scholarly journals Optimization of Year-End Water Level of Multi-Year Regulating Reservoir in Cascade Hydropower System Considering the Inflow Frequency Difference

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5345
Author(s):  
Zhiqiang Jiang ◽  
Peibing Song ◽  
Xiang Liao
Keyword(s):  
Power Generation ◽  
Dynamic Programming ◽  
Water Level ◽  
Water Levels ◽  
Least Square ◽  
Frequency Difference ◽  
Impact Factors ◽  
Cascade System ◽  
Cascade Reservoirs ◽  
Hydropower System

In order to analyze the year-end water level of multi-year regulating reservoir of the cascade hydropower system, this paper studied the joint operation optimization model of cascade reservoirs and its solving method based on multi-dimensional dynamic programming, and analyzed the power generation impact factors of cascade system that contains multi-year regulating reservoir. In particular, taking the seven reservoirs in the middle and lower reaches of Yalong River as an example, the optimal year-end water levels of multi-year regulating reservoir under the multi-year average situation and different inflow frequencies situation were studied. Based on the optimal calculation results of multi-dimensional dynamic programming, the inflow frequency difference considered operation rule of year-end water level of Lianghekou reservoir was extracted using the least square principle. The simulation results showed that, compared with the fixed year-end water level in multi-year, the extracted rule can improve the cascade power generation by more than 400 million kWh in an average year, representing an increase of 0.4%. This result means that the extracted rule can give full play to the regulation performance of multi-year regulating reservoir and improve the conversion efficiency of hydropower resources in cascade system. This is of great significance to the practical operation of cascade reservoirs system that contains multi-year regulating reservoir.

scholarly journals Multi-Objective Optimal Scheduling Model of Dynamic Control of Flood Limit Water Level for Cascade Reservoirs

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1836 ◽  
Author(s):  
Guanjun Liu ◽  
Hui Qin ◽  
Qin Shen ◽  
Rui Tian ◽  
Yongqi Liu
Keyword(s):  
Power Generation ◽  
Water Level ◽  
Flood Control ◽  
Dynamic Control ◽  
Optimal Scheduling ◽  
Cascade Reservoirs ◽  
Multi Objective ◽  
Scheduling Model ◽  
Extreme Risk ◽  
Risk Rate

Reservoirs play a significant role in water resources management and water resource allocation. Traditional flood limited water level (FLWL) of reservoirs is set as a fixed value which over-considers the reservoir flood control and limits the benefits of reservoirs to a certain extent. However, the dynamic control of the reservoir FLWL is an effective solution. It is a method to temporarily increase the water level of the reservoir during the flood season by using forecast information and discharge capacity, and it can both consider flood control and power generation during the flood season. Therefore, this paper focuses on multi-objective optimal scheduling of dynamic control of FLWL for cascade reservoirs based on multi-objective evolutionary algorithm to get the trade-off between flood control and power generation. A multi-objective optimal scheduling model of dynamic control of FLWL for cascade reservoirs which contains a new dynamic control method is developed, and the proposed model consists of an initialization module, a dynamic control programming module and an optimal scheduling module. In order to verify the effectiveness of the model, a cascade reservoir consisting of seven reservoirs in the Hanjiang Basin of China were selected as a case study. Twenty-four-hour runoff data series for three typical hydrological years were used in this model. At the same time, two extreme schemes were chosen for comparison from optimized scheduling schemes. The comparison result showed that the power generation can be increased by 9.17 × 108 kW·h (6.39%) at most, compared to the original design scheduling scheme, while the extreme risk rate also increased from 0.1% to 0.268%. In summary, experimental results show that the multi-objective optimal scheduling model established in this study can provide decision makers with a set of alternative feasible optimized scheduling schemes by considering the two objectives of maximizing power generation and minimizing extreme risk rate.

