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

Author: Grafiati

Published: 5 June 2025

Last updated: 24 June 2025

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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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Shi, Zhichao, Hao Liang, and Venkata Dinavahi. "Data-Driven Distributionally Robust Chance-Constrained Unit Commitment With Uncertain Wind Power." IEEE Access 7 (2019): 135087–98. http://dx.doi.org/10.1109/access.2019.2942178.

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12

Sha, Qiangyi, Weiqing Wang, and Haiyun Wang. "A Distributionally Robust Chance-Constrained Unit Commitment with N-1 Security and Renewable Generation." Energies 14, no. 18 (2021): 5618. http://dx.doi.org/10.3390/en14185618.

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With the increasing penetration of renewable energy generation, one of the major challenges is the problem of how to express the stochastic process of wind power and photovoltaic output as the exact probability density and distribution, in order to improve the security and accuracy of unit commitment results, a distributed robust security-constrained optimization model based on moment uncertainty is proposed, in which the uncertainty of wind and photovoltaic power is captured by two uncertain sets of first- and second-order moments, respectively. The two sets contain the probability distribution of the forecast error of the wind and photovoltaic power, and in the model, the energy storage is considered. In order to solve the model effectively, firstly, based on the traditional chance-constrained second-order cone transformation, according to the first- and second-order moments polyhedron expression of the distribution set, a cutting plane method is proposed to solve the distributed robust chance constraints. Secondly, the modified IEEE-RTS 24 bus system is selected to establish a simulation example, an improved generalized Benders decomposition algorithm is developed to solve the model to optimality. The results show that the unit commitment results with different emphasis on economy and security can be obtained by setting different conservative coefficients and confidence levels and, then, provide a reasonable decision-making basis for dispatching operation.

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13

霍, 东升. "A Two-Stage Chance-Constrained Stochastic Program for Unit Commitment with Wind Power Output." Advances in Applied Mathematics 07, no. 02 (2018): 179–88. http://dx.doi.org/10.12677/aam.2018.72022.

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14

Alqunun, K. "Optimal Unit Commitment Problem Considering Stochastic Wind Energy Penetration." Engineering, Technology & Applied Science Research 10, no. 5 (2020): 6316–22. http://dx.doi.org/10.48084/etasr.3795.

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Wind energy has attracted much attention as a clean energy resource with low running cost over the last decade,. However, due to the unpredictable nature of wind speed, the Unit Commitment (UC) problem including wind power becomes more difficult. Therefore, engineers and researchers are required to seek reliable models and techniques to plan the operation of thermal units in presence of wind farms. This paper presents a new attempt to solve the stochastic UC including wind energy sources. In order to achieve this, the problem is modeled as a chance-constrained optimization problem. Then, a method based on the here-and-now strategy is used to convert the uncertain power balance constraint into a deterministic constraint. The obtained deterministic problem is modeled using Mixed Integer Programming (MIP) on GAMS interface whereas the CEPLEX MIP solver is employed for its solution.

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15

Alqunun, K. "Optimal Unit Commitment Problem Considering Stochastic Wind Energy Penetration." Engineering, Technology & Applied Science Research 10, no. 5 (2020): 6316–22. https://doi.org/10.48084/etasr.3795.

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Wind energy has attracted much attention as a clean energy resource with low running cost over the last decade,. However, due to the unpredictable nature of wind speed, the Unit Commitment (UC) problem including wind power becomes more difficult. Therefore, engineers and researchers are required to seek reliable models and techniques to plan the operation of thermal units in presence of wind farms. This paper presents a new attempt to solve the stochastic UC including wind energy sources. In order to achieve this, the problem is modeled as a chance-constrained optimization problem. Then, a method based on the here-and-now strategy is used to convert the uncertain power balance constraint into a deterministic constraint. The obtained deterministic problem is modeled using Mixed Integer Programming (MIP) on GAMS interface whereas the CEPLEX MIP solver is employed for its solution.

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16

Wang, Qianfan, Yongpei Guan, and Jianhui Wang. "A Chance-Constrained Two-Stage Stochastic Program for Unit Commitment With Uncertain Wind Power Output." IEEE Transactions on Power Systems 27, no. 1 (2012): 206–15. http://dx.doi.org/10.1109/tpwrs.2011.2159522.

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17

Pozo, David, and Javier Contreras. "A Chance-Constrained Unit Commitment With an $n-K$ Security Criterion and Significant Wind Generation." IEEE Transactions on Power Systems 28, no. 3 (2013): 2842–51. http://dx.doi.org/10.1109/tpwrs.2012.2227841.

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18

van Ackooij, Wim. "Decomposition approaches for block-structured chance-constrained programs with application to hydro-thermal unit commitment." Mathematical Methods of Operations Research 80, no. 3 (2014): 227–53. http://dx.doi.org/10.1007/s00186-014-0478-5.

