An argument-driven classification and comparison of reservoir operation optimization methods

Dobson, Barnaby; Wagener, Thorsten; Pianosi, Francesca

doi:10.1016/j.advwatres.2019.04.012

Cited by 83 publications

(58 citation statements)

References 98 publications

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“…Thus, it is critical that designing and implementing coordinated reservoir operations for conflicting objectives not only employ advanced optimization techniques but also actively involve stakeholders in the process. As noted by repeated practitioner surveys (Dobson et al, ; Rogers & Fiering, ; Whateley et al, ), translating advances in WRSA will require not only opening the black box of the optimization models, but involving practitioners in the development of those models so that they have faith in both the models' representations of the system, and their suggestions on how to manage it.…”

Section: Discussionmentioning

confidence: 99%

“…Yet, while the field has continually pushed new methods for optimizing the planning and management of water resources, repeated surveys of water managers have found low adoption of these methods in practice (Dobson et al, ; Rayner et al, ; Rogers & Fiering, ; Whateley et al, ). A number of factors have been found or proposed to be the cause of this low adoption, including severe risk aversion, institutional and legal constraints inhibiting or even prohibiting their use, real or perceived errors in the models/forecasts, lack of involvement of stakeholders in the modeling process, and the black box nature of systems models making their recommendations difficult to understand and explain (Brown et al, ; Rayner et al, ; Rogers & Fiering, ; Simonovic, ; Whateley et al, ).…”

Section: Introductionmentioning

confidence: 99%

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What Is Controlling Our Control Rules? Opening the Black Box of Multireservoir Operating Policies Using Time‐Varying Sensitivity Analysis

Quinn

Reed

Giuliani

et al. 2019

Water Resources Research

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Multireservoir systems are designed to serve multiple conflicting demands over varying time scales that may be out of phase with the system's hydroclimatic inputs. Adaptive, nonlinear reservoir control policies are often best suited to serve these needs. However, nonlinear operating policies are hard to interpret, so water managers tend to favor simple, static rules that may not effectively manage conflicts between the system's multisectoral demands. In this study, we introduce an analytical framework for opening the black box of optimized nonlinear operating policies, decomposing their time‐varying information sensitivities to show how their adaptive and coordinated release prescriptions better manage hydrologic variability. Interestingly, these information sensitivities vary significantly across policies depending on how they negotiate tradeoffs between conflicting objectives. We illustrate this analysis in the Red River basin of Vietnam, where four major reservoirs serve to protect the capital of Hanoi from flooding while also providing the surrounding region with electric power and meeting multisectoral water demands for the agricultural and urban economies. Utilizing Evolutionary Multi‐Objective Direct Policy Search, we are able to design policies that, using the same information as sequential if/then/else‐based operating guidelines developed by the government, outperform these traditional rules with respect to every objective. Policy diagnostics using time‐varying sensitivity analysis illustrate how the Evolutionary Multi‐Objective Direct Policy Search operations better adapt and coordinate information use to reduce food‐energy‐water conflicts in the basin. These findings accentuate the benefits of transitioning to dynamic operating policies in order to manage evolving hydroclimatic variability and socioeconomic demands in multipurpose reservoir networks.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

What Is Controlling Our Control Rules? Opening the Black Box of Multireservoir Operating Policies Using Time‐Varying Sensitivity Analysis

Quinn

Reed

Giuliani

et al. 2019

Water Resources Research

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“…WATHNET is also highly efficient in its simulation, so although WREW contains 1,252 nodes and 1,756 arcs, 1 year of simulation at a daily time step takes around only 2 min on a 3.6‐GHz processor. For context, the similar‐sized CALVIN water resource simulation model runs at around 10 min/year at a monthly time step on a 2‐GHz PC (Harou et al, 2010) (we note this is for context and not comparison since CALVIN's simulation philosophy is inherently different; CALVIN is a perfect foresight optimization model that represents operation with a release sequence as defined in Dobson et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

The Spatial Dynamics of Droughts and Water Scarcity in England and Wales

Dobson

Coxon

Freer

et al. 2020

Water Resources Research

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Water scarcity occurs when water demand exceeds natural water availability over a range of spatial and temporal scales. Though meteorological and hydrological droughts have been analyzed over large spatial scales, the impacts of water scarcity have typically been addressed at a catchment scale. Here we explore how droughts and water scarcity interact over a larger and more complex spatial domain, by combining climate, hydrological, and water resource system models at a national scale across England and Wales. This approach is essential in a highly connected and heterogeneous region like England and Wales, where we represent 80 different catchments; 70 different water resource zones; 16 water utility companies; and the water supply for over 50 million people. We find that if a reservoir's storage is in its first percentile (i.e., the volume that is exceeded 99% of the time), then there is, on average, a 40% chance that reservoirs in neighboring catchments will also be at or below their first percentile storage volume. The coincidence of low reservoir storage decays relatively quickly, stabilizing after about 100–150 km, implying that if interbasin transfers are to be provided to enhance drought resilience, they will need to be at least this length. Based on a large ensemble of future climate simulations, we show that extreme droughts in precipitation, streamflow, and reservoir storage volume are projected to worsen in every catchment. The probability of a year with water use restrictions doubles by 2050 and is four times worse by 2100.

