Optimal scheduling of integrated energy system based on improved grey wolf optimization algorithm.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
02 05 2022
02 05 2022
Historique:
received:
22
01
2022
accepted:
14
04
2022
entrez:
2
5
2022
pubmed:
3
5
2022
medline:
6
5
2022
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 35501451
doi: 10.1038/s41598-022-10958-7
pii: 10.1038/s41598-022-10958-7
pmc: PMC9061848
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Retracted Publication
Langues
eng
Sous-ensembles de citation
IM
Pagination
7095Commentaires et corrections
Type : RetractionIn
Informations de copyright
© 2022. The Author(s).
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