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19

Wang, Yang, Shuqiang Zhao, Zhi Zhou, Audun Botterud, Yan Xu, and Runze Chen. "Risk Adjustable Day-Ahead Unit Commitment With Wind Power Based on Chance Constrained Goal Programming." IEEE Transactions on Sustainable Energy 8, no. 2 (2017): 530–41. http://dx.doi.org/10.1109/tste.2016.2608841.

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20

Zheng, Haiyan, Liying Huang, and Ran Quan. "Mixed-Integer Conic Formulation of Unit Commitment with Stochastic Wind Power." Mathematics 11, no. 2 (2023): 346. http://dx.doi.org/10.3390/math11020346.

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Due to the high randomness and volatility of renewable energy sources such as wind energy, the traditional thermal unit commitment (UC) model is no longer applicable. In this paper, in order to reduce the possible negative effects of an inaccurate wind energy forecast, the chance-constrained programming (CCP) method is used to study the UC problem with uncertainty wind power generation, and chance constraints such as power balance and spinning reserve are satisfied with a predetermined probability. In order to effectively solve the CCP problem, first, we used the sample average approximation (SAA) method to transform the chance constraints into deterministic constraints and to obtain a mixed-integer quadratic programming (MIQP) model. Then, the quadratic terms were incorporated into the constraints by introducing some auxiliary variables, and some second-order cone constraints were formed by combining them with the output characteristics of thermal unit; therefore, a tighter mixed-integer second-order cone programming (MISOCP) formulation was obtained. Finally, we applied this method to some systems including 10 to 100 thermal units and 1 to 2 wind units, and we invoked MOSEK in MATLAB to solve the MISOCP formulation. The numerical results obtained within 24 h confirm that not only is the MISOCP formulation a successful reformulation that can achieve better suboptimal solutions, but it is also a suitable method for solving the large-scale uncertain UC problem. In addition, for systems of up to 40 units within 24 h that do not consider wind power and pollution emissions, the numerical results were compared with those of previously published methods, showing that the MISOCP formulation is very promising, given its excellent performance.

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21

Gao, Shan, Yiqing Zhang, and Yu Liu. "Incorporating Concentrating Solar Power into High Renewables Penetrated Power System: A Chance-Constrained Stochastic Unit Commitment Analysis." Applied Sciences 9, no. 11 (2019): 2340. http://dx.doi.org/10.3390/app9112340.

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High renewables penetrated power systems would be greatly influenced by the uncertainty of variable renewable energy such as wind power and photovoltaic power. Unlike wind and photovoltaic plant, concentrating solar power with thermal energy storage has similar dispatchable characteristics as conventional thermal unit. Besides, thermal energy storage could support the coordinated operation of concentrating solar power with an electrical heater, which can be employed to convert surplus electricity in the grid into thermal power stored in thermal energy storage for further utilization. In this paper, concentrating solar power is incorporated into a chance-constrained two-stage stochastic unit commitment model. The model considers the energy and reserve services of concentrating solar power and the uncertainty of renewables. The proposed method is employed to assess the role of a concentrating solar power station with thermal energy storage and an electrical heater to provide grid flexibility in high renewables penetrated power systems. Numerical studies are performed on a modified IEEE 24-bus system to validate the viability of the proposed method for the day-ahead stochastic scheduling. The results demonstrate the economic and reliable value of concentrating solar power station to the improvement of unit commitment schedule, to the mitigation of wind uncertainty and photovoltaic uncertainty, and to the reduction of traditional unit reserve requirement. It is concluded that concentrating solar power with thermal energy storage and an electrical heater is effective in promoting the further penetration of renewables.

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22

Shao, Jian, Bu Han Zhang, Wei Si Deng, Kai Min Zhang, Bing Jie Jin, and Teng Yu Ge. "A Stochastic Programming Method for Unit Commitment of Wind Integrated Power System." Advanced Materials Research 732-733 (August 2013): 1390–95. http://dx.doi.org/10.4028/www.scientific.net/amr.732-733.1390.

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This paper presents a stochastic programming method that can assess the impact of wind generation uncertainties on unit comment (UC) problem. To model the uncertainty of wind genration, scenarios of wind speed are generated based on the known probability interval of forecasted wind speed and a scenario reduction technique limits the number of scenarios. The UC problem is modeled as a stochastic programming problem based on chance-constrained programming, and is decomposed into two embedded optimization sub-problems: the unit on/off status schedule problem and the load economic dispatch problem. Discrete particle swarm optimization (DPSO) and the equal incremental principle are used to solve the stochastic UC problem. The numerical results indicate that the proposed stochastic model is more suitable for wind integrated system with uncertainty.

