Relevant bibliographies by topics / Chance-constrained unit commitment

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  1. Journal articles
  2. Dissertations / Theses
  3. Conference papers

Academic literature on the topic 'Chance-constrained unit commitment'

Author: Grafiati
Published: 5 June 2025
Last updated: 14 July 2025

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Journal articles on the topic "Chance-constrained unit commitment"

1

Guo, Ge, Luckny Zephyr, José Morillo, Zongjie Wang, and C. Lindsay Anderson. "Chance constrained unit commitment approximation under stochastic wind energy." Computers & Operations Research 134 (October 2021): 105398. http://dx.doi.org/10.1016/j.cor.2021.105398.

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Wu, Chutian, Fouad Hasan, and Amin Kargarian. "Scalable nonparametric joint chance-constrained unit commitment with renewable uncertainty." Electric Power Systems Research 245 (August 2025): 111573. https://doi.org/10.1016/j.epsr.2025.111573.

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Li, Zhiwei, Tianran Jin, Shuqiang Zhao, and Jinshan Liu. "Power System Day-Ahead Unit Commitment Based on Chance-Constrained Dependent Chance Goal Programming." Energies 11, no. 7 (2018): 1718. http://dx.doi.org/10.3390/en11071718.

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Wang, Beibei, Xuechun Yang, Taylor Short, and Shengchun Yang. "Chance constrained unit commitment considering comprehensive modelling of demand response resources." IET Renewable Power Generation 11, no. 4 (2017): 490–500. http://dx.doi.org/10.1049/iet-rpg.2016.0397.

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Sundar, Kaarthik, Harsha Nagarajan, Line Roald, Sidhant Misra, Russell Bent, and Daniel Bienstock. "Chance-Constrained Unit Commitment With N-1 Security and Wind Uncertainty." IEEE Transactions on Control of Network Systems 6, no. 3 (2019): 1062–74. http://dx.doi.org/10.1109/tcns.2019.2919210.

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Singh, Bismark, Bernard Knueven, and Jean-Paul Watson. "Modeling flexible generator operating regions via chance-constrained stochastic unit commitment." Computational Management Science 17, no. 2 (2020): 309–26. http://dx.doi.org/10.1007/s10287-020-00368-3.

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Sheng, Si Qing, and Xiao Xia Sun. "Mixed Chance Constrained Unit Commitment Model for Power System Containing Wind Farm." Applied Mechanics and Materials 644-650 (September 2014): 3850–53. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.3850.

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This paper presents a new unit commitment model to solve the uncertainty of wind and load. The chance constrained programming is introduced in this paper. The uncertainty of wind and load is expressed as their prediction error. Considering their different characteristic, wind prediction error is indicated as a fuzzy variable, while load prediction error is represented as random variable. Different confidences reflect the different satisfaction of the constraints. Finally, example analysis shows that the proposed model is feasible and effectiveness.
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Hong, Ying-Yi, Gerard Francesco DG Apolinario, Tai-Ken Lu, and Chia-Chi Chu. "Chance-constrained unit commitment with energy storage systems in electric power systems." Energy Reports 8 (November 2022): 1067–90. http://dx.doi.org/10.1016/j.egyr.2021.12.035.

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Zhao, Chaoyue, Qianfan Wang, Jianhui Wang, and Yongpei Guan. "Expected Value and Chance Constrained Stochastic Unit Commitment Ensuring Wind Power Utilization." IEEE Transactions on Power Systems 29, no. 6 (2014): 2696–705. http://dx.doi.org/10.1109/tpwrs.2014.2319260.

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Ozturk, U. A., M. Mazumdar, and B. A. Norman. "A Solution to the Stochastic Unit Commitment Problem Using Chance Constrained Programming." IEEE Transactions on Power Systems 19, no. 3 (2004): 1589–98. http://dx.doi.org/10.1109/tpwrs.2004.831651.

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Dissertations / Theses on the topic "Chance-constrained unit commitment"

1

Van, ackooij Wim Stefanus. "Chance Constrained Programming : with applications in Energy Management." Phd thesis, Ecole Centrale Paris, 2013. http://tel.archives-ouvertes.fr/tel-00978519.

