Stochastic Programming for Improved Multiuse Reservoir Operation in Burkina Faso, West Africa

Etkin, Derek; Kirshen, Paul; Watkins, David; Roncoli, Carla; Sanon, Moussa; Some, Léopold; Dembélé, Youssouf; Sanfo, Judith; Zoungrana, Jacqueline; Hoogenboom, Gerrit

doi:10.1061/(asce)wr.1943-5452.0000396

Cited by 14 publications

(5 citation statements)

References 29 publications

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“…The problem is compounded by the fact that instances of lower than average water supply often coincide with higher than average need for irrigation. A wide range of mathematical programming techniques have been applied, individually and in combination, to support irrigation planning including multistage stochastic programming (28% of articles on irrigation in the sample including [34,54,173]), stochastic dynamic programming (25% of articles on irrigation in the sample including [12,22,158]), inexact programming, including fuzzy and interval-based programming, (36% of articles on irrigation in the sample, for example [35,105,152]) and nonlinear programming (14% of articles on irrigation in the sample including [10,25]). The problem has been studied at various levels ranging from the farm level [22,83,92] to the level of a large water system or country [107,144,221].…”

Section: Agricultural Irrigationmentioning

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: Agricultural Irrigationmentioning

confidence: 99%

Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty

Archibald

Marshall

2018

Environ Model Assess

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“…Under this representation, a two-or multi-stage problem can be formulated as a "deterministic equivalent" mathematical program (with due non-anticipativity contraints). Thus [25,26] formulate deterministic scenariotree-based linear programs to be used on a rolling horizon basis. The very large size of such deterministic programs usually calls for some form of decomposition.…”

Section: Models and Methods For Tactical Reservoir Managementmentioning

confidence: 99%

Approximate stochastic dynamic programming for hydroelectric production planning

Zéphyr

Lang

Lamond

et al. 2017

European Journal of Operational Research

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This paper presents a novel approach for approximate stochastic dynamic programming (ASDP) over a continuous state space when the optimization phase has a near-convex structure. The approach entails a simplicial partitioning of the state space. Bounds on the true value function are used to refine the partition. We also provide analytic formulae for the computation of the expectation of the value function in the "uni-basin" case where natural inflows are strongly correlated. The approach is experimented on several configurations of hydroenergy systems. It is also tested against actual industrial data.

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“…When new information regarding the inflow into reservoirs is available, the whole system is validated, and the required adjustments are made to the DSS before the start of the next stage. Several studies employ this approach for the operation of reservoir cascades (Wang et al 2012;Etkin et al 2015). However, such an approach is dependent on the availability of reservoir inflows at regular intervals for the validation of the entire DSS, which may not be possible in data-scarce regions such as Africa.…”

Section: Optimization Systemmentioning

confidence: 99%

A Scalable Earth Observations‐Based Decision Support System for Hydropower Planning in Africa

Koppa

Gebremichael

Hopson

et al. 2021

J American Water Resour Assoc

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A novel decision support system based on earth observation satellites, ensemble climate forecasts and hydrologic models for hydropower planning under deep data uncertainty in Africa.ABSTRACT: Hydropower is a key part of the increasing shift in power production from nonrenewables to renewable energy. In regions such as Africa, hydropower reservoirs are vital for achieving several sustainable development goals, including clean water, energy, and poverty elimination. However, the operations of hydropower reservoirs are often suboptimal due to the lack of hydrologic data for generating reliable inflow forecasts. Here, we present a decision support system (DSS) framework for hydropower planning at daily to seasonal time scales by combining data from earth observation satellites (EOS) with ensemble climate forecasts from dynamical models and hydrologic modeling. The large uncertainty inherent in satellite-based datasets is overcome by using a data validation framework which does not require ground-based measurements. In addition, an EOS evapotranspiration product is used as a proxy for streamflow in calibrating hydrologic models. Compared to a DSS forced with a climatological forecast (zero-skill), the hydropower production with the new DSS increased by 20%. The study highlights the advantage of using data from EOS in overcoming the issue of data scarcity in water resources applications, particularly in developing regions of the world such as Africa.

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Stochastic Programming for Improved Multiuse Reservoir Operation in Burkina Faso, West Africa

Cited by 14 publications

References 29 publications

Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty

Review of Mathematical Programming Applications in Water Resource Management Under Uncertainty

Approximate stochastic dynamic programming for hydroelectric production planning

A Scalable Earth Observations‐Based Decision Support System for Hydropower Planning in Africa

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