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23

Hong, Ying-Yi, and Gerard Francesco DG Apolinario. "Uncertainty in Unit Commitment in Power Systems: A Review of Models, Methods, and Applications." Energies 14, no. 20 (2021): 6658. http://dx.doi.org/10.3390/en14206658.

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The unit commitment problem (UCP) is one of the key and fundamental concerns in the operation, monitoring, and control of power systems. Uncertainty management in a UCP has been of great interest to both operators and researchers. The uncertainties that are considered in a UCP can be classified as technical (outages, forecast errors, and plugin electric vehicle (PEV) penetration), economic (electricity prices), and “epidemics, pandemics, and disasters” (techno-socio-economic). Various methods have been developed to model the uncertainties of these parameters, such as stochastic programming, probabilistic methods, chance-constrained programming (CCP), robust optimization, risk-based optimization, the hierarchical scheduling strategy, and information gap decision theory. This paper reviews methods of uncertainty management, parameter modeling, simulation tools, and test systems.

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24

Chen, Zhe, Zhengshuo Li, Chuangxin Guo, Yi Ding, and Yubin He. "Two-stage chance-constrained unit commitment based on optimal wind power consumption point considering battery energy storage." IET Generation, Transmission & Distribution 14, no. 18 (2020): 3738–49. http://dx.doi.org/10.1049/iet-gtd.2019.1492.

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25

Wu, Zhi, Pingliang Zeng, Xiao-Ping Zhang, and Qinyong Zhou. "A Solution to the Chance-Constrained Two-Stage Stochastic Program for Unit Commitment With Wind Energy Integration." IEEE Transactions on Power Systems 31, no. 6 (2016): 4185–96. http://dx.doi.org/10.1109/tpwrs.2015.2513395.

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26

Zhang, Yao, Jianxue Wang, Bo Zeng, and Zechun Hu. "Chance-Constrained Two-Stage Unit Commitment Under Uncertain Load and Wind Power Output Using Bilinear Benders Decomposition." IEEE Transactions on Power Systems 32, no. 5 (2017): 3637–47. http://dx.doi.org/10.1109/tpwrs.2017.2655078.

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27

Zhang, Ning, Zhaoguang Hu, Xue Han, Jian Zhang, and Yuhui Zhou. "A fuzzy chance-constrained program for unit commitment problem considering demand response, electric vehicle and wind power." International Journal of Electrical Power & Energy Systems 65 (February 2015): 201–9. http://dx.doi.org/10.1016/j.ijepes.2014.10.005.

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28

Ding, Tao, Ziyu Zeng, Ming Qu, Joao P. S. Catalao, and Mohammad Shahidehpour. "Two-Stage Chance-Constrained Stochastic Thermal Unit Commitment for Optimal Provision of Virtual Inertia in Wind-Storage Systems." IEEE Transactions on Power Systems 36, no. 4 (2021): 3520–30. http://dx.doi.org/10.1109/tpwrs.2021.3051523.

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29

Chen, Rui, Deping Ke, Yuanzhang Sun, et al. "Hierarchical Frequency-dependent Chance Constrained Unit Commitment for Bulk AC/DC Hybrid Power Systems with Wind Power Generation." Journal of Modern Power Systems and Clean Energy 11, no. 4 (2023): 1053–64. http://dx.doi.org/10.35833/mpce.2022.000138.

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30

Riaz, Muhammad, Sadiq Ahmad, Irshad Hussain, Muhammad Naeem, and Lucian Mihet-Popa. "Probabilistic Optimization Techniques in Smart Power System." Energies 15, no. 3 (2022): 825. http://dx.doi.org/10.3390/en15030825.

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Uncertainties are the most significant challenges in the smart power system, necessitating the use of precise techniques to deal with them properly. Such problems could be effectively solved using a probabilistic optimization strategy. It is further divided into stochastic, robust, distributionally robust, and chance-constrained optimizations. The topics of probabilistic optimization in smart power systems are covered in this review paper. In order to account for uncertainty in optimization processes, stochastic optimization is essential. Robust optimization is the most advanced approach to optimize a system under uncertainty, in which a deterministic, set-based uncertainty model is used instead of a stochastic one. The computational complexity of stochastic programming and the conservativeness of robust optimization are both reduced by distributionally robust optimization.Chance constrained algorithms help in solving the constraints optimization problems, where finite probability get violated. This review paper discusses microgrid and home energy management, demand-side management, unit commitment, microgrid integration, and economic dispatch as examples of applications of these techniques in smart power systems. Probabilistic mathematical models of different scenarios, for which deterministic approaches have been used in the literature, are also presented. Future research directions in a variety of smart power system domains are also presented.

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31

Jain, Smriti, Ramesh Kumar Pachar, and Lata Gidwani. "Chance Constrained Day Ahead Stochastic Unit Commitment with Multiple Uncertainties." Journal of Electrical Engineering & Technology, August 2, 2024. http://dx.doi.org/10.1007/s42835-024-01990-w.

