{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:48:55Z","timestamp":1776811735715,"version":"3.51.2"},"reference-count":16,"publisher":"European Society of Computational Methods in Sciences and Engineering","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2023,10,6]]},"abstract":"<jats:p>With the large-scale grid integration of new energy sources, the power relationship between source and demand side is becoming more and more complex, the flexibility requirements of the distribution network are increasing, and new requirements for system safety and reliability are put forward, and it can\u2019t meet the peak regulating requirements of grid by relying solely on source side to cope with variable loads. In view of this, to make the grid operation flexibility improved by using an adjustable load capacity, the study first constructs a load classifying method on the foundation of fuzzy style K-plane clustering method to understand the interaction information of the load devices. Then the adjustable value of the load is analyzed from the demand response and standby perspectives, respectively, and an improved dynamic time-bending-based source-load similarity inscription method is proposed, which aims to unify the multiple load information obtained by clustering. The proposed clustering algorithm takes the highest value of 0.096 for the Davies-Bouldin index index, which is 0.012 and 0.014 higher than the K-plane clustering algorithm and the K-means algorithm, respectively. In addition, the load demand response regulation with the improved dynamic time bending method has a higher capacity for new energy consumption than the variance method, with a difference of 2.0 kW. This indicates that using load regulation to consume new energy to exploit the load curve will stimulate the active participation of customer-side load in maintaining power balance between the electricity consumption side and the demand side, and form an interest community between power supplying and demanding sides.<\/jats:p>","DOI":"10.3233\/jcm-226944","type":"journal-article","created":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T12:09:23Z","timestamp":1695384563000},"page":"2275-2289","source":"Crossref","is-referenced-by-count":2,"title":["Demand-side adjustable resources unified data model study"],"prefix":"10.66113","volume":"23","author":[{"given":"Juntao","family":"Wu","sequence":"first","affiliation":[{"name":"School of Information Engineering, Nanchang University, Nanchang, Jiangxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junjie","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Nanchang University, Nanchang, Jiangxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Songsong","family":"Chen","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Demand Side Multi-Energy Carriers Optimization and Interaction Technique (China Electric Power Research Institute), Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wentao","family":"Xu","sequence":"additional","affiliation":[{"name":"State Grid Ningxia Electric Power Co., Ltd., Yinchuan, Ningxia, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bihong","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Nanchang University, Nanchang, Jiangxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuan","family":"Wen","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Nanchang University, Nanchang, Jiangxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"55691","reference":[{"key":"10.3233\/JCM-226944_ref1","doi-asserted-by":"crossref","first-page":"112213","DOI":"10.1016\/j.rser.2022.112213","article-title":"A comprehensive review of stationary energy storage devices for large scale renewable energy sources grid integration","volume":"159","author":"Kebede","year":"2022","journal-title":"Renew Sust Energ Rev."},{"issue":"2","key":"10.3233\/JCM-226944_ref2","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1109\/TEM.2020.3046749","article-title":"Analyzing the public opinion as a guide for renewable-energy status in malaysia: a case study","volume":"40","author":"Qazi","year":"2021","journal-title":"IEEE T Eng Manage."},{"key":"10.3233\/JCM-226944_ref3","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1016\/j.egyr.2020.01.011","article-title":"Analysis of the robustness of energy supply in Japan: Role of renewable energy","volume":"6","author":"Zhu","year":"2020","journal-title":"Energy Rep."},{"issue":"6","key":"10.3233\/JCM-226944_ref4","doi-asserted-by":"crossref","first-page":"5167","DOI":"10.1016\/j.aej.2021.04.008","article-title":"Application of photovoltaic power generation in rail transit power supply system under the background of energy low carbon transformation","volume":"60","author":"Tian","year":"2021","journal-title":"Alex Eng J."