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

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Soares, Tiago, and Ricardo J. Bessa. "Proactive management of distribution grids with chance-constrained linearized AC OPF." International Journal of Electrical Power & Energy Systems 109 (July 2019): 332–42. http://dx.doi.org/10.1016/j.ijepes.2019.02.002.

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12

Zhou, Changyu, Guohe Huang, and Jiapei Chen. "A Type-2 Fuzzy Chance-Constrained Fractional Integrated Modeling Method for Energy System Management of Uncertainties and Risks." Energies 12, no. 13 (2019): 2472. http://dx.doi.org/10.3390/en12132472.

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In this study, a type-2 fuzzy chance-constrained fractional integrated programming (T2FCFP) approach is developed for the planning of sustainable management in an electric power system (EPS) under complex uncertainties. Through simultaneously coupling mixed-integer linear programming (MILP), chance-constrained stochastic programming (CCSP), and type-2 fuzzy mathematical programming (T2FMP) techniques into a fractional programming (FP) framework, T2FCFP can tackle dual objective problems of uncertain parameters with both type-2 fuzzy characteristics and stochastic effectively and enhance the robustness of the obtained decisions. T2FCFP has been applied to a case study of a typical electric power system planning to demonstrate these advantages, where issues of clean energy utilization, air-pollutant emissions mitigation, mix ratio of renewable energy power generation in the entire energy supply, and the displacement efficiency of electricity generation technologies by renewable energy are incorporated within the modeling formulation. The suggested optimal alternative that can produce the desirable sustainable schemes with a maximized share of clean energy power generation has been generated. The results obtained can be used to conduct desired energy/electricity allocation and help decision-makers make suitable decisions under different input scenarios.

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13

Kong, Xiangyu, Siqiong Zhang, Bowei Sun, Qun Yang, Shupeng Li, and Shijian Zhu. "Research on Home Energy Management Method for Demand Response Based on Chance-Constrained Programming." Energies 13, no. 11 (2020): 2790. http://dx.doi.org/10.3390/en13112790.

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With the development of smart devices and information technology, it is possible for users to optimize their usage of electrical equipment through the home energy management system (HEMS). To solve the problems of daily optimal scheduling and emergency demand response (DR) in an uncertain environment, this paper provides an opportunity constraint programming model for the random variables contained in the constraint conditions. Considering the probability distribution of the random variables, a home energy management method for DR based on chance-constrained programming is proposed. Different confidence levels are set to reflect the influence mechanism of random variables on constraint conditions. An improved particle swarm optimization algorithm is used to solve the problem. Finally, the demand response characteristics in daily and emergency situations are analyzed by simulation examples, and the effectiveness of the method is verified.

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14

Nagpal, Himanshu, Iason-Iraklis Avramidis, Florin Capitanescu, and Per Heiselberg. "Optimal energy management in smart sustainable buildings – A chance-constrained model predictive control approach." Energy and Buildings 248 (October 2021): 111163. http://dx.doi.org/10.1016/j.enbuild.2021.111163.

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15

Su, Su, Zening Li, Xiaolong Jin, Koji Yamashita, Mingchao Xia, and Qifang Chen. "Energy management for active distribution network incorporating office buildings based on chance-constrained programming." International Journal of Electrical Power & Energy Systems 134 (January 2022): 107360. http://dx.doi.org/10.1016/j.ijepes.2021.107360.

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16

Liu, Jianzhe, Hua Chen, Wei Zhang, Benjamin Yurkovich, and Giorgio Rizzoni. "Energy Management Problems Under Uncertainties for Grid-Connected Microgrids: A Chance Constrained Programming Approach." IEEE Transactions on Smart Grid 8, no. 6 (2017): 2585–96. http://dx.doi.org/10.1109/tsg.2016.2531004.

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17

Vergara-Dietrich, José D., Marcelo M. Morato, Paulo R. C. Mendes, Alex A. Cani, Julio E. Normey-Rico, and Carlos Bordons. "Advanced chance-constrained predictive control for the efficient energy management of renewable power systems." Journal of Process Control 74 (February 2019): 120–32. http://dx.doi.org/10.1016/j.jprocont.2017.11.003.

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18

Hemmati, Mohammad, Navid Bayati, and Thomas Ebel. "Integrated Optimal Energy Management of Multi-Microgrid Network Considering Energy Performance Index: Global Chance-Constrained Programming Framework." Energies 17, no. 17 (2024): 4367. http://dx.doi.org/10.3390/en17174367.