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32

Shi, Zhichao, Hao Liang, and Venkata Dinavahi. "Data-Driven Distributionally Robust Chance-Constrained Unit Commitment With Uncertain Wind Power." September 18, 2019. https://doi.org/10.5281/zenodo.7683252.

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The Unit commitment (UC) problem in power systems has been studied for a long time; however, many new challenges have emerged in the UC problem with the increasing penetration of renewable generation which is intermittent and uncertain. Compared with the common uncertainty modeling methods including stochastic programming and robust optimization, in this paper, we develop a data-driven distributionally robust chance-constrained (DDRC) UC model. The proposed two-stage UC model focuses on the commitment decision and dispatch plan in the rst stage, and considers the worst-case expected cost for possible power imbalance or re-dispatch in the second stage. To capture the uncertainty of wind power distribution, a distance-based ambiguity set is designed which can be constructed in a data-driven manner. Based on the ambiguity set, the original complicated UC problem is reformulated to a tractable optimization problem which is then solved by the column-and-constraint generation (C&CG) algorithm. The performance of the the proposed approach is validated by case studies with different test systems including the IEEE 6-bus test system, modified IEEE 118-bus system and a practical-scale system, especially the value of data in controlling the conservativeness of the problem.

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33

Liang, Jinhao, Wenqian Jiang, Chenbei Lu, and Chenye Wu. "Joint Chance-constrained Unit Commitment: Statistically Feasible Robust Optimization with Learning-to-Optimize Acceleration." IEEE Transactions on Power Systems, 2024, 1–13. http://dx.doi.org/10.1109/tpwrs.2024.3351435.

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34

Li, Jinghua, Licheng Lin, Yifu Xu, Shuang Zhou, Dunlin Zhu, and Junjie Liang. "Probability Efficient Point Method to Solve Joint Chance-Constrained Unit Commitment for Multi-Area Power Systems with Renewable Energy." IEEE Transactions on Power Systems, 2022, 1. http://dx.doi.org/10.1109/tpwrs.2022.3180111.

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35

Qian, Liang, Shunfu Lin, Bo Zhou, et al. "Stochastic unit commitment based on energy‐intensive loads participating in wind and solar power consumption." IET Renewable Power Generation, January 15, 2024. http://dx.doi.org/10.1049/rpg2.12915.

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AbstractThe fluctuation and intermittency of wind and solar power outputs result in increased regulation pressure on thermal units in power systems. Adjustable energy‐intensive loads (such as electrolytic aluminium and steel plants) have great potential for participating in demand response (DR) programs with the goal of reducing thermal unit regulation pressure. This paper proposes an optimal scheduling method of unit commitment (UC) which gives consideration to energy‐intensive loads participating in wind and solar power consumption. The UC method adopts the nonparametric kernel density estimation method to model wind and solar power outputs and then uses the Frank‐Copula function to describe the correlation between the scenarios of wind and solar power outputs. A stochastic unit commitment (SUC) model introduces a chance‐constrained theory of a reserve coefficient to describe time‐variant scenarios on the basis of the deviation between the typical and simulative scenarios. The simulation results based on the IEEE 118‐bus system show that the energy‐intensive load in the SUC model can flexibly adjust and respond to changes in wind and solar power output, reduce the impact of the uncertainties of wind and solar power output, and promote the consumption of wind and solar power.

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36

Gao, Lan-Da, Zhen-Hua Li, Meng-Yi Wu, et al. "Interval reservoir computing: theory and case studies." Frontiers in Energy Research 11 (February 20, 2024). http://dx.doi.org/10.3389/fenrg.2023.1239973.

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The time series data in many applications, for example, wind power and vehicle trajectory, show significant uncertainty. Using a single prediction value of wind power as feedback information for wind turbine control or unit commitment is not enough since the uncertainty of the prediction is not described. This paper addresses the uncertainty issue in time series data forecasting by proposing the novel interval reservoir computing method. The proposed interval reservoir computing can capture the underlying evolution of the stochastic dynamical system for time series data using the recurrent neural network (RNN). On the other hand, by formulating a chance-constrained optimization problem, interval reservoir computing outputs a set of parameters in the RNN, which maps to an interval of prediction values. The capacity of the interval is the smallest one satisfying the condition that the probability of having a prediction inside the interval is lower than the required level. The scenario approach solves the formulated chance-constrained optimization problem. We implemented an experimental data-based validation to evaluate the proposed method. The validation results show that the proposed interval reservoir computing can give a tight interval of time series data forecasting values for wind power and traffic trajectory. In addition, the confidence probability over the feasibility goes to 1 very quickly as the sample number increases.

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