons. The reservoir effect change intensity (RECI) was established to explore the impact of the reservoir on local weather changes in different flood seasons. The combination of the TD and RECI was applied to explore the role of the hydropower station in regulating the temperature of the surrounding reservoir. The results showed the following: (1) after hydropower station construction (HSC), the TD in the valleys decreased and the TD in the dry season was lower than that in the wet season; (2) the RECI had different distribution characteristics in different flood seasons of the reservoir, and the RECI was stronger in the wet season than that in the dry season; and (3) unlike in the plains, cooling and warming effects existed simultaneously in different parts of the mountains.