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In optimization problems involving uncertainty, probabilistic constraints are an important tool for defining safety of decisions. In Energy management, many optimization problems have some underlying uncertainty. In particular this is the case of unit commitment problems. In this Thesis, we will investigate probabilistic constraints from a theoretical, algorithmic and applicative point of view. We provide new insights on differentiability of probabilistic constraints and on convexity results of feasible sets. New variants of bundle methods, both of proximal and level type, specially tailored for convex optimization under probabilistic constraints, are given and convergence shown. Both methods explicitly deal with evaluation errors in both the gradient and value of the probabilistic constraint. We also look at two applications from energy management: cascaded reservoir management with uncertainty on inflows and unit commitment with uncertainty on customer load. In both applications uncertainty is dealt with through the use of probabilistic constraints. The presented numerical results seem to indicate the feasibility of solving an optimization problem with a joint probabilistic constraint on a system having up to 200 constraints. This is roughly the order of magnitude needed in the applications. The differentiability results involve probabilistic constraints on uncertain linear and nonlinear inequality systems. In the latter case a convexity structure in the underlying uncertainty vector is required. The uncertainty vector is assumed to have a multivariate Gaussian or Student law. The provided gradient formulae allow for efficient numerical sampling schemes. For probabilistic constraints that can be rewritten through the use of Copulae, we provide new insights on convexity of the feasible set. These results require a generalized concavity structure of the Copulae, the marginal distribution functions of the underlying random vector and of the underlying inequality system. These generalized concavity properties may hold only on specific sets.
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Van, Ackooij Wim. "Chance Constrained Programming : with applications in Energy Management." Thesis, Châtenay-Malabry, Ecole centrale de Paris, 2013. http://www.theses.fr/2013ECAP0071/document.

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Les contraintes en probabilité constituent un modèle pertinent pour gérer les incertitudes dans les problèmes de décision. En management d’énergie de nombreux problèmes d’optimisation ont des incertitudes sous-jacentes. En particulier c’est le cas des problèmes de gestion de la production au court-terme. Dans cette Thèse, nous investiguons les contraintes probabilistes sous l’angle théorique, algorithmique et applicative. Nous donnons quelques nouveaux résultats de différentiabilité des contraintes en probabilité et de convexité des ensembles admissibles. Des nouvelles variantes des méthodes de faisceaux « proximales » et « de niveaux » sont spécialement mises au point pour traiter des problèmes d’optimisation convexe sous contrainte en probabilité. Ces algorithmes gèrent en particulier, les erreurs d’évaluation de la contrainte en probabilité, ainsi que son gradient. La convergence vers une solution du problème est montrée. Enfin, nous examinons deux applications : l’optimisation d’une vallée hydraulique sous incertitude sur les apports et l’optimisation d’un planning de production sous incertitude sur la demande. Dans les deux cas nous utilisons une contrainte en probabilité pour gérer les incertitudes. Les résultats numériques présentés semblent montrer la faisabilité de résoudre des problèmes d’optimisation avec une contrainte en probabilité jointe portant sur un système de environ 200 contraintes. Il s’agit de l’ordre de grandeur nécessaire pour les applications. Les nouveaux résultats de différentiabilité concernent à la fois des contraintes en probabilité portant sur des systèmes linéaires et non-linéaires. Dans le deuxième cas, la convexité dans l’argument représentant le vecteur incertain est requise. Ce vecteur est supposé suivre une loi Gaussienne ou Student multi-variée. Les formules de gradient permettent l’application directe d’un schéma d’évaluation numérique efficient. Pour les contraintes en probabilité qui peuvent se réécrire à l’aide d’une Copule, nous donnons de nouveau résultats de convexité pour l’ensemble admissibles. Ces résultats requirent la concavité généralisée de la Copule, les distributions marginales sous-jacents et du système d’incertitude. Il est suffisant que ces propriétés de concavité généralisée tiennent sur un ensemble spécifique<br>In optimization problems involving uncertainty, probabilistic constraints are an important tool for defining safety of decisions. In Energy management, many optimization problems have some underlying uncertainty. In particular this is the case of unit commitment problems. In this Thesis, we will investigate probabilistic constraints from a theoretical, algorithmic and applicative point of view. We provide new insights on differentiability of probabilistic constraints and on convexity results of feasible sets. New variants of bundle methods, both of proximal and level type, specially tailored for convex optimization under probabilistic constraints, are given and convergence shown. Both methods explicitly deal with evaluation errors in both the gradient and value of the probabilistic constraint. We also look at two applications from energy management: cascaded reservoir management with uncertainty on inflows and unit commitment with uncertainty on customer load. In both applications uncertainty is dealt with through the use of probabilistic constraints. The presented numerical results seem to indicate the feasibility of solving an optimization problem with a joint probabilistic constraint on a system having up to 200 constraints. This is roughly the order of magnitude needed in the applications. The differentiability results involve probabilistic constraints on uncertain linear and nonlinear inequality systems. In the latter case a convexity structure in the underlying uncertainty vector is required. The uncertainty vector is assumed to have a multivariate Gaussian or Student law. The provided gradient formulae allow for efficient numerical sampling schemes. For probabilistic constraints that can be rewritten through the use of Copulae, we provide new insights on convexity of the feasible set. These results require a generalized concavity structure of the Copulae, the marginal distribution functions of the underlying random vector and of the underlying inequality system. These generalized concavity properties may hold only on specific sets
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Conference papers on the topic "Chance-constrained unit commitment"