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Distributed generation (DG) sources play a special role in the operation of active energy networks. The microgrid (MG) is known as a suitable substrate for the development and installation of DGs. However, the future of energy distribution networks will consist of more interconnected and complex MGs, called multi-microgrid (MMG) networks. Therefore, energy management in such an energy system is a major challenge for distribution network operators. This paper presents a new energy management method for the MMG network in the presence of battery storage, renewable sources, and demand response (DR) programs. To show the performance of each connected MG’s inefficient utilization of its available generation capacity, an index called unused power capacity (UPC) is defined, which indicates the availability and individual performance of each MG. The uncertainties associated with load and the power output of wind and solar sources are handled by employing the chance-constrained programming (CCP) optimization framework in the MMG energy management model. The proposed CCP ensures the safe operation of the system at the desired confidence level by involving various uncertainties in the problem while optimizing operating costs under Mixed-Integer Linear Programming (MILP). The proposed energy management model is assessed on a sample network concerning DC power flow limitations. The procured power of each MG and power exchanges at the distribution network level are investigated and discussed.

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19

Hojjat, Mehrdad, and M. Hossein Javidi D. B. "Probabilistic Congestion Management Considering Power System Uncertainties Using Chance-constrained Programming." Electric Power Components and Systems 41, no. 10 (2013): 972–89. http://dx.doi.org/10.1080/15325008.2013.801054.

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20

Zhou, C. Y., G. H. Huang, J. P. Chen, and X. Y. Zhang. "Inexact Fuzzy Chance-Constrained Fractional Programming for Sustainable Management of Electric Power Systems." Mathematical Problems in Engineering 2018 (November 19, 2018): 1–13. http://dx.doi.org/10.1155/2018/5794016.

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An inexact fuzzy chance-constrained fractional programming model is developed and applied to the planning of electric power systems management under uncertainty. An electric power system management system involves several processes with socioeconomic and environmental influenced. Due to the multiobjective, multilayer and multiperiod features, associated with these various factors and their interactions extensive uncertainties, may exist in the study system. As an extension of the existing fractional programming approach, the inexact fuzzy chance-constrained fractional programming can explicitly address system uncertainties with complex presentations. The approach can not only deal with multiple uncertainties presented as random variables, fuzzy sets, interval values, and their combinations but also reflect the tradeoff in conflicting objectives between greenhouse gas mitigation and system economic profit. Different from using least-cost models, a more sustainable management approach is to maximize the ratio between clean energy power generation and system cost. Results of the case study indicate that useful solutions for planning electric power systems management practices can be generated.

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21

Hojjat, Mehrdad, and Mohammad Hossein Javidi. "Chance-constrained programming approach to stochastic congestion management considering system uncertainties." IET Generation, Transmission & Distribution 9, no. 12 (2015): 1421–29. http://dx.doi.org/10.1049/iet-gtd.2014.0376.

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22

Cai, Yanpeng, Xuan Lin, Wencong Yue, and Pingping Zhang. "Inexact fuzzy chance-constrained programming for community-scale urban stormwater management." Journal of Cleaner Production 182 (May 2018): 937–45. http://dx.doi.org/10.1016/j.jclepro.2018.02.009.

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23

Shams, Mohammad H., Haider Niaz, and J. Jay Liu. "Energy management of hydrogen refueling stations in a distribution system: A bilevel chance-constrained approach." Journal of Power Sources 533 (June 2022): 231400. http://dx.doi.org/10.1016/j.jpowsour.2022.231400.

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24

Zhao, Pengfei, Han Wu, Chenghong Gu, and Ignacio Hernando‐Gil. "Optimal home energy management under hybrid photovoltaic‐storage uncertainty: a distributionally robust chance‐constrained approach." IET Renewable Power Generation 13, no. 11 (2019): 1911–19. http://dx.doi.org/10.1049/iet-rpg.2018.6169.

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25

Liu, Zhengping, Guohe Huang, and Wei Li. "An inexact stochastic–fuzzy jointed chance-constrained programming for regional energy system management under uncertainty." Engineering Optimization 47, no. 6 (2014): 788–804. http://dx.doi.org/10.1080/0305215x.2014.927451.

