An enhanced decomposition algorithm for multistage stochastic hydroelectric scheduling

Morton, David P.

doi:10.1007/bf02187647

Cited by 52 publications

(29 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We refer the reader to the work in [32] for a more detailed description of Benders' decomposition and for a more detailed discussion on valid cuts [33].…”

Section: Theorymentioning

confidence: 99%

Automatic selection of candidate investments for Transmission Expansion Planning

Lumbreras

Ramos

Sánchez

2014

International Journal of Electrical Power & Energy Systems

View full text Add to dashboard Cite

“…We refer the reader to the work in [32] for a more detailed description of Benders' decomposition and for a more detailed discussion on valid cuts [33].…”

Section: Theorymentioning

confidence: 99%

Automatic selection of candidate investments for Transmission Expansion Planning

Lumbreras

Ramos

Sánchez

2014

International Journal of Electrical Power & Energy Systems

View full text Add to dashboard Cite

“…In this figure, the fast-forward fast-backward tree traversing strategy [18] is used. A discussion on alternative tree traversing strategies (Shuffle, Cautious) can be found in [10] for instance.…”

Section: Matricesmentioning

confidence: 99%

SDDP for some interstage dependent risk-averse problems and application to hydro-thermal planning

Guigues

2013

Comput Optim Appl

View full text Add to dashboard Cite

Abstract. We consider interstage dependent stochastic linear programs where both the random right-hand side and the model of the underlying stochastic process have a special structure. Namely, for equality constraints (resp. inequality constraints) the right-hand side is an affine function (resp. a given function bt) of the process value for the current time step t. As for m-th component of the process at time step t, it depends on previous values of the process through a function htm.For this type of problem, to obtain an approximate policy under some assumptions for functions bt and htm, we detail a stochastic dual dynamic programming algorithm. Our analysis includes some enhancements of this algorithm such as the definition of a state vector of minimal size, the computation of feasibility cuts without the assumption of relatively complete recourse, as well as efficient formulas for sharing optimality and feasibility cuts between nodes of the same stage. The algorithm is given for both a non-risk-averse and a risk-averse model. We finally provide preliminary results comparing the performances of the recourse functions corresponding to these two models for a real-life application.

show abstract

“…Scenario-based optimisation has been used successfully in several cases to make decisions while taking into consideration possible future scenarios (see, for example, Ramos, 1992, Morton, 1996, Pallotino et al, 2002, Li et al, 2003, Mulvey et al, 2004and Beraldi et al, 2006.…”

Section: Multistage Scenario Stochastic Optimisationmentioning

confidence: 99%

A multistage scenario optimisation procedure to plan annualised working hours under demand uncertainty

Lusa

Corominas

Muñoz

2008

International Journal of Production Economics

View full text Add to dashboard Cite

The annualisation of working hours (i.e., the irregular distribution of the total number of working hours over the course of a year) makes it possible to adapt production capacity to fluctuations in demand. The required capacity, which is an essential data for the optimal planning of working time, usually depends on several complex factors. Often, it is impossible to reliably predict the required capacity or it is unrealistic to adjust it to a probability distribution. In some cases, it is possible to determine a set of required capacity scenarios, each with a related probability. This paper presents a multistage stochastic optimisation model that provides a robust solution (i.e., feasible for any possible scenario) and minimises the expected total capacity shortage.

show abstract

An enhanced decomposition algorithm for multistage stochastic hydroelectric scheduling

Abstract: Public reporting burden lor this collection of information is estimated B average 1 hour per response, including the time far reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the cotection of information. Send

Cited by 52 publications

References 11 publications

Automatic selection of candidate investments for Transmission Expansion Planning

Automatic selection of candidate investments for Transmission Expansion Planning

SDDP for some interstage dependent risk-averse problems and application to hydro-thermal planning

A multistage scenario optimisation procedure to plan annualised working hours under demand uncertainty

Contact Info

Product

Resources

About