China’ energy-water nexus: Hydropower generation potential of joint operation of the Three Gorges and Qingjiang cascade reservoirs

Shang, Yizi; Lu, Shilei; Ye, Yuntao; Liu, Ronghua; Shang, Ling; Liu, Chunna; Meng, Xianyong; Li, Xiaofei; Fan, Qixiang

doi:10.1016/j.energy.2017.09.131

Cited by 40 publications

(13 citation statements)

References 48 publications

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“…Half of the major global river systems are affected by reservoirs and dams, and human beings manage and utilize water resources through reservoirs for power generation, water supply, navigation, disaster prevention, flood control and mitigation, drought relief (Dynesius and Nilsso, 1994;WCD, 2000;ICOLD, 2011;Lehner et al, 2011;Shang et al, 2018). In recent years, many countries (including China) have also actively adopted reservoir operations to mitigate the adverse effects of reservoirs and maintain the health of river ecosystems.…”

Section: Introductionmentioning

confidence: 99%

Modeling and simulating of reservoir operation using the artificial neural network, support vector regression, deep learning algorithm

Zhang

Peng

Wang

et al. 2018

Journal of Hydrology

274

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Reservoirs and dams are vital human-built infrastructures that play essential roles in flood control, hydroelectric power generation, water supply, navigation, and other functions. The realization of those functions requires efficient reservoir operation, and the effective controls on the outflow from a reservoir or dam. Over the last decade, artificial intelligence (AI) techniques have become increasingly popular in the field of streamflow forecasts, reservoir operation planning and scheduling approaches. In this study, three AI models, namely, the backpropagation (BP) neural network, support vector regression (SVR) technique, and long short-term memory (LSTM) model, are employed to simulate reservoir operation at monthly, daily, and hourly time scales, using approximately 30 years of historical reservoir operation records. This study aims to summarize the influence of the parameter settings on model performance and to explore the applicability of the LSTM model to reservoir operation simulation. The results show the following: (1) for the BP neural network and LSTM model, the effects of the number of maximum iterations on model performance should be prioritized; for the SVR model, the simulation performance is directly related to the selection of the kernel function, and sigmoid and RBF kernel functions should be prioritized; (2) the BP neural network and SVR are suitable for the model to learn the operation rules of a reservoir from a small amount of data; and (3) the LSTM model is able to effectively reduce the time consumption and memory storage required by other AI models, and demonstrate good capability in simulating low-flow conditions and the outflow curve for the peak operation period.

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Section: Introductionmentioning

confidence: 99%

Modeling and simulating of reservoir operation using the artificial neural network, support vector regression, deep learning algorithm

Zhang

Peng

Wang

et al. 2018

Journal of Hydrology

274

View full text Add to dashboard Cite

show abstract

“…One important character of hydro dams in China is that, they are usually part of a hydro station cascade, such as Three Gorges-Gezhouba, Xiluodu-Xiangjiaba, Longyangxia-Laxiwa-Lijiaxia-Gongboxia-Qingtongxia [42,43]. In such cascades, the dams are chained along the same river, and the downstream dams' inflow largely depends on their upstream stations' turbine control or spillage.…”

Section: Hydroelectricity Time Seriesmentioning

confidence: 99%

The role of hydro power, storage and transmission in the decarbonization of the Chinese power system

et al. 2019

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Deep decarbonization of the electricity sector can be provided by a high penetration of renewable sources such as wind, solar PV and hydro power. Flexibility from hydro and storage complements the high temporal variability of wind and solar, and transmission infrastructure helps the power balancing by moving electricity in the spatial dimension. We study cost-optimal highly-renewable Chinese power systems under ambitious CO 2 emission reduction targets, by deploying a 31-node hourly-resolved technoeconomic optimization model supported by a validated weather-converted 38-year-long renewable power generation and electricity demand dataset. With a new realistic reservoir hydro model, we find that if CO 2 emission reduction goes beyond 70%, storage facilities such as hydro, battery and hydrogen become necessary for a moderate system cost. Numerical results show that these flexibility components can lower renewable curtailment by two thirds, allow higher solar PV share by a factor of two and contribute to covering summer cooling demand. We show that expanding unidirectional high-voltage DC lines on top of the regional inter-connections is technically sufficient and more economical than ultra-high-voltage-AC-connected "One-Net" grid. Finally, constraining transmission volume from the optimum by up to 25% does not push total costs much higher, while the significant need for battery storage remains even with abundant interconnectivity.

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“…Operating rules are widely used in the reservoir long-term operation because of it provide a more practical and reliable guidance to reservoir system operators in the current period [13]. With in-depth study, cascade reservoirs confronts increasing complexity of the operation context and involves with challenges including various decision variables, multiple conflicting objectives and constraints [14]. The best way to deal with this is the coupling of multi-objective evolutionary algorithms (MOEAs) and simulation models [15], typically produce a set of Pareto optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

Operation Rules Optimization of Cascade Reservoirs Based on Multi-Objective Tangent Algorithm

Zhu

Hui

et al. 2019

IEEE Access

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Obtaining optimal operation rules for cascade reservoirs is crucial for making the most of the comprehensive benefits of reservoirs. However, the operation of cascade reservoirs is generally complex, which involves with challenges including various decision variables, multiple conflicting objectives and constraints. In this paper, a new algorithm named multi-objective tangent algorithm (MOTA) is proposed for optimizing operation rules of cascade reservoirs with the objectives of hydropower generation, ecology and navigation. The performance of MOTA is firstly validated through several well-known benchmark problems. Then it is applied to a case study of cascade reservoirs optimization in China's Pearl River. Using observed inflow data (1956-2015) of cascade reservoirs, a uniform Pareto front is obtained eventually via MOTA after 1000 generations. The optimal operation rules fully considers the comprehensive benefits of hydropower generation, ecology and navigation in China's Pearl River. The optimal operation rules can be used as a guidance tool for decision makers, through the objectives' tradeoff without having to embed a priori preferences in the decision process. Finally, this paper use observed inflow data (2016) of cascade reservoirs for examining the operational rule to comprehend the analysis under different optimal operation rules. The obtained results show that MOTA can be a viable alternative for generating optimal operation rules for cascade reservoirs planning and management. INDEX TERMS Operation rules, tangent algorithm, multi-objective optimization, cascade reservoirs.

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China’ energy-water nexus: Hydropower generation potential of joint operation of the Three Gorges and Qingjiang cascade reservoirs

Cited by 40 publications

References 48 publications

Modeling and simulating of reservoir operation using the artificial neural network, support vector regression, deep learning algorithm

Modeling and simulating of reservoir operation using the artificial neural network, support vector regression, deep learning algorithm

The role of hydro power, storage and transmission in the decarbonization of the Chinese power system

Operation Rules Optimization of Cascade Reservoirs Based on Multi-Objective Tangent Algorithm

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