1

Geng, Xinbo, and Le Xie. "Chance-constrained Unit Commitment via the Scenario Approach." In 2019 North American Power Symposium (NAPS). IEEE, 2019. http://dx.doi.org/10.1109/naps46351.2019.9000192.

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Mengping, GAO, XIA Tian, ZHU Tao, and WANG Zhenyi. "Convex Relaxation of Chance Constrained Two-stage Unit Commitment." In 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2). IEEE, 2018. http://dx.doi.org/10.1109/ei2.2018.8582453.

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Zephyr, Luckny, Ge Guo, Zongjie Wang, and Jose Morillo. "Approximate Chance-Constrained Unit Commitment Under Wind Energy Penetration." In Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences, 2022. http://dx.doi.org/10.24251/hicss.2022.417.

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Peralta, J. J., J. Perez-Ruiz, and S. de la Torre. "Unit commitment with load uncertainty by joint chance-constrained programming." In 2013 IEEE Grenoble PowerTech. IEEE, 2013. http://dx.doi.org/10.1109/ptc.2013.6652433.

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Chen, Y., T. Sato, and T. Shiina. "A cutting-plane solution for chance-constrained unit commitment problems." In 2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI). IEEE, 2022. http://dx.doi.org/10.1109/iiaiaai55812.2022.00126.

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Bagheri, Ali, Chaoyue Zhao, and Yuanxiong Guo. "Data-driven chance-constrained stochastic unit commitment under wind power uncertainty." In 2017 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2017. http://dx.doi.org/10.1109/pesgm.2017.8273948.

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Dawei He, Zhenyu Tan, and R. G. Harley. "Chance constrained unit commitment with wind generation and superconducting magnetic energy storages." In 2012 IEEE Power & Energy Society General Meeting. New Energy Horizons - Opportunities and Challenges. IEEE, 2012. http://dx.doi.org/10.1109/pesgm.2012.6345719.

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Qianfan Wang, Yongpei Guan, and Jianhui Wang. "A chance-constrained two-stage stochastic program for unit commitment with uncertain wind power output." In 2012 IEEE Power & Energy Society General Meeting. New Energy Horizons - Opportunities and Challenges. IEEE, 2012. http://dx.doi.org/10.1109/pesgm.2012.6345252.

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Martinez, Gabriela, and Lindsay Anderson. "Toward a scalable chance-constrained formulation for unit commitment to manage high penetration of variable generation." In 2014 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2014. http://dx.doi.org/10.1109/allerton.2014.7028526.

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10

Sun, Yan, Dong Mo, Yan Chen, Qiuwen Li, and Wuneng Ling. "An Improved Chance-Constrained Method for Unit Commitment in Multi-regional Power Systems Considering Wind Power Uncertainties." In 2022 5th International Conference on Renewable Energy and Power Engineering (REPE). IEEE, 2022. http://dx.doi.org/10.1109/repe55559.2022.9949396.

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Journal articles
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