Evidence map›Paper›PMID 39232023›Full record

ArticleScientific reports2024

An innovative bio-inspired Aquila technique for efficient solution of combined power and heat economic dispatch problem.

Sultan Hassan Hakmi, Ghareeb Moustafa, Hashim Alnami, Hany S E Mansour, Ahmed Ginidi

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In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Sultan Hassan HakmiElectrical Engineering Department, College of Engineering, Jazan University, 45142, Jazan, Saudi Arabia.
Ghareeb MoustafaElectrical Engineering Department, College of Engineering, Jazan University, 45142, Jazan, Saudi Arabia.
Hashim AlnamiElectrical Engineering Department, College of Engineering, Jazan University, 45142, Jazan, Saudi Arabia.
Hany S E MansourElectrical Engineering Department, Suez Canal University, Ismailia, 41522, Egypt.
Ahmed GinidiDepartment of Electrical Engineering, Faculty of Engineering, Suez University, P.O. Box: 43221, Suez, Egypt. ahmed.ginidi@eng.suezuni.edu.eg.ORCID http://orcid.org/0000-0001-7379-6060

Funding

Jazan University ISP23-122
6 · The paper itself

Abstract

In the field of power systems, the optimization challenge of combined heat and power units economic dispatch (CHPUED) holds immense importance. This study presents an improved Aquila optimization technique (IAQT) that effectively tackles the CHPUED. The primary objective of the enhanced IAQT model is to minimize the overall cost of power generation in CHP systems while satisfying demand and operational constraints. However, to achieve more accurate cost estimations and avoid suboptimal solutions, it is crucial to consider transmission losses in the optimization model. By incorporating transmission losses, the IAQT algorithm can allocate power generation resources more effectively, leading to improved system efficiency and reduced operational costs. The proposed IAQT algorithm addresses the limitations of the standard AQT and introduces novel features to enhance its search capabilities. One key limitation of the standard AQT is its heavy reliance on the best solution found during optimization. To overcome this drawback, the enhanced IAQT model eliminates the dependency on the best solution and enables a more thorough exploration of the search space. Moreover, the algorithm incorporates specific limitations and constraints for each dimension of the newly generated solutions, ensuring their feasibility and validity. The standard AQT and proposed IAQT are tested on CEC 20 benchmark functions. Moreover, the proposed approach is extensively evaluated through experimentation and testing on various scenarios, including 7-48-unit and large 96-unit systems with/without losses. Furthermore, the overall costs for the 7 unit-system are considered including the reserve constraint. The results exhibit the remarkable performance and efficiency of the enhanced IAQT model, outperforming the standard version and several previously reported results. This validation underscores the significant contribution of the study in addressing the CHPUED and highlights its potential for real-world applications.

Indexed as

Aquila optimizationCHP unitsEconomic dispatchImproved Aquila techniqueValve point loadings

Identifiers

PMID39232023
PMCPMC11375123

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.