Scenario-Tree Modeling for Stochastic Short-Term Hydropower Operations Planning

Séguin, Sara; Audet, Charles; Côté, Pascal

doi:10.1061/(asce)wr.1943-5452.0000854

Cited by 22 publications

(15 citation statements)

References 25 publications

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“…2), rather than a scenario tree (with a limited number of nodes for approximating the stochastic inflows). In numerical experiments on short-term hydropower planning using a scenario fan formulation with decisions updated in a rolling horizon, Séguin et al (2017a) achieved comparable solutions to those obtained from a scenario tree model, while requiring less computational effort. Although the performance of the scenario fan and the scenario tree models depend on multiple factors, such as the specific problem, its formulation and the implemented solution method, the empirical results in Séguin et al (2017a) suggest that a scenario fan approach may be promising in some practical applications where computational times are critical.…”

Section: Modeling Approachmentioning

confidence: 93%

Accelerating Benders decomposition for short-term hydropower maintenance scheduling

Rodríguez

Anjos

Côté

et al. 2021

European Journal of Operational Research

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Maintenance of power generators is essential for reliable and efficient electricity production. Because generators under maintenance are typically inactive, optimal planning of maintenance activities must consider the impact of maintenance outages on the system operation. However, in hydropower systems finding a minimum cost maintenance schedule is a challenging optimization problem due to the uncertainty of the water inflows and the nonlinearity of the hydroelectricity production.

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Section: Modeling Approachmentioning

confidence: 93%

Accelerating Benders decomposition for short-term hydropower maintenance scheduling

Rodríguez

Anjos

Côté

et al. 2021

European Journal of Operational Research

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“…As reliable SF observations are not available in data-scarce regions to validate the reservoir inflow forecasts, the assumption of a deterministic first stage may not be appropriate. For example, Séguin et al (2017) highlight the importance of stochastic scenarios on hydropower by comparing scenario trees of varying complexities: (1) full scenario tree with SPWR-D, (2) scenario tree with only the median scenario at all stages, and (3) scenario fan. They conclude that, for hydropower planning, stochastic scenarios (scenario tree or fans) are preferable over deterministic scenarios.…”

Section: Formulation Of the Hydropower Optimization Modelmentioning

confidence: 99%

“…They conclude that, for hydropower planning, stochastic scenarios (scenario tree or fans) are preferable over deterministic scenarios. In this study, we extend the comparisons presented in Séguin et al (2017) by reformulating the stochastic programming with recourse model for hydropower planning to consider the uncertainty in seasonal reservoir inflow forecasts at the first/immediate stage. Specifically, we evaluate the impact of considering deterministic and stochastic first stages in reservoir inflows on the optimized released decisions and hence hydropower production.…”

Section: Formulation Of the Hydropower Optimization Modelmentioning

confidence: 99%

Seasonal Hydropower Planning for Data‐Scarce Regions Using Multimodel Ensemble Forecasts, Remote Sensing Data, and Stochastic Programming

Koppa

Gebremichael

Zambon

et al. 2019

Water Resources Research

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In data‐scarce regions, seasonal hydropower planning is hindered by the unavailability of reliable long‐term streamflow observations, which are required for the construction of inflow scenario trees. In this study, we develop a methodological framework to overcome the problem of streamflow data scarcity by combining precipitation forecasts from ensemble numerical weather prediction models, spatially distributed hydrologic models, and stochastic programming. We use evapotranspiration as a proxy for streamflow in generating reliable reservoir inflow forecasts. Using the framework, we compare three different formulations of inflow scenario structures and their applicability to data‐scarce regions: (1) a single deterministic forecast, (2) a scenario fan with the first stage deterministic, and (3) a scenario fan with all stages stochastic. We apply the framework to a cascade of two reservoirs in the Omo‐Gibe River basin in Ethiopia. Future reservoir inflows are generated using a 3‐model 30‐member ensemble seasonal precipitation forecast from the North American Multimodel Ensemble and the Noah‐MP hydrologic model. We then perform deterministic and stochastic optimization for hydropower operation and planning. Comparing the results from the three different inflow scenario structures, we observe that the uncertainty in reservoir inflows is significant only for the dry stages of the planning horizon. In addition, we find that the impact of model parameter uncertainty on hydropower production is significant (0.14–0.18×106 MWh).

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“…The method has been used previously, (e.g. in Shukla andLettenmaier, 2011 andGreuell et al, 2018) where the pseudo-observations are shown to be the best estimate of the true conditions of the catchment. The ESP forecasts were generated following a straightforward procedure:…”

Section: Preparation Of Esp Forecastsmentioning

confidence: 99%

Analysis of the effects of biases in ensemble streamflow prediction (ESP) forecasts on electricity production in hydropower reservoir management

Arsenault

Côté

2019

Hydrol. Earth Syst. Sci.

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Abstract. This paper presents an analysis of the effects of biased extended streamflow prediction (ESP) forecasts on three deterministic optimization techniques implemented in a simulated operational context with a rolling horizon test bed for managing a cascade of hydroelectric reservoirs and generating stations in Québec, Canada. The observed weather data were fed to the hydrological model, and the synthetic streamflow subsequently generated was considered to be a proxy for the observed inflow. A traditional, climatology-based ESP forecast approach was used to generate ensemble streamflow scenarios, which were used by three reservoir management optimization approaches. Both positive and negative biases were then forced into the ensembles by multiplying the streamflow values by constant factors. The optimization method's response to those biases was measured through the evaluation of the average annual energy generation in a forward-rolling simulation test bed in which the entire system is precisely and accurately modelled. The ensemble climate data forecasts, the hydrological modelling and ESP forecast generation, optimization model, and decision-making process are all integrated, as is the simulation model that updates reservoir levels and computes generation at each time step. The study focussed on one hydropower system both with and without minimum baseload constraints. This study finds that the tested deterministic optimization algorithms lack the capacity to compensate for uncertainty in future inflows and therefore place the reservoir levels at greater risk to maximize short-term profit. It is shown that for this particular system, an increase in ESP forecast inflows of approximately 5 % allows managing the reservoirs at optimal levels and producing the most energy on average, effectively negating the deterministic model's tendency to underestimate the risk of spilling. Finally, it is shown that implementing minimum load constraints serves as a de facto control on deterministic bias by forcing the system to draw more water from the reservoirs than what the models consider to be optimal trajectories.

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Scenario-Tree Modeling for Stochastic Short-Term Hydropower Operations Planning

Cited by 22 publications

References 25 publications

Accelerating Benders decomposition for short-term hydropower maintenance scheduling

Accelerating Benders decomposition for short-term hydropower maintenance scheduling

Seasonal Hydropower Planning for Data‐Scarce Regions Using Multimodel Ensemble Forecasts, Remote Sensing Data, and Stochastic Programming

Analysis of the effects of biases in ensemble streamflow prediction (ESP) forecasts on electricity production in hydropower reservoir management

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