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

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Bibliographic Details
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]
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Database:	Engineering Source

Description
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]
ISSN:	1819656X