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“…Dobson et al (2019), Rani and Moreira (2010), Ahmad et al (2014), Celeste and Billib (2009) and Labadie (2004) carried out extensive reviews of the most common optimization methods. Three main classes of optimization algorithms that are efficient for optimizing reservoir management are (1) linear and nonlinear programming (Arsenault and Côté, 2019;Yoo, 2009;Barros et al, 2003), (2) dynamic programming (Bellman, 1957) and its variants, deterministic dynamic programming (DDP) (Haguma and Leconte, 2018;Ming et al, 2017;Yuan et al, 2016), stochastic dynamic programming (SDP) (Wu et al, 2018;Yuan et al, 2016;Celeste and Billib, 2009;Tejada-Guibert et al, 1995), sampling stochastic dynamic programming (SSDP) (Haguma and Leconte, 2018;Faber and Stedinger, 2001;Kelman et al, 1990) and stochastic dual dynamic programming (SDDP) Tilmant and Kelman, 2007;Tilmant et al, 2008Tilmant et al, , 2011Pereira and Pinto, 1991), and (3) heuristic programming Ahmed and Sarma, 2005). The choice among these algorithms depends on many factors, such as the stakes and objectives to address, as well as the configuration of the system and the data available to parametrize and run the model.…”

Section: Introductionmentioning

confidence: 99%

Impact of the quality of hydrological forecasts on the management and revenue of hydroelectric reservoirs – a conceptual approach

Cassagnole

Ramos

Zalachori

et al. 2021

Hydrol. Earth Syst. Sci.

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Abstract. The improvement of a forecasting system and the continuous evaluation of its quality are recurrent steps in operational practice. However, the systematic evaluation of forecast value or usefulness for better decision-making is less frequent, even if it is also essential to guide strategic planning and investments. In the hydropower sector, several operational systems use medium-range hydrometeorological forecasts (up to 7–10 d ahead) and energy price predictions as input to models that optimize hydropower production. The operation of hydropower systems, including the management of water stored in reservoirs, is thus partially impacted by weather and hydrological conditions. Forecast value can be quantified by the economic gains obtained with the optimization of operations informed by the forecasts. In order to assess how much improving the quality of hydrometeorological forecasts will improve their economic value, it is essential to understand how the system and its optimization model are sensitive to sequences of input forecasts of different quality. This paper investigates the impact of 7 d streamflow forecasts of different quality on the management of hydroelectric reservoirs and the economic gains generated from a linear programming optimization model. The study is based on a conceptual approach. Flows from 10 catchments in France are synthetically generated over a 4-year period to obtain forecasts of different quality in terms of accuracy and reliability. These forecasts define the inflows to 10 hydroelectric reservoirs, which are conceptually parameterized. Relationships between forecast quality and economic value (hydropower revenue) show that forecasts with a recurrent positive bias (overestimation) and low accuracy generate the highest economic losses when compared to the reference management system where forecasts are equal to observed inflows. The smallest losses are observed for forecast systems with underdispersion reliability bias, while forecast systems with negative bias (underestimation) show intermediate losses. Overall, the losses (which amount to millions of Euros) represent approximately 1 % to 3 % of the revenue over the study period. Besides revenue, the quality of the forecasts also impacts spillage, stock evolution, production hours and production rates, with systematic over- and underestimations being able to generate some extreme reservoir management situations.

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An argument-driven classification and comparison of reservoir operation optimization methods

Cited by 83 publications

References 98 publications

What Is Controlling Our Control Rules? Opening the Black Box of Multireservoir Operating Policies Using Time‐Varying Sensitivity Analysis

What Is Controlling Our Control Rules? Opening the Black Box of Multireservoir Operating Policies Using Time‐Varying Sensitivity Analysis

The Spatial Dynamics of Droughts and Water Scarcity in England and Wales

Impact of the quality of hydrological forecasts on the management and revenue of hydroelectric reservoirs – a conceptual approach

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