A method of direct, real-time forecasting of downstream water levels via hydropower station reregulation: A case study from Gezhouba Hydropower Plant, China

Shang, Yizi; Xu, Yuebin; Shang, Ling; Fan, Qixiang; Wang, Yong-Yi; Liu, Zhiwu

doi:10.1016/j.jhydrol.2019.04.017

Cited by 25 publications

(20 citation statements)

References 33 publications

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“…Its aim was also to forecast water levels, but applied to Polish lakes. In [6], Multilayer Perceptron (MLP) was applied for the same purpose, but for a navigable river and, in [7], the authors apply the level forecast for a HPP and point out that this study also contributes to ensuring the safety of the dam structures.…”

Section: Related Workmentioning

confidence: 99%

Daily Water Flow Forecasting via Coupling Between SMAP and Deep Learning

et al. 2020

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Hydrological models are essential tools to forecast daily water resources' availability, which are used to plan the short-term electrical systems' operation. However, there is a trade-off when choosing a given model. Complex models may provide good results depending on very complicated analytical and optimization procedures beyond sophisticated data, whereas simpler models offer reasonable results with much more amenable tuning approaches. To improve the quality of simpler models this paper proposes the coupling of the Soil Moisture Accounting Procedure (SMAP) hydrological model with a Deep Learning architecture based on Conv3D-LSTM. In the proposed methodology, the SMAP is first optimized to obtain general parameters of the hydrographic basin. This optimized model's output is used as input to the Conv3D-LSTM estimator to provide the final results. This gray estimator model can generate fast and accurate results. Studies whit the goal of forecast the natural flow seven days ahead are carried out for two large Brazilian hydroelectric plants to validate the method. The results obtained by the architecture are better than those obtained with decoupled techniques.

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Section: Related Workmentioning

confidence: 99%

Daily Water Flow Forecasting via Coupling Between SMAP and Deep Learning

et al. 2020

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“…Large reservoirs with large installed capacity are usually equipped with immediate downstream reservoirs to buffer the sharp fluctuation of outflow caused by the peak load regulation in a short period [1], [2]. In principle, the downstream reservoir formulates the power generation scheme according to the outflow process of the upstream reservoir, the forecasting local inflow process and the own re-regulation ability, so as to achieve the maximum power generation revenues of cascade reservoirs [1], [3]- [5]. In SGDCR, the hydraulic connection mainly consists of the MR of streamflow and the jacking effect between reservoirs[1], [2].…”

Section: Introductionmentioning

confidence: 99%

“…Take the TGGCR in China as an example. As the re-regulation reservoir of the Three Gorges reservoir (TGR), the Gezhouba reservoir, which is a run-ofriver hydropower plant, only owns a re-regulation capacity of 85 million m 3 [1]. There are no large tributaries between the two dams in geography, and the local inflow also can be ignored [3].…”

Section: Introductionmentioning

confidence: 99%

Characterizing the Hydraulic Connection of Cascade Reservoirs for Short-Term Generation Dispatching via Gaussian Process Regression

et al. 2020

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The characterization of the mapping relationship (MR) between outflow of the upstream reservoir (OUR) and inflow of the downstream reservoir (IDR) in the short-term generation dispatching of cascade reservoirs (SGDCR) greatly impacts the safe and economic operation of hydropower plants. If this MR is not characterized properly, the operation process of hydropower stations will deviate from the planning dispatching schemes. Especially when the upstream reservoir (UR) undertakes peak load regulation tasks frequently and the downstream reservoir (DR) owns weak re-regulation capacity, the safety of cascade reservoirs will be seriously threatened. In this extreme system, the commonest characterizing models in SGDCR, such as the lag time (LT) model and the Muskingum model, may cause a huge deviation when simulating or forecasting the IDR. Given this dilemma, the Gaussian process regression (GPR) model, which is a representative for Bayesian regression methods, is firstly introduced to handle this MR and compared with the LT and the Muskingum model in this paper. The Three Gorges-Gezhouba cascade reservoirs (TGGCR) in China are selected as a typical case study. The performance indicators of four categories are adopted to evaluate the simulated IDR in a single period and a set of pivotal indicators are proposed to estimate the simulation dispatching in multiple periods. The results show that (1) The GPR model reduces the mean absolute deviation (MAD) about inflow of Gezhouba by 197m 3 /s and 263m 3 /s than the LT and the Muskingum model respectively; (2) The distribution characteristics of inflow deviation produced by the GPR model are most competitive. Meanwhile, the GPR model has the strongest ability when conducting the multi-period simulation dispatching and owns best applicability on the whole range of streamflow. (3) The Muskingum model is not recommended to characterize the hour-scale hydraulic connection in the extreme system of SGDCR. INDEX TERMS cascade reservoirs, short-term generation dispatching, hour-scale hydraulic connection, Gaussian process regression.