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26

Marino, Carlos Antonio, and Mohammad Marufuzzaman. "A microgrid energy management system based on chance-constrained stochastic optimization and big data analytics." Computers & Industrial Engineering 143 (May 2020): 106392. http://dx.doi.org/10.1016/j.cie.2020.106392.

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27

Xu, Xiao, Weihao Hu, Yuefang Du, et al. "Robust chance-constrained gas management for a standalone gas supply system based on wind energy." Energy 212 (December 2020): 118723. http://dx.doi.org/10.1016/j.energy.2020.118723.

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28

Lei, Xingyu, Zhifang Yang, Junbo Zhao, and Juan Yu. "Data-driven assisted chance-constrained energy and reserve scheduling with wind curtailment." Applied Energy 321 (September 2022): 119291. http://dx.doi.org/10.1016/j.apenergy.2022.119291.

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29

Su, Su, Zening Li, Xiaolong Jin, Koji Yamashita, Mingchao Xia, and Qifang Chen. "Bi-level energy management and pricing for community energy retailer incorporating smart buildings based on chance-constrained programming." International Journal of Electrical Power & Energy Systems 138 (June 2022): 107894. http://dx.doi.org/10.1016/j.ijepes.2021.107894.

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30

Häussling Löwgren, Bartolomeus, Joris Weigert, Erik Esche, and Jens-Uwe Repke. "Uncertainty Analysis for Data-Driven Chance-Constrained Optimization." Sustainability 12, no. 6 (2020): 2450. http://dx.doi.org/10.3390/su12062450.

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In this contribution our developed framework for data-driven chance-constrained optimization is extended with an uncertainty analysis module. The module quantifies uncertainty in output variables of rigorous simulations. It chooses the most accurate parametric continuous probability distribution model, minimizing deviation between model and data. A constraint is added to favour less complex models with a minimal required quality regarding the fit. The bases of the module are over 100 probability distribution models provided in the Scipy package in Python, a rigorous case-study is conducted selecting the four most relevant models for the application at hand. The applicability and precision of the uncertainty analyser module is investigated for an impact factor calculation in life cycle impact assessment to quantify the uncertainty in the results. Furthermore, the extended framework is verified with data from a first principle process model of a chloralkali plant, demonstrating the increased precision of the uncertainty description of the output variables, resulting in 25% increase in accuracy in the chance-constraint calculation.

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31

Zhou, Changyu, Guohe Huang, and Jiapei Chen. "A Multi-Objective Energy and Environmental Systems Planning Model: Management of Uncertainties and Risks for Shanxi Province, China." Energies 11, no. 10 (2018): 2723. http://dx.doi.org/10.3390/en11102723.

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In this study, a fuzzy chance-constrained fractional programming (FCFP) approach is developed to help tackle various uncertainties involved in electric power systems (EPSs) management. The FCFP approach is capable of solving ratio optimization decision problems in power systems associated with random and fuzzy information by chance-constrained programming (CCP) method, fuzzy measure programming, fractional programming (FP) into a general framework. It can tackle inexact information expressed as fuzzy set and probability distributions, comprehensively reflect the decision maker’s pessimistic and optimistic preferences, and balance dual objectives of system economy and sustainability. To demonstrate its applicability, FCFP approach is then applied to a case study of Shanxi Province, a typical coal-heavy electricity region in China. The results indicate that the FCFP approach reveals uncertain interactions among the decision maker’s preferences and various random variables. Reasonable solutions have been generated for Shanxi EPS management practices, which can provide strategies in mitigating pollutant emissions, reducing system costs, and promoting coalbed methane as an alternative energy source for coal-fired and plays an essential role in Shanxi’s municipal planning. The solutions will help decision makers generate alternatives in the event of the reducing coal-fired power generation and could be applicable in other coal-heavy electricity regions.

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32

Wang, Bo, Payman Dehghanian, and Dongbo Zhao. "Chance-Constrained Energy Management System for Power Grids With High Proliferation of Renewables and Electric Vehicles." IEEE Transactions on Smart Grid 11, no. 3 (2020): 2324–36. http://dx.doi.org/10.1109/tsg.2019.2951797.

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33

Shen, Yi, Junyi Zhai, Zhongjian Kang, Bei Zhao, Xianhui Gao, and Zhengmao Li. "Distributionally robust chance-constrained energy management for island DC microgrid with offshore wind power hydrogen production." Energy 316 (February 2025): 134570. https://doi.org/10.1016/j.energy.2025.134570.