},{"issue":"8","key":"10.3233\/JCM-226944_ref5","doi-asserted-by":"crossref","first-page":"7652","DOI":"10.1109\/TIE.2020.3007100","article-title":"An edge computing framework for powertrain control system optimization of intelligent and connected vehicles based on curiosity-driven deep reinforcement learning","volume":"68","author":"Hu","year":"2021","journal-title":"IEEE T Ind Electron."},{"issue":"2","key":"10.3233\/JCM-226944_ref6","doi-asserted-by":"crossref","first-page":"1112","DOI":"10.1109\/TCYB.2020.2983871","article-title":"Industrial power load forecasting method based on reinforcement learning and PSO-LSSVM","volume":"52","author":"Ge","year":"2020","journal-title":"IEEE T Cybernetics."},{"issue":"2","key":"10.3233\/JCM-226944_ref7","doi-asserted-by":"crossref","first-page":"237","DOI":"10.35833\/MPCE.2020.000472","article-title":"Electric load clustering in smart grid: Methodologies, applications, and future trends","volume":"9","author":"Si","year":"2021","journal-title":"J Mod Power Syst Cle."},{"issue":"8","key":"10.3233\/JCM-226944_ref8","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1049\/iet-com.2019.0359","article-title":"WOATCA: A secure and energy aware scheme based on whale optimisation in clustered wireless sensor networks","volume":"14","author":"Sharma","year":"2020","journal-title":"IET Commun."},{"key":"10.3233\/JCM-226944_ref9","doi-asserted-by":"crossref","first-page":"116249","DOI":"10.1016\/j.apenergy.2020.116249","article-title":"Short-term electric load forecasting for buildings using logistic mixture vector autoregressive model with curve registration","volume":"282","author":"Jeong","year":"2021","journal-title":"Appl Energ."},{"issue":"2","key":"10.3233\/JCM-226944_ref10","first-page":"261","article-title":"Short-term forecasting of individual residential load based on deep learning and K-means clustering","volume":"7","author":"Han","year":"2020","journal-title":"CSEE J Power Energy."},{"issue":"4","key":"10.3233\/JCM-226944_ref11","doi-asserted-by":"crossref","first-page":"3146","DOI":"10.1109\/TSG.2020.2967430","article-title":"Deep reinforcement learning method for demand response management of interruptible load","volume":"11","author":"Wang","year":"2020","journal-title":"IEEE T Smart Grid."},{"issue":"6","key":"10.3233\/JCM-226944_ref12","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.35833\/MPCE.2019.000449","article-title":"Technologies and practical implementations of air-conditioner based demand response","volume":"9","author":"Waseem","year":"2020","journal-title":"J Mod Power Syst Cle."},{"issue":"2","key":"10.3233\/JCM-226944_ref13","doi-asserted-by":"crossref","first-page":"1496","DOI":"10.1109\/TSG.2020.3037066","article-title":"Deep reinforcement learning for demand response in distribution networks","volume":"12","author":"Bahrami","year":"2020","journal-title":"IEEE T Smart Grid."},{"issue":"4","key":"10.3233\/JCM-226944_ref14","doi-asserted-by":"crossref","first-page":"4301","DOI":"10.1002\/er.7427","article-title":"A swarm intelligence approach for energy management of grid-connected microgrids with flexible load demand response","volume":"46","author":"Singh","year":"2022","journal-title":"Int J Energ Res."},{"key":"10.3233\/JCM-226944_ref15","doi-asserted-by":"crossref","first-page":"55279","DOI":"10.1109\/ACCESS.2020.2981877","article-title":"Algorithm for demand response to maximize the penetration of renewable energy","volume":"8","author":"Hadi","year":"2020","journal-title":"IEEE Access."},{"key":"10.3233\/JCM-226944_ref16","doi-asserted-by":"crossref","first-page":"107415","DOI":"10.1016\/j.ijepes.2021.107415","article-title":"Agent based simulation of centralized electricity transaction market using bi-level and Q-learning algorithm approach","volume":"134","author":"Namalomba","year":"2022","journal-title":"Int J Elec Power."}],"container-title":["Journal of Computational Methods in Sciences and Engineering"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JCM-226944","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:07:01Z","timestamp":1776809221000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JCM-226944"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,6]]},"references-count":16,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.3233\/jcm-226944","relation":{},"ISSN":["1472-7978","1875-8983"],"issn-type":[{"value":"1472-7978","type":"print"},{"value":"1875-8983","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,6]]}}}