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“…The output model of hydropower is a relatively complex nonlinear function. The upstream and downstream water levels and the net water heads are variables changing over time, the output of hydropower is restricted by the available water resources, has strong seasonal characteristics, therefore, the compensation effect of hydropower on PV is obviously different in different scenarios [7]. Research on the sizing of integrated generation system is particularly important based on multiple typical scenarios.In the electricity market, the uncertain factors would bring more unknown challenges for the sizing of integrated generation system [8], cost from electricity purchase and profit from electricity sales profit are affected by fluctuating spot price, which would bring higher risks for the economy of the system.…”

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confidence: 99%

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confidence: 99%

Optimal Sizing of Hydro-PV-Pumped Storage Integrated Generation System Considering Uncertainty of PV, Load and Price

Liu

Xiang

et al. 2019

Energies

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Effectively using complementary property of various renewable energy sources by an integrated generation system is a concerned study. Considering the uncertainty of PV, spot price and load, the sizing of integrated generation system with combined market operation is a difficult problem. Based on the above uncertain factors, the sizing method of hydro-PV-pumped storage integrated generation system is proposed in this paper. The output characteristic model is established by utilizing the wide range of hydropower output and flexible schedulability of pumped storage. Then, the volatility ratio of the power exchange curve and load tracking coefficient are the evaluation index of the complementary effect, which ensures the stable and smooth output curve. Further, the uncertainty models of PV, spot price and load are established, considering that, the operation of the power generation system is optimized in the day-ahead market, and the predictive deviation of PV and load is balanced in a real-time market. Based on the above factors, the sizing model of the hydro-PV-pumped storage integrated energy system was proposed based on economics and complementary index. Finally, a case study was undertaken, the sensitivity analysis of the economy and complementarity indices was carried out, as the complementarity index is improved, the economy would be down. Further, the optimal sizing of hydro-PV-pumped storage integrated generation system was obtained, hydropower was a total of 165 MW, PV was 100 MW and pumped storage was 50 MW, that could ensure the economy while meeting the complementary index, the effectiveness of the model proposed was verified.Energies 2019, 12, 3001 2 of 23 compensate for PVs better, the pumped storage power station can suppress the fluctuation of output in a shorter time [5], which would improve the grid's ability to PV consumption and the schedulability of the total output of the system. For instance, in China the Longyangxia hydro-PV complementary power station has achieved better coordination operation and complementarity, which has greatly weakened the impact of PV's integration into the power grid [6]. The output model of hydropower is a relatively complex nonlinear function. The upstream and downstream water levels and the net water heads are variables changing over time, the output of hydropower is restricted by the available water resources, has strong seasonal characteristics, therefore, the compensation effect of hydropower on PV is obviously different in different scenarios [7]. Research on the sizing of integrated generation system is particularly important based on multiple typical scenarios.In the electricity market, the uncertain factors would bring more unknown challenges for the sizing of integrated generation system [8], cost from electricity purchase and profit from electricity sales profit are affected by fluctuating spot price, which would bring higher risks for the economy of the system. The higher the transacted electricity of the complementary system in the spot market is, the gr...

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A method of direct, real-time forecasting of downstream water levels via hydropower station reregulation: A case study from Gezhouba Hydropower Plant, China

Cited by 25 publications

References 33 publications

Daily Water Flow Forecasting via Coupling Between SMAP and Deep Learning

Daily Water Flow Forecasting via Coupling Between SMAP and Deep Learning

Characterizing the Hydraulic Connection of Cascade Reservoirs for Short-Term Generation Dispatching via Gaussian Process Regression

Optimal Sizing of Hydro-PV-Pumped Storage Integrated Generation System Considering Uncertainty of PV, Load and Price

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