A Multiobjective Stochastic Programming Model for Hydropower Hedging Operations under Inexact Information

Xu, Bin; Zhong, Ping‐an; Wu, Yenan; Fu, Fangming; Chen, Yuting; Zhao, Yong

doi:10.1007/s11269-017-1771-x

Cited by 18 publications

(6 citation statements)

References 24 publications

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“…Tang et al [185,186] Heihe River Basin Li and Guo [107], Li et al [109,111] and Zhang et al [231] Heshui River Basin Li et al [117], Liu et al [128,132] and Xu et al [212] Huai River Basin Gu et al [73] and Li et al [112] Hun River Xu et al [215,216] Kaidu-Kongque River Basin Huang et al [88,90], Li et al [125], Zeng et al [222,224,[226][227][228][229] and Zhou et al [242] Lake Tai Watershed Liu et al [134] and Xu and Huang [217] Miyun Reservoir Han et al [81] and Rong et al [175] Nansihu Lake Basin Xie et al [207,209] Tarim River Basin Huang et al [88][89][90] Three Gorges Reservoir Feng et al [58], Han et al [79], Han et al [87], Huang et al [91], Li et al [126], Xu et al [211], Yuan et al [220] and Zhang et al [236] Xiangxi River Basin Han et al [79], Hu et al [86,87], Huang et al …”

Section: China Ertan Reservoirmentioning

confidence: 99%

Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty

Archibald

Marshall

2018

Environ Model Assess

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Economic development, variation in weather patterns and natural disasters focus attention on the management of water resources. This paper reviews the literature on the development of mathematical programming models for water resource management under uncertainty between 2010 and 2017. A systematic search of the academic literature identified 448 journal articles on water resource management for examination. Bibliometric analysis is employed to investigate the methods that researchers are currently using to address this problem and to identify recent trends in research in the area. The research reveals that stochastic dynamic programming and multistage stochastic programming are the methods most commonly applied. Water resource allocation, climate change, water quality and agricultural irrigation are amongst the most frequently discussed topics in the literature. A more detailed examination of the literature on each of these topics is included. The findings suggest that there is a need for mathematical programming models of large-scale water systems that deal with uncertainty and multiobjectives in an effective and computationally efficient way.

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Section: China Ertan Reservoirmentioning

confidence: 99%

Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty

Archibald

Marshall

2018

Environ Model Assess

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“…Stochastic optimization is a technique for solving optimization problems where partial information is uncertain, and related uncertainties can be described with estimated probability distributions (Côté and Leconte, 2016). Stochastic optimization has been successfully used in many areas, and proved to be superior to traditional methods in providing better insights of optimization results (Bayesteh and Azari, 2021;Xu et al, 2017;.…”

Section: Stochastic Optimization Model For Reservoir Real-time Operationmentioning

confidence: 99%

Tracking the Uncertainty Propagation Process Between Hydrological Forecasting and Reservoir Real-Time Optimal Operation

Zhu¹,

Zhong²,

Xu³

et al. 2021

Preprint

Self Cite

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The inherent uncertainty in hydrological forecasting poses a challenge for reservoir real-time optimal operation. In this paper, a stochastic framework is proposed to track the uncertainty propagation process between hydrological forecasting and reservoir operation. The framework simulates the comprehensive uncertainty of hydrological forecasts in the form of ensemble forecasts and scenario trees. Based on the derived analytic relationship between the performance metric Nash-Sutcliffe efficiency coefficient (NSE) and forecast uncertainty probability distribution, we use three methods (two are commonly used classical methods and one is the Gaussian copula method) simultaneously to generate inflow forecast ensembles. Compared with the two classical methods, the Gaussian copula method additionally takes into account the temporal correlation of reservoir inflows. Then, the neural gas method is employed to transform the generated ensembles into a scenario tree, which is further used as an input for reservoir stochastic optimization. To improve the adaptability to uncertainties in inflow forecasts, we establish a stochastic optimization model that optimizes the expectation of objective values over all scenarios. Meanwhile, we propose a parallel differential evolution (DE) algorithm based on parallel computing techniques for solving the stochastic optimization model efficiently. Risk assessment is performed to capture the uncertainty and corresponding risk associated with the reservoir optimal decision. The proposed framework is demonstrated in a flood control reservoir system in China. Furthermore, we conduct several numerical experiments to explore the effect of forecast uncertainty level and temporal correlation on reservoir real-time optimal operation. The results indicate that the temporal correlation of inflows must be considered in inflow stochastic simulation and reservoir stochastic optimization, otherwise the operational risk is likely to be overestimated or underestimated, thus leading to operation failures. Based on the risk simulation surface, reservoir operators can examine the robustness of operational decisions and thus make more reliable final decisions.

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“…That means we can use the probability theory to solve this kind of indeterministic phenomenon. Based on the probability theory, stochastic multi-objective programming (SMOP) problem has been presented such as [4]- [6]. However, due to economic or technical reasons, we sometimes cannot obtain enough sample data to estimate a probability distribution.…”

Section: Introductionmentioning

confidence: 99%

A Novel Ideal Point Method for Uncertain Random Multi-Objective Programming Problem Under P_E Criterion

Yao

Wang

Liang³

et al. 2019

IEEE Access

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There are two kinds of methods for uncertain random multi-objective programming (URMOP) problem now. One is to convert the URMOP problem into deterministic multi-objective programming (DMOP) problem directly, and then solves the DMOP problem, which neglects the nature of the uncertainty and randomness. The other is to use the linear weighting method (LVM) to convert the URMOP problem into the uncertain random single-objective programming (URSOP) problem, and then convert it into the deterministic single-objective programming (DSOP) problem, which can be solved directly. However, the LVM has limited application range and low reliability. In this paper, we propose a new method named ideal point method (IPM) for solving the URMOP problem. First, we define the ideal point of URMOP. Based on different modules, we propose three different IPMs named SD-IPM, SWS-IPM, and WMM-IPM. It is then proved that under the P E criterion, the three IPMs can transform the URMOP problem into its equivalent URSOP problem, that is, the optimal solution of the transformed URSOP problem is proved to be the Pareto efficient solution of the original URMOP problem. Then, the URSOP problem can be transformed into its equivalent DSOP problem, which can be solved directly. The example discusses the differences and application range of the IPMs and other methods. The influences of weights are discussed simultaneously.

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A Multiobjective Stochastic Programming Model for Hydropower Hedging Operations under Inexact Information

Cited by 18 publications

References 24 publications

Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty

Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty

Tracking the Uncertainty Propagation Process Between Hydrological Forecasting and Reservoir Real-Time Optimal Operation

A Novel Ideal Point Method for Uncertain Random Multi-Objective Programming Problem Under P_E Criterion

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A Multiobjective Stochastic Programming Model for Hydropower Hedging Operations under Inexact Information

Cited by 18 publications

References 24 publications

Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty

Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty

Tracking the Uncertainty Propagation Process Between Hydrological Forecasting and Reservoir Real-Time Optimal Operation

A Novel Ideal Point Method for Uncertain Random Multi-Objective Programming Problem Under PE Criterion

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A Novel Ideal Point Method for Uncertain Random Multi-Objective Programming Problem Under P_E Criterion