| 1/2022 - 11 | View TOC | « Previous Article | Next Article » |
Clustering-based Energy-aware Scheduling of Smart Residential AreaMUTHUSELVI, G.   , SARAVANAN, B. |
Extra paper information in    |
| Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science |
Download PDF  (3,629 KB) | Citation | Downloads: 857 | Views: 1,323 |
Author keywords
clustering algorithms, energy management, load management, meter reading, smart grids
References keywords
energy(18), smart(15), residential(15), demand(13), response(10), load(10), grid(10), clustering(7), data(6), analysis(5)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2022-02-28
Volume 22, Issue 1, Year 2022, On page(s): 95 - 102
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2022.01011
Web of Science Accession Number: 000762769600010
SCOPUS ID: 85126765661
Abstract
Updating power system networks without changing the existing network facilities is done by modifying the consumer's energy demand curve using the Demand Response (DR) program. The increase in energy consumption, its environmental impact and limits in generation illustrates the importance of energy savings and alternate usage as Demand side management (DSM). Clustering methods provide proper planning and management of loads during the DR program. DR congestion of residential electrical loads scheduling is effectively managed by clustering of all the load curves in the smart residential area. The purpose of clustering the consumers is to understand the different energy behaviour better and identify the typical seasonal consumption patterns for the residential consumers, thereby creating a smart control strategy for the DR program. This work mainly focuses on applying load clustering method to reshape the load curve in the residential area during summer. The optimal scheduling of loads using this proposed method provide peak load management, Peak to Average Ratio (PAR) reduction, and the minimization of electricity cost of the consumer. The proposed seasonal clustering-based scheduling framework is solved using CPLEX solver. |
| References | | | Cited By «-- Click to see who has cited this paper |
| [1] T. Teeraratkul, D. O'Neill, and S. Lall, "Shape-based approach to household electric load curve clustering and prediction," IEEE Trans. Smart Grid, vol. 9, no. 5, pp. 5196-5206, Sep. 2018, [CrossRef] [Web of Science Times Cited 109] [SCOPUS Times Cited 146] [2] S. Dasgupta, A. Srivastava, J. Cordova, and R. Arghandeh, "Clustering household electrical load profiles using elastic shape analysis," in 2019 IEEE Milan PowerTech, Milan, Italy, Jun. 2019, pp. 1-6. [CrossRef] [SCOPUS Times Cited 13] [3] G. Le Ray and P. Pinson, "Online adaptive clustering algorithm for load profiling," Sustain. Energy Grids Netw., vol. 17, p. 