AUTOMATIC WATER LEVEL MONITORING WITH IOT AND LPWAN

2019 ◽  
pp. 95-103
Author(s):  
Krum Videnov ◽  
Vanya Stoykova
Keyword(s):  
Water Level ◽  
Real Time ◽  
Early Warning ◽  
Water Sources ◽  
Water Levels ◽  
Water Level Fluctuations ◽  
Level Monitoring ◽  
Water Level Monitoring

Monitoring water levels of lakes, streams, rivers and other water basins is of essential importance and is a popular measurement for a number of different industries and organisations. Remote water level monitoring helps to provide an early warning feature by sending advance alerts when the water level is increased (reaches a certain threshold). The purpose of this report is to present an affordable solution for measuring water levels in water sources using IoT and LPWAN. The assembled system enables recording of water level fluctuations in real time and storing the collected data on a remote database through LoRaWAN for further processing and analysis.

Interaction between water pressure in the basal drainage system and discharge from an Alpine glacier before and during a rainfall-induced subglacial hydrological event

1997 ◽  
Vol 24 ◽  
pp. 288-292 ◽  
Author(s):  
Andrew P. Barrett ◽  
David N. Collins
Keyword(s):  
Water Level ◽  
Water Pressure ◽  
Drainage System ◽  
Water Levels ◽  
Drainage Network ◽  
Alpine Glacier ◽  
Hydraulic Conditions ◽  
Borehole Water ◽  
Progressive Growth ◽  
Resultant Increase

Combined measurements of meltwater discharge from the portal and of water level in a borehole drilled to the bed of Findelengletscher, Switzerland, were obtained during the later part of the 1993 ablation season. A severe storm, lasting from 22 through 24 September, produced at least 130 mm of precipitation over the glacier, largely as rain. The combined hydrological records indicate periods during which the basal drainage system became constricted and water storage in the glacier increased, as well as phases of channel growth. During the storm, water pressure generally increased as water backed up in the drainage network. Abrupt, temporary falls in borehole water level were accompanied by pulses in portal discharge. On 24 September, whilst borehole water level continued to rise, water started to escape under pressure with a resultant increase in discharge. As the drainage network expanded, a large amount of debris was flushed from a wide area of the bed. Progressive growth in channel capacity as discharge increased enabled stored water to drain and borehole water level to fall rapidly. Possible relationships between observed borehole water levels and water pressures in subglacial channels are influenced by hydraulic conditions at the base of the hole, distance between the hole and a channel, and the nature of the substrate.

scholarly journals Spatial distribution of water level impact to back-barrier bays

2018 ◽  
Author(s):  
Alfredo L. Aretxabaleta ◽  
Neil K. Ganju ◽  
Zafer Defne ◽  
Richard P. Signell
Keyword(s):  
Water Level ◽  
Sea Level ◽  
Water Levels ◽  
Analytical Models ◽  
Barnegat Bay ◽  
Sea Level Fluctuations ◽  
Flooding Hazard ◽  
Short Period ◽  
Inlet Geometry ◽  
Spatial Estimates

Abstract. Water level in semi-enclosed bays, landward of barrier islands, is mainly driven by offshore sea level fluctuations that are modulated by bay geometry and bathymetry, causing spatial variability in the ensuing response (transfer). Local wind setup can have a secondary role that depends on wind speed, fetch, and relative orientation of the wind direction and the bay. Inlet geometry and bathymetry primarily regulate the magnitude of the transfer between open ocean and bay. Tides and short-period offshore oscillations are more damped in the bays than longer-lasting offshore fluctuations, such as storm surge and sea level rise. We compare observed and modeled water levels at stations in a mid-Atlantic bay (Barnegat Bay) with offshore water level proxies. Observed water levels in Barnegat Bay are compared and combined with model results from the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) modeling system to evaluate the spatial structure of the water level transfer. Analytical models based on the dimensional characteristics of the bay are used to combine the observed data and the numerical model results in a physically consistent approach. Model water level transfers match observed values at locations inside the Bay in the storm frequency band (transfers ranging from 70–100 %) and tidal frequencies (10–55 %). The contribution of frequency-dependent local setup caused by wind acting along the bay is also considered. The approach provides transfer estimates for locations inside the Bay where observations were not available resulting in a complete spatial characterization. The approach allows for the study of the Bay response to alternative forcing scenarios (landscape changes, future storms, and rising sea level). Detailed spatial estimates of water level transfer can inform decisions on inlet management and contribute to the assessment of current and future flooding hazard in back-barrier bays and along mainland shorelines.