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34

Li, Dedi, Yue Zong, Xinjie Lai, Huimin Huang, Haiqi Zhao, and Shufeng Dong. "Chance-Constrained Dispatching of Integrated Energy Systems Considering Source–Load Uncertainty and Photovoltaic Absorption." Sustainability 15, no. 16 (2023): 12459. http://dx.doi.org/10.3390/su151612459.

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Because of their renewable and non-polluting characteristics in power production, distributed photovoltaics have been developed, but they have also been criticized for the volatility of their output power. In this paper, an integrated energy system optimal dispatching model is proposed to improve the local absorption capacity of distributed photovoltaics. First, an integrated energy system consisting of electricity, heat, cooling, gas, and hydrogen is modeled, and a mathematical model of the system is constructed. After that, the uncertainty of distributed photovoltaic power and load demand is modeled, and a typical scenario data set is generated through Monte Carlo simulation and K-means clustering. Finally, an optimal dispatching model of the integrated energy system is constructed to minimize the daily operating cost, including energy consumption, equipment operation and maintenance, and curtailment penalty costs, as the optimization objective. In the objective, a segmented curtailment penalty cost is Introduced. Moreover, this paper presents a chance constraint to convert the optimization problem containing uncertain variables into a mixed integer linear programming problem, which can reduce the difficulty of the solution. The case shows that the proposed optimal dispatching model can improve the ability of photovoltaics to be accommodated locally. At the same time, due to the introduction of the segmented curtailment penalty cost, the system improves the absorption of distributed photovoltaic generation at peak tariff intervals and enhances the economy of system operation.

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35

Ding, Xiaowen, Dongxu Hua, Guihong Jiang, Zhengfeng Bao, and Lei Yu. "Two-stage interval stochastic chance-constrained robust programming and its application in flood management." Journal of Cleaner Production 167 (November 2017): 908–18. http://dx.doi.org/10.1016/j.jclepro.2017.07.205.

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36

Wang, Xin, Najmeh Bazmohammadi, Jason Atkin, Serhiy Bozhko, and Josep M. Guerrero. "Chance-constrained model predictive control-based operation management of more-electric aircraft using energy storage systems under uncertainty." Journal of Energy Storage 55 (November 2022): 105629. http://dx.doi.org/10.1016/j.est.2022.105629.

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37

Li, Gongchen, Wei Sun, Ying Lv, Guanhui Cheng, Yumin Chen, and Guo H. Huang. "Interval joint-probabilistic chance-constrained programming with two-side multi-randomness: an application to energy-environment systems management." Stochastic Environmental Research and Risk Assessment 32, no. 7 (2017): 2093–110. http://dx.doi.org/10.1007/s00477-017-1502-0.

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38

Liu, Hui, Zhenggang Fan, Haimin Xie, and Ni Wang. "Distributionally Robust Joint Chance-Constrained Dispatch for Electricity–Gas–Heat Integrated Energy System Considering Wind Uncertainty." Energies 15, no. 5 (2022): 1796. http://dx.doi.org/10.3390/en15051796.

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With the increasing penetration of wind power, the uncertainty associated with it brings more challenges to the operation of the integrated energy system (IES), especially the power subsystem. However, the typical strategies to deal with wind power uncertainty have poor performance in balancing economy and robustness. Therefore, this paper proposes a distributionally robust joint chance-constrained dispatch (DR-JCCD) model to coordinate the economy and robustness of the IES with uncertain wind power. The optimization dispatch model is formulated as a two-stage problem to minimize both the day-ahead and the real-time operation costs. Moreover, the ambiguity set is generated using Wasserstein distance, and the joint chance constraints are used to ensure that the safety constraints (e.g., ramping limit and transmission limit) can be satisfied jointly under the worst-case probability distribution of wind power. The model is remodeled as a mixed-integer tractable programming issue, which can be solved efficiently by ready-made solvers using linear decision rules and linearization methods. Case studies on an electricity–gas–heat regional integrated system, which includes a modified IEEE 24-bus system, 20 natural gas-nodes, and 6 heat-node system, are investigated for verification. Numerical simulation results demonstrate that the proposed DR-JCCD approach effectively coordinates the economy and robustness of IES and can offer operators a reasonable energy management scheme with an acceptable risk level.

