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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: In this paper, a stochastic dynamic programming algorithm is used to solve the investment problem, where uncertainty in demand is represented as a discrete Markov chain, and the stochastically dynamic model allows us to evaluate investment projects in new base and peak load power generation as real options, and determine optimal timing of the investments.
Abstract: This work presents a novel model for optimization of investments in new power generation under uncertainty. The model can calculate optimal investment strategies under both centralized social welfare and decentralized profit objectives. The power market is represented with linear supply and demand curves. A stochastic dynamic programming algorithm is used to solve the investment problem, where uncertainty in demand is represented as a discrete Markov chain. The stochastic dynamic model allows us to evaluate investment projects in new base and peak load power generation as real options, and determine optimal timing of the investments. In a case study, we use the model to compare optimal investment strategies under centralized and decentralized decision making. A number of interesting results follow by varying the assumptions about market structure and price response on the demand side.

199 citations

Journal ArticleDOI
TL;DR: In this paper, the authors investigated the operation of a GB integrated gas and electricity network considering the uncertainty in wind power forecast using three operational planning methods: deterministic, two-stage stochastic programming, and multistage Stochastic Programming.
Abstract: In many power systems, in particular in Great Britain (GB), significant wind generation is anticipated and gas-fired generation will continue to play an important role. Gas-fired generating units act as a link between the gas and electricity networks. The variability of wind power is, therefore, transferred to the gas network by influencing the gas demand for electricity generation. Operation of a GB integrated gas and electricity network considering the uncertainty in wind power forecast was investigated using three operational planning methods: deterministic, two-stage stochastic programming, and multistage stochastic programming. These methods were benchmarked against a perfect foresight model which has no uncertainty associated with the wind power forecast. In all the methods, thermal generators were controlled through an integrated unit commitment and economic dispatch algorithm that used mixed integer programming. The nonlinear characteristics of the gas network, including the gas flow along pipes and the operation of compressors, were taken into account and the resultant nonlinear problem was solved using successive linear programming. The operational strategies determined by the stochastic programming methods showed reductions of the operational costs compared to the solution of the deterministic method by almost 1%. Also, using the stochastic programming methods to schedule the thermal units was shown to make a better use of pumped storage plants to mitigate the variability and uncertainty of the net demand.

199 citations

Journal ArticleDOI
TL;DR: This paper develops a two-stage stochastic programming approach for process planning under uncertainty by extending a deterministic mixed-integer linear programming formulation to account for the presence of discrete random parameters and devise a decomposition algorithm for the solution of the Stochastic model.
Abstract: This paper develops a two-stage stochastic programming approach for process planning under uncertainty. We first extend a deterministic mixed-integer linear programming formulation to account for t...

199 citations

Journal ArticleDOI
TL;DR: In this paper, a stochastic bi-objective supply chain design model for the efficient (cost minimizing) and effective (delivery time minimizing) supply of blood in disasters is presented.

199 citations

Journal ArticleDOI
Zhe Yu1, Liyan Jia1, Mary Murphy-Hoye2, Annabelle Pratt2, Lang Tong1 
TL;DR: The problem of modeling and stochastic optimization for home energy management is considered, and a model predictive control based heuristic is proposed for the scheduling of loads of different characteristics.
Abstract: The problem of modeling and stochastic optimization for home energy management is considered. Several different types of load classes are discussed, including heating, ventilation, and air conditioning unit, plug-in hybrid electric vehicle, and deferrable loads such as washer and dryer. A first-order thermal dynamic model is extracted and validated using real measurements collected over an eight months time span. A mixed integer multi-time scale stochastic optimization is formulated for the scheduling of loads of different characteristics. A model predictive control based heuristic is proposed. Numerical simulations coupled with real data measurements are used for performance evaluation and comparison studies.

197 citations


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