Relevant bibliographies by topics / Chance-constrained energy management

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Chance-constrained energy management'

Author: Grafiati
Published: 5 June 2025
Last updated: 8 July 2025

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Journal articles on the topic "Chance-constrained energy management"

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Huang, Yantai, Lei Wang, Weian Guo, Qi Kang, and Qidi Wu. "Chance Constrained Optimization in a Home Energy Management System." IEEE Transactions on Smart Grid 9, no. 1 (2018): 252–60. http://dx.doi.org/10.1109/tsg.2016.2550031.

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Shi, Zhichao, Hao Liang, Shengjun Huang, and Venkata Dinavahi. "Distributionally Robust Chance-Constrained Energy Management for Islanded Microgrids." IEEE Transactions on Smart Grid 10, no. 2 (2019): 2234–44. http://dx.doi.org/10.1109/tsg.2018.2792322.

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Ciftci, Okan, Mahdi Mehrtash, and Amin Kargarian. "Data-Driven Nonparametric Chance-Constrained Optimization for Microgrid Energy Management." IEEE Transactions on Industrial Informatics 16, no. 4 (2020): 2447–57. http://dx.doi.org/10.1109/tii.2019.2932078.

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Cao, Zehao, Zhengshuo Li, and Chang Yang. "Credible joint chance-constrained low-carbon energy Management for Multi-energy Microgrids." Applied Energy 377 (January 2025): 124390. http://dx.doi.org/10.1016/j.apenergy.2024.124390.

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Zhai, Junyi, Sheng Wang, Lei Guo, Yuning Jiang, Zhongjian Kang, and Colin N. Jones. "Data-driven distributionally robust joint chance-constrained energy management for multi-energy microgrid." Applied Energy 326 (November 2022): 119939. http://dx.doi.org/10.1016/j.apenergy.2022.119939.

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Daneshvar, Mohammadreza, Behnam Mohammadi-Ivatloo, Somayeh Asadi, et al. "Chance-constrained models for transactive energy management of interconnected microgrid clusters." Journal of Cleaner Production 271 (October 2020): 122177. http://dx.doi.org/10.1016/j.jclepro.2020.122177.

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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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Cao, R., G. H. Huang, J. P. Chen, Y. P. Li, and C. Y. He. "A chance-constrained urban agglomeration energy model for cooperative carbon emission management." Energy 223 (May 2021): 119885. http://dx.doi.org/10.1016/j.energy.2021.119885.

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Zhou, Yuqi, Wenbin Yu, Shanying Zhu, Bo Yang, and Jianping He. "Distributionally robust chance-constrained energy management of an integrated retailer in the multi-energy market." Applied Energy 286 (March 2021): 116516. http://dx.doi.org/10.1016/j.apenergy.2021.116516.

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Aghdam, Farid Hamzeh, Navid Taghizadegan Kalantari, and Behnam Mohammadi-Ivatloo. "A chance-constrained energy management in multi-microgrid systems considering degradation cost of energy storage elements." Journal of Energy Storage 29 (June 2020): 101416. http://dx.doi.org/10.1016/j.est.2020.101416.

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Dissertations / Theses on the topic "Chance-constrained energy management"

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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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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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Abstract:
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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Book chapters on the topic "Chance-constrained energy management"

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van, Wim, Riadh Zorgati, Ren Henrion, and Andris Mller. "Chance Constrained Programming and Its Applications to Energy Management." In Stochastic Optimization - Seeing the Optimal for the Uncertain. InTech, 2011. http://dx.doi.org/10.5772/15438.

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Conference papers on the topic "Chance-constrained energy management"

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Haggi, Hamed, and James M. Fenton. "Distributionally Robust Chance-Constrained Energy Management of Multi-Building Residential Apartment Complexes Using Wasserstein Metric." In 2025 IEEE Texas Power and Energy Conference (TPEC). IEEE, 2025. https://doi.org/10.1109/tpec63981.2025.10906851.

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Hojjat, Mehrdad, and Abolfazl Ghasemi. "A Chance-Constrained Programming (CCP) Approach to Solve the Energy Management Problem in Microgrids Considering Uncertainties of Renewable Energy Resources." In 2024 International Conference on Electrical, Computer and Energy Technologies (ICECET). IEEE, 2024. http://dx.doi.org/10.1109/icecet61485.2024.10698265.

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Shi, Zhichao, Hao Liang, Shengjun Huang, and Venkata Dinavahi. "Distributionally Robust Chance-Constrained Energy Management for Islanded Microgrids." In 2019 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2019. http://dx.doi.org/10.1109/pesgm40551.2019.8973753.

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Cui, Hongbo, and Wei Xia. "Chance-constrained Energy Management Strategy for Micro-grids Intra-day Operation." In 2023 IEEE 3rd International Conference on Power, Electronics and Computer Applications (ICPECA). IEEE, 2023. http://dx.doi.org/10.1109/icpeca56706.2023.10075930.

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Ciftci, Okan, Mahdi Mehrtash, Farnaz Safdarian, and Amin Kargarian. "Chance-Constrained Microgrid Energy Management with Flexibility Constraints Provided by Battery Storage." In 2019 IEEE Texas Power and Energy conference (TPEC). IEEE, 2019. http://dx.doi.org/10.1109/tpec.2019.8662200.

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Chen, Yue, and Yashen Lin. "Hierarchical Management of Distributed Energy Resources Using Chance-Constrained OPF and Extremum Seeking Control." In 2019 American Control Conference (ACC). IEEE, 2019. http://dx.doi.org/10.23919/acc.2019.8815192.

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Li, Yang, Feng Wu, Jingyan Li, Yiwu Yin, Zhiyi Li, and Lan Ai. "Chance-constrained energy management for pumped storage hydropower plant to compensate for wind power uncertainties." In 2021 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2021. http://dx.doi.org/10.1109/pesgm46819.2021.9637867.

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You might also be interested in the extended bibliographies on the topic 'Chance-constrained energy management' for particular source types:
Journal articles
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