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Improved Hybrid GWO-Based Multi-Objective Optimal Scheduling of CCHP Microgrids with Considering Risk Assessment.

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Title: Improved Hybrid GWO-Based Multi-Objective Optimal Scheduling of CCHP Microgrids with Considering Risk Assessment.
Authors: Luo, Wei1 lwgd86@126.com, Zhang, Chenwei1 zhangcwmz@126.com, Cao, Defa1 caodfmz@126.com, Wei, Cunliang1 52141961@qq.com, Du, Wanlin2 dwlgd@126.com, Chen, Weizhong1 chenwzmz@126.com, Wang, Jijun3 wangjijunmz@163.com
Source: IAENG International Journal of Computer Science. May2026, Vol. 53 Issue 5, p1684-1696. 13p.
Subjects: Grey Wolf Optimizer algorithm, Risk assessment, Technological risk assessment, Multi-objective optimization, Environmental protection, Microgrids, Renewable energy sources
Abstract: To achieve integrated energy supply and lowcarbon high efficiency operation, this paper develops a multi-objective optimal scheduling model for the combined cooling, heating, and power (CCHP) microgrids that simultaneously addresses economic, environmental, and operational risk considerations. The model comprehensively incorporates the operating characteristics of multiple distributed energy resources, the coupling relationships among different energy forms, and the dynamic nature of cooling, heating, and power loads. The three optimization objectives include total operating cost, environmental pollution cost, and comprehensive operational risk. The risk model quantifies the technical and economic impacts of uncertainties such as renewable energy intermittency, load forecast errors, and electricity market price fluctuations. To solve this complex nonlinear optimization problem with multiple constraints, an improved multi-objective Grey Wolf Optimization (MOGWO) algorithm is proposed. The improvement strategies involve Tent chaotic map initialization, adaptive nonlinear parameter control, and a hybrid position update mechanism integrating Particle Swarm Optimizer. Simulation studies are conducted to verify the performance of the improved algorithm in terms of convergence accuracy, solution diversity, and global search capability. The results show that the Pareto-optimal solution set obtained by the proposed method provides more flexible operational strategies for balancing economic, environmental, and risk-related objectives. This approach offers effective decision support for the green, efficient, and reliable scheduling of CCHP microgrids. [ABSTRACT FROM AUTHOR]
Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
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  Data: Improved Hybrid GWO-Based Multi-Objective Optimal Scheduling of CCHP Microgrids with Considering Risk Assessment.
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  Data: <searchLink fieldCode="AR" term="%22Luo%2C+Wei%22">Luo, Wei</searchLink><relatesTo>1</relatesTo><i> lwgd86@126.com</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Chenwei%22">Zhang, Chenwei</searchLink><relatesTo>1</relatesTo><i> zhangcwmz@126.com</i><br /><searchLink fieldCode="AR" term="%22Cao%2C+Defa%22">Cao, Defa</searchLink><relatesTo>1</relatesTo><i> caodfmz@126.com</i><br /><searchLink fieldCode="AR" term="%22Wei%2C+Cunliang%22">Wei, Cunliang</searchLink><relatesTo>1</relatesTo><i> 52141961@qq.com</i><br /><searchLink fieldCode="AR" term="%22Du%2C+Wanlin%22">Du, Wanlin</searchLink><relatesTo>2</relatesTo><i> dwlgd@126.com</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Weizhong%22">Chen, Weizhong</searchLink><relatesTo>1</relatesTo><i> chenwzmz@126.com</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Jijun%22">Wang, Jijun</searchLink><relatesTo>3</relatesTo><i> wangjijunmz@163.com</i>
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  Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Computer+Science%22">IAENG International Journal of Computer Science</searchLink>. May2026, Vol. 53 Issue 5, p1684-1696. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Grey+Wolf+Optimizer+algorithm%22">Grey Wolf Optimizer algorithm</searchLink><br /><searchLink fieldCode="DE" term="%22Risk+assessment%22">Risk assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Technological+risk+assessment%22">Technological risk assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+protection%22">Environmental protection</searchLink><br /><searchLink fieldCode="DE" term="%22Microgrids%22">Microgrids</searchLink><br /><searchLink fieldCode="DE" term="%22Renewable+energy+sources%22">Renewable energy sources</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: To achieve integrated energy supply and lowcarbon high efficiency operation, this paper develops a multi-objective optimal scheduling model for the combined cooling, heating, and power (CCHP) microgrids that simultaneously addresses economic, environmental, and operational risk considerations. The model comprehensively incorporates the operating characteristics of multiple distributed energy resources, the coupling relationships among different energy forms, and the dynamic nature of cooling, heating, and power loads. The three optimization objectives include total operating cost, environmental pollution cost, and comprehensive operational risk. The risk model quantifies the technical and economic impacts of uncertainties such as renewable energy intermittency, load forecast errors, and electricity market price fluctuations. To solve this complex nonlinear optimization problem with multiple constraints, an improved multi-objective Grey Wolf Optimization (MOGWO) algorithm is proposed. The improvement strategies involve Tent chaotic map initialization, adaptive nonlinear parameter control, and a hybrid position update mechanism integrating Particle Swarm Optimizer. Simulation studies are conducted to verify the performance of the improved algorithm in terms of convergence accuracy, solution diversity, and global search capability. The results show that the Pareto-optimal solution set obtained by the proposed method provides more flexible operational strategies for balancing economic, environmental, and risk-related objectives. This approach offers effective decision support for the green, efficient, and reliable scheduling of CCHP microgrids. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Code: eng
        Text: English
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        PageCount: 13
        StartPage: 1684
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      – SubjectFull: Grey Wolf Optimizer algorithm
        Type: general
      – SubjectFull: Risk assessment
        Type: general
      – SubjectFull: Technological risk assessment
        Type: general
      – SubjectFull: Multi-objective optimization
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      – SubjectFull: Environmental protection
        Type: general
      – SubjectFull: Microgrids
        Type: general
      – SubjectFull: Renewable energy sources
        Type: general
    Titles:
      – TitleFull: Improved Hybrid GWO-Based Multi-Objective Optimal Scheduling of CCHP Microgrids with Considering Risk Assessment.
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            NameFull: Luo, Wei
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            NameFull: Zhang, Chenwei
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            NameFull: Cao, Defa
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            NameFull: Du, Wanlin
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            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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