Quantized deep learning model based Volt-Var control for hosting capacity maximization: a practical case study

Approved

Classifications

MinEdu publication type
A1 Journal article (peer-reviewed)
Definition
Article
Target group
Scientific
Peer reviewed
Peer-reviewed
Article type
Journal article
Host publication type
Journal

Authors of the publication

Number of authors
4
Authors
Khan, Muhammad Kamran; Kauhaniemi, Kimmo; Laaksonen, Hannu; Zafar, Muhammad Hamza

Publication channel information

Title of journal/series
International journal of electrical power and energy systems
ISSN (print)
0142-0615
ISSN (electronic)
1879-3517
ISSN (linking)
0142-0615
Publisher
Elsevier
Publication forum ID
58403
Publication forum level
2
Publication appears in FT-list
No
SNIP-level of the publication
1.61
Country of publication
United Kingdom
Internationality
Yes

Detailed publication information

Publication year
2026
Reporting year
2026
Journal/series volume number
174
Article number
111524
DOI
10.1016/j.ijepes.2025.111524
Language of publication
English

Co-publication information

International co-publication
Yes
Co-publication with a company
No

Availability

Classification and additional information

MinEdu field of science classification
222 Other engineering and technologies
Keywords
Hosting capacity (HC); Modified Reptile search Algorithm (MRSA); Quantized 1D Convolutional Neural Network (QCNN); Decoupled Finite Control Set Model Predictive control (D-FCS-MPC); Post-Training Quantization (PTQ); EN 50549 standard

Funding information

Funding information in the publication
This work was carried out under projects titled Smart Grid 2.0 and Grid Code Certification by Simulation, with financial support provided by Business Finland under Grant #1386/31/2022 and Grant #2452/31/ 2024.
Funders
Funder
Business Finland
Name of funding
-
Funding decision
1386/31/2022
Funder
Business Finland
Name of funding
-
Funding decision
2452/31/2024

Research data information

Research data information in the publication
Data will be made available on request.

Source database ID

WoS ID
WOS:001663731100001
Scopus ID
2-s2.0-105027315171