scholarly journals The effects of ENSO, climate change and human activities on the water level of Lake Toba, Indonesia: a critical literature review

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hendri Irwandi ◽  
Mohammad Syamsu Rosid ◽  
Terry Mart
Keyword(s):  
Climate Change ◽  
Water Level ◽  
Human Activities ◽  
Southern Oscillation ◽  
Climatic Conditions ◽  
Water Levels ◽  
Dominant Factor ◽  
Climate Projections ◽  
Continuous Increase ◽  
The Future

AbstractThis research quantitatively and qualitatively analyzes the factors responsible for the water level variations in Lake Toba, North Sumatra Province, Indonesia. According to several studies carried out from 1993 to 2020, changes in the water level were associated with climate variability, climate change, and human activities. Furthermore, these studies stated that reduced rainfall during the rainy season due to the El Niño Southern Oscillation (ENSO) and the continuous increase in the maximum and average temperatures were some of the effects of climate change in the Lake Toba catchment area. Additionally, human interventions such as industrial activities, population growth, and damage to the surrounding environment of the Lake Toba watershed had significant impacts in terms of decreasing the water level. However, these studies were unable to determine the factor that had the most significant effect, although studies on other lakes worldwide have shown these factors are the main causes of fluctuations or decreases in water levels. A simulation study of Lake Toba's water balance showed the possibility of having a water surplus until the mid-twenty-first century. The input discharge was predicted to be greater than the output; therefore, Lake Toba could be optimized without affecting the future water level. However, the climate projections depicted a different situation, with scenarios predicting the possibility of extreme climate anomalies, demonstrating drier climatic conditions in the future. This review concludes that it is necessary to conduct an in-depth, comprehensive, and systematic study to identify the most dominant factor among the three that is causing the decrease in the Lake Toba water level and to describe the future projected water level.

scholarly journals Application of Neural Network Models and ANFIS for Water Level Forecasting of the Salve Faccha Dam in the Andean Zone in Northern Ecuador

Water ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2011
Author(s):  
Pablo Páliz Larrea ◽  
Xavier Zapata Ríos ◽  
Lenin Campozano Parra
Keyword(s):  
Neural Network ◽  
Water Level ◽  
Clustering Algorithm ◽  
Network Models ◽  
Water Levels ◽  
Subtractive Clustering ◽  
Neural Network Models ◽  
Reservoir Water ◽  
Reservoir Water Level ◽  
Maximum Delay

Despite the importance of dams for water distribution of various uses, adequate forecasting on a day-to-day scale is still in great need of intensive study worldwide. Machine learning models have had a wide application in water resource studies and have shown satisfactory results, including the time series forecasting of water levels and dam flows. In this study, neural network models (NN) and adaptive neuro-fuzzy inference systems (ANFIS) models were generated to forecast the water level of the Salve Faccha reservoir, which supplies water to Quito, the Capital of Ecuador. For NN, a non-linear input–output net with a maximum delay of 13 days was used with variation in the number of nodes and hidden layers. For ANFIS, after up to four days of delay, the subtractive clustering algorithm was used with a hyperparameter variation from 0.5 to 0.8. The results indicate that precipitation was not influencing input in the prediction of the reservoir water level. The best neural network and ANFIS models showed high performance, with a r > 0.95, a Nash index > 0.95, and a RMSE < 0.1. The best the neural network model was t + 4, and the best ANFIS model was model t + 6.

Sign in / Sign up

Export Citation Format

Share Document