Cargando…
RETRACTED ARTICLE: Optimal scheduling of integrated energy system based on improved grey wolf optimization algorithm
The optimal scheduling problem of integrated energy system (IES) has the characteristics of high-dimensional nonlinearity. Using the traditional Grey Wolf Optimizer (GWO) to solve the problem, it is easy to fall into a local optimum in the process of optimization, resulting in a low-quality scheduli...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9061848/ https://www.ncbi.nlm.nih.gov/pubmed/35501451 http://dx.doi.org/10.1038/s41598-022-10958-7 |
_version_ | 1784698808070832128 |
---|---|
author | Du, Jun Zhang, Zongnan Li, Menghan Guo, Jing Zhu, Kongge |
author_facet | Du, Jun Zhang, Zongnan Li, Menghan Guo, Jing Zhu, Kongge |
author_sort | Du, Jun |
collection | PubMed |
description | The optimal scheduling problem of integrated energy system (IES) has the characteristics of high-dimensional nonlinearity. Using the traditional Grey Wolf Optimizer (GWO) to solve the problem, it is easy to fall into a local optimum in the process of optimization, resulting in a low-quality scheduling scheme. Aiming at the dispatchability of electric and heat loads, this paper proposes an electric and heat comprehensive demand response model considering the participation of dispatchers. On the basis of incentive demand response, the group aggregation model of electrical load is constructed, and the electric load response model is constructed with the goal of minimizing the deviation between the dispatch signal and the load group aggregation characteristic model. Then, a heat load scheduling model is constructed according to the ambiguity of the human body's perception of temperature. On the basis of traditional GWO, the Fuzzy C-means (FCM) clustering algorithm is used to group wolves, which increases the diversity of the population, uses the Harris Hawk Optimizer (HHO) to design the prey to search for the best escape position, and reduces the local The optimal probability, and the use of Particle Swarm Optimizer (PSO) and Bat Optimizer (BO) to design the moving modes of different positions, increase the ability to find the global optimum, so as to obtain an Improved Gray Wolf Optimizer (IGWO), and then efficiently solve the model. IGWO can improve the defect of insufficient population diversity in the later stage of evolution, so that the population diversity can be better maintained during the entire evolution process. While taking into account the speed of optimization, it improves the algorithm's ability to jump out of the local optimum and realizes continuous deep search. Compared with the traditional intelligent Optimizer, IGWO has obvious improvement and achieved better results. At the same time, the comprehensive demand response that considers the dispatcher's desired signal improves the accommodation of new energy and reduces the operating cost of the system, and promotes the benign interaction between the source and the load. |
format | Online Article Text |
id | pubmed-9061848 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-90618482022-05-04 RETRACTED ARTICLE: Optimal scheduling of integrated energy system based on improved grey wolf optimization algorithm Du, Jun Zhang, Zongnan Li, Menghan Guo, Jing Zhu, Kongge Sci Rep Article The optimal scheduling problem of integrated energy system (IES) has the characteristics of high-dimensional nonlinearity. Using the traditional Grey Wolf Optimizer (GWO) to solve the problem, it is easy to fall into a local optimum in the process of optimization, resulting in a low-quality scheduling scheme. Aiming at the dispatchability of electric and heat loads, this paper proposes an electric and heat comprehensive demand response model considering the participation of dispatchers. On the basis of incentive demand response, the group aggregation model of electrical load is constructed, and the electric load response model is constructed with the goal of minimizing the deviation between the dispatch signal and the load group aggregation characteristic model. Then, a heat load scheduling model is constructed according to the ambiguity of the human body's perception of temperature. On the basis of traditional GWO, the Fuzzy C-means (FCM) clustering algorithm is used to group wolves, which increases the diversity of the population, uses the Harris Hawk Optimizer (HHO) to design the prey to search for the best escape position, and reduces the local The optimal probability, and the use of Particle Swarm Optimizer (PSO) and Bat Optimizer (BO) to design the moving modes of different positions, increase the ability to find the global optimum, so as to obtain an Improved Gray Wolf Optimizer (IGWO), and then efficiently solve the model. IGWO can improve the defect of insufficient population diversity in the later stage of evolution, so that the population diversity can be better maintained during the entire evolution process. While taking into account the speed of optimization, it improves the algorithm's ability to jump out of the local optimum and realizes continuous deep search. Compared with the traditional intelligent Optimizer, IGWO has obvious improvement and achieved better results. At the same time, the comprehensive demand response that considers the dispatcher's desired signal improves the accommodation of new energy and reduces the operating cost of the system, and promotes the benign interaction between the source and the load. Nature Publishing Group UK 2022-05-02 /pmc/articles/PMC9061848/ /pubmed/35501451 http://dx.doi.org/10.1038/s41598-022-10958-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Du, Jun Zhang, Zongnan Li, Menghan Guo, Jing Zhu, Kongge RETRACTED ARTICLE: Optimal scheduling of integrated energy system based on improved grey wolf optimization algorithm |
title | RETRACTED ARTICLE: Optimal scheduling of integrated energy system based on improved grey wolf optimization algorithm |
title_full | RETRACTED ARTICLE: Optimal scheduling of integrated energy system based on improved grey wolf optimization algorithm |
title_fullStr | RETRACTED ARTICLE: Optimal scheduling of integrated energy system based on improved grey wolf optimization algorithm |
title_full_unstemmed | RETRACTED ARTICLE: Optimal scheduling of integrated energy system based on improved grey wolf optimization algorithm |
title_short | RETRACTED ARTICLE: Optimal scheduling of integrated energy system based on improved grey wolf optimization algorithm |
title_sort | retracted article: optimal scheduling of integrated energy system based on improved grey wolf optimization algorithm |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9061848/ https://www.ncbi.nlm.nih.gov/pubmed/35501451 http://dx.doi.org/10.1038/s41598-022-10958-7 |
work_keys_str_mv | AT dujun retractedarticleoptimalschedulingofintegratedenergysystembasedonimprovedgreywolfoptimizationalgorithm AT zhangzongnan retractedarticleoptimalschedulingofintegratedenergysystembasedonimprovedgreywolfoptimizationalgorithm AT limenghan retractedarticleoptimalschedulingofintegratedenergysystembasedonimprovedgreywolfoptimizationalgorithm AT guojing retractedarticleoptimalschedulingofintegratedenergysystembasedonimprovedgreywolfoptimizationalgorithm AT zhukongge retractedarticleoptimalschedulingofintegratedenergysystembasedonimprovedgreywolfoptimizationalgorithm |