Self-Scheduling Short-Term Unit Commitment and Loading Problem

Séguin, Sara; Côté, Pascal; Audet, Charles

doi:10.1109/tpwrs.2014.2383911

Cited by 40 publications

(26 citation statements)

References 17 publications

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“…The two phase deterministic optimization models taken from [5] are updated to consider stochastic inflows. This section presents the modeling of the short-term problem as well as the mathematical formulations.…”

Section: Stochastic Short-term Hydropower Modelmentioning

confidence: 99%

“…Optimization variables of this nonlinear stochastic multistage mixed integer problem are water flows, volumes and number of working turbines, for each node and plant in the scenario tree. The model must choose one surface of number of working turbines among available, but we have shown [5] that relaxing these variables leads to an integer solution. Therefore, we solve a nonlinear stochastic multistage continuous problem.…”

Section: Phase I: Loading Problemmentioning

confidence: 99%

“…The above short-term loading problem is described in more details in [5]. We now show how to integrate a water-value function for the remaining water at the end of the planning horizon.…”

Section: Phase I: Loading Problemmentioning

confidence: 99%

“…Few papers have looked specifically into short-term hydropower models with uncertain inflows. In [5], a short-term hydropower optimization model treats deterministic inflows. Water head variations are considered and nonlinearities and nonconvexities of the hydropower production function are accounted for.…”

Section: Introductionmentioning

confidence: 99%

“…Applying the theory outlined in [23], we detail/provide a nonparametric scenario generation approach that relies on the information in the history of inflows. We expand [5] by introducing stochasticity to both the loading and unit commitment problems.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic short-term hydropower planning with inflow scenario trees

Séguin

Fleten

Côté

et al. 2017

European Journal of Operational Research

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This paper presents an optimization approach to solve the short-term hydropower unit commitment and loading problem with uncertain inflows. A scenario tree is built based on a forecasted fan of inflows, which in turn comes from the weather forecast and the historical weather realizations. The tree-building approach seeks to minimize the nested distance between the stochastic process of historical inflow data and the multistage stochastic process represented in the scenario tree. A two-phase multistage stochastic model is used to solve the problem. One of the main features of the modeling of the problem is that the effect of varying water head on production is carefully heeded. The proposed approach is tested on a 31 day rolling horizon with daily forecasted inflows for three power plants situated in the province of Quebec, Canada, that belong to the company Rio Tinto.

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Section: Stochastic Short-term Hydropower Modelmentioning

confidence: 99%

Section: Phase I: Loading Problemmentioning

confidence: 99%

“…The above short-term loading problem is described in more details in [5]. We now show how to integrate a water-value function for the remaining water at the end of the planning horizon.…”

Section: Phase I: Loading Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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