Nonconvex Medium-Term Hydropower Scheduling by Stochastic Dual Dynamic Integer Programming

Hydropower producers rely on stochastic optimization when scheduling their resources over long periods of time. Due to its computational complexity, the optimization problem is normally cast as a stochastic linear program. In a future power market with more volatile power prices, it becomes increasi...

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Vydáno v:IEEE transactions on sustainable energy Ročník 10; číslo 1; s. 481 - 490
Hlavní autoři: Hjelmeland, Martin N., Zou, Jikai, Helseth, Arild, Ahmed, Shabbir
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.01.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Bending machines
Case studies
Computer applications
Dependent variables
Dynamic programming
Dynamic scheduling
Hydroelectric power
Hydroelectric power generation
Integer programming
Linear programming
Linearization
Modeling
Optimization
Pricing
Reservoirs
Resource scheduling
State of the art
Stochastic processes
Unit commitment
ISSN:1949-3029, 1949-3037
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Shrnutí:Hydropower producers rely on stochastic optimization when scheduling their resources over long periods of time. Due to its computational complexity, the optimization problem is normally cast as a stochastic linear program. In a future power market with more volatile power prices, it becomes increasingly important to capture parts of the hydropower operational characteristics that are not easily linearized, e.g., unit commitment and nonconvex generation curves. Stochastic dual dynamic programming (SDDP) is a state-of-the-art algorithm for long- and medium-term hydropower scheduling with a linear problem formulation. A recently proposed extension of the SDDP method known as stochastic dual dynamic integer programming (SDDiP) has proven convergence also in the nonconvex case. We apply the SDDiP algorithm to the medium-term hydropower scheduling (MTHS) problem and elaborate on how to incorporate stagewise-dependent stochastic variables on the right-hand sides and the objective of the optimization problem. Finally, we demonstrate the capability of the SDDiP algorithm on a case study for a Norwegian hydropower producer. The case study demonstrates that it is possible but time-consuming to solve the MTHS problem to optimality. However, the case study shows that a new type of cut, known as strengthened Benders cut, significantly contributes to close the optimality gap compared to classical Benders cuts.
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ISSN:1949-3029
1949-3037
DOI:10.1109/TSTE.2018.2805164