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39

Huang, Xian, Wentong Ji, Xiaorong Ye, and Zhangjie Feng. "Configuration Planning of Expressway Self-Consistent Energy System Based on Multi-Objective Chance-Constrained Programming." Sustainability 15, no. 6 (2023): 5605. http://dx.doi.org/10.3390/su15065605.

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Regarding the problem of the optimal configuration of self-consistent energy systems based on a 100% renewable energy supply for expressway electricity demand in no-grid areas, this paper proposes a multi-objective planning model based on chance-constrained programming (CCP) to achieve the optimization objectives of low cost and high reliability. Firstly, the number of units of different types of wind turbines (WT), the capacity of photovoltaic (PV) cells, and the number of sets of energy storage systems (ESS) are selected for the design variables in our configuration plan. After defining the load grading shedding and ESS scheduling strategy, the Monte Carlo Simulation (MCS) method and the backward reduction method are applied to model the uncertainties of electric load and renewable energy sources. Finally, the set of Pareto solutions are optimized by the non-dominated sorted genetic algorithm-II (NSGA-II) and its unique best solution is determined by the Criteria Importance Though Intercriteria Correlation (CRITIC) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach. Making use of the wind speed and solar radiation intensity historical data of an area in northwest China in the last five years, eight case studies of two typical scenarios are designed and carried out to explore in-depth the impact of different confidence levels and load fluctuation ranges on the planning results. The results verify that the proposed method can effectively improve the robustness of the system and satisfy the power demand in confidence scenarios.

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40

Xu, Ye, Guohe Huang, and Liguo Shao. "A stochastic fuzzy chance-constrained programming model for energy–environment system planning and management in the City of Beijing." International Journal of Green Energy 14, no. 2 (2016): 171–83. http://dx.doi.org/10.1080/15435075.2016.1253572.

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41

Xun, Qian, Nikolce Murgovski, and Yujing Liu. "Chance-constrained robust co-design optimization for fuel cell hybrid electric trucks." Applied Energy 320 (August 2022): 119252. http://dx.doi.org/10.1016/j.apenergy.2022.119252.

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42

Bao, Zhe, Ye Xu, Wei Li, et al. "A Birandom Chance-Constrained Linear Programming Model for CCHP System Operation Management: A Case Study of Hotel in Shanghai, China." Mathematical Problems in Engineering 2020 (November 19, 2020): 1–16. http://dx.doi.org/10.1155/2020/1589415.

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Due to its capability to reduce fuel consumption and increase energy efficiency, the combined cooling, heating, and power (CCHP) system has obtained great concern during the last decade. A large number of deterministic and stochastic optimization models were proposed for supporting the operation management of the CCHP system, but few studies noticed that users’ demands in the real world may be subjected to twofold randomness with incomplete or uncertain information. In this study, a birandom chance-constrained linear programming (BCCLP) model is developed for identifying optimal operation strategies under random uncertainties. Compared with traditional stochastic programming models, the BCCLP model made the improvement through describing the energy demands as the birandom variables firstly, instead of traditional random variables. This way effectively avoided potential imbalance between energy supply and demand caused by oversimplified expression of uncertain parameters. A gas-fired CCHP system of a hotel in Shanghai, China, was used as a study case for demonstration. A variety of operation strategies are obtained under specific constraints-satisfaction conditions. It is concluded that the BCCLP model was capable of generating the cost-effective operation strategies and evaluating the tradeoffs between system economy and reliability. The influence imposed by some critical parameters on the system performance was examined through the sensitivity analysis, which provided the important guidance to the design and operational management of other similar CCHP systems in the future.

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43

Wang, Zong Wu, Guo He Huang, and Yan Tao Niu. "A Chance-Constrained Interval-Inexact Energy Systems Planning Model (CCIESM) for City B Based on Power Demand Probabilistic Forecasting." Advanced Materials Research 753-755 (August 2013): 1891–902. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.1891.

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Energy management systems (EMS) are fraught with uncertainties, while current EMS models always deal with deterministic factors. The uncertainties in EMS could be expressed as interval values and probabilistic distributions. To tackle these uncertainties within EMS, a chance-constrained interval-inexact energy system planning model (CCIESM) was developed in this study, and the probability distribution of power demand was addressed with CCP, and interval values in the left and right hand was addressed with ILP. This probabilistic distribution was calculated through three models including relative electricity model, middle/long-term power demand prediction model and Shapiro-Wilk statistical model. The results of case study in city B indicated that CCIESM would have advantages of addressing interval-value and probabilistic distribution in EMS.

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44

Arrigo, Adriano, Christos Ordoudis, Jalal Kazempour, Zacharie De Grève, Jean-François Toubeau, and François Vallée. "Wasserstein distributionally robust chance-constrained optimization for energy and reserve dispatch: An exact and physically-bounded formulation." European Journal of Operational Research 296, no. 1 (2022): 304–22. http://dx.doi.org/10.1016/j.ejor.2021.04.015.

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45

Bazmohammadi, Najmeh, Ahmadreza Tahsiri, Amjad Anvari-Moghaddam, and Josep M. Guerrero. "Optimal operation management of a regional network of microgrids based on chance-constrained model predictive control." IET Generation, Transmission & Distribution 12, no. 15 (2018): 3772–79. http://dx.doi.org/10.1049/iet-gtd.2017.2061.

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46

Mijatović, Aleksandar, John Moriarty, and Jure Vogrinc. "Procuring load curtailment from local customers under uncertainty." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 375, no. 2100 (2017): 20160311. http://dx.doi.org/10.1098/rsta.2016.0311.

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Demand side response (DSR) provides a flexible approach to managing constrained power network assets. This is valuable if future asset utilization is uncertain. However there may be uncertainty over the process of procurement of DSR from customers. In this context we combine probabilistic modelling, simulation and optimization to identify economically optimal procurement policies from heterogeneous customers local to the asset, under chance constraints on the adequacy of the procured DSR. Mathematically this gives rise to a search over permutations, and we provide an illustrative example implementation and case study. This article is part of the themed issue ‘Energy management: flexibility, risk and optimization’.

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47

Wu, Jiasi, Buhan Zhang, Yazhou Jiang, Pei Bie, and Hang Li. "Chance-constrained stochastic congestion management of power systems considering uncertainty of wind power and demand side response." International Journal of Electrical Power & Energy Systems 107 (May 2019): 703–14. http://dx.doi.org/10.1016/j.ijepes.2018.12.026.

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48

Zhang, Shida, Shaoyun Ge, Hong Liu, Junkai Li, Chenghong Gu, and Chengshan Wang. "A Multi-objective Chance-constrained Information-gap Decision Model for Active Management to Accommodate Multiple Uncertainties in Distribution Networks." Journal of Modern Power Systems and Clean Energy 11, no. 1 (2023): 17–34. http://dx.doi.org/10.35833/mpce.2022.000193.

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Fekete, Krešimir, Srete Nikolovski, Zvonimir Klaić, and Ana Androjić. "Optimal Re-Dispatching of Cascaded Hydropower Plants Using Quadratic Programming and Chance-Constrained Programming." Energies 12, no. 9 (2019): 1604. http://dx.doi.org/10.3390/en12091604.

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Abstract:

Stochastic production from wind power plants imposes additional uncertainty in power system operation. It can cause problems in load and generation balancing in the power system and can also cause congestion in the transmission network. This paper deals with the problems of congestion in the transmission network, which are caused by the production of wind power plants. An optimization model for corrective congestion management is developed. Congestions are relieved by re-dispatching several cascaded hydropower plants. Optimization methodology covers the optimization period of one day divided into the 24 segments for each hour. The developed optimization methodology consists of two optimization stages. The objective of the first optimization stage is to obtain an optimal day-ahead dispatch plan of the hydropower plants that maximizes profit from selling energy to the day-ahead electricity market. If such a dispatch plan, together with the wind power plant production, causes congestion in the transmission network, the second optimization stage is started. The objective of the second optimization stage is the minimization of the re-dispatching of cascaded hydropower plants in order to avoid possible congestion. The concept of chance-constrained programming is used in order to consider uncertain wind power production. The first optimization stage is defined as a mixed-integer linear programming problem and the second optimization stage is defined as a quadratic programming (QP) problem, in combination with chance-constrained programming. The developed optimization model is tested and verified using the model of a real-life power system.

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Khishtandar, Soheila. "Simulation based evolutionary algorithms for fuzzy chance-constrained biogas supply chain design." Applied Energy 236 (February 2019): 183–95. http://dx.doi.org/10.1016/j.apenergy.2018.11.092.

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