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Stochastic programming

About: Stochastic programming is a research topic. Over the lifetime, 12343 publications have been published within this topic receiving 421049 citations.


Papers
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Journal ArticleDOI
TL;DR: The incorporation of a time-consistent coherent risk measure into a multi-stage stochastic programming model is considered, so that the model can be solved using a SDDP-type algorithm.

217 citations

Book ChapterDOI
21 Oct 2009
TL;DR: Experiments on both random and real word problems show that Light Robustness is sometimes able to produce solutions whose quality is comparable with that obtained through stochastic programming or robust models, though it requires less effort in terms of model formulation and solution time.
Abstract: We consider optimization problems where the exact value of the input data is not known in advance and can be affected by uncertainty. For these problems, one is typically required to determine a robust solution, i.e., a possibly suboptimal solution whose feasibility and cost is not affected heavily by the change of certain input coefficients. Two main classes of methods have been proposed in the literature to handle uncertainty: stochastic programming (offering great flexibility, but often leading to models too large in size to be handled efficiently), and robust optimization (whose models are easier to solve but sometimes lead to very conservative solutions of little practical use). In this paper we investigate a heuristic way to model uncertainty, leading to a modelling framework that we call Light Robustness. Light Robustness couples robust optimization with a simplified two-stage stochastic programming approach, and has a number of important advantages in terms of flexibility and ease to use. In particular, experiments on both random and real word problems show that Light Robustness is sometimes able to produce solutions whose quality is comparable with that obtained through stochastic programming or robust models, though it requires less effort in terms of model formulation and solution time.

216 citations

Book ChapterDOI
01 Jan 1983
TL;DR: Solutions techniques for stochastic programs are reviewed and particular emphasis is placed on those methods that allow us to proceed by approximation.
Abstract: Solutions techniques for stochastic programs are reviewed. Particular emphasis is placed on those methods that allow us to proceed by approximation. We consider both stochastic programs with recourse and stochastic programs with chance-constraints.

214 citations

Journal ArticleDOI
TL;DR: A novel hybrid robust-stochastic programming (HRSP) approach to simultaneously model two different types of uncertainties by including stochastic scenarios for transportation costs and polyhedral uncertainty sets for demands and returns is developed.

212 citations

Journal ArticleDOI
TL;DR: In this article, a multi-timescale coordinated stochastic voltage/var control method for high renewable-penetrated distribution networks is proposed, which utilizes multiple devices to counteract uncertain voltage fluctuation and deviation.
Abstract: This paper proposes a multi-timescale coordinated stochastic voltage/var control method for high renewable-penetrated distribution networks. It aims to utilize multiple devices to counteract uncertain voltage fluctuation and deviation. In the hourly timescale (first stage), capacitor banks and transformer tap changers are scheduled before stochastic renewable output and load variations are realized. In the 15-min timescale (second stage), inverters that interface the renewable energy resources provide var support to supplement the first-stage decision after uncertainty is observed. The coordination is model as a two-stage stochastic programming problem with scenario reduction. It is then converted to a deterministic mixed-integer quadratic programming equivalence model and solved by commercial solvers combined. Compared with existing methods, the proposed volt/var control can achieve lower expected energy loss and can sustain a secure voltage level under random load demand and renewable power injection. The proposed method is verified on the IEEE 33-bus distribution network and compared with existing practices.

210 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023175
2022423
2021526
2020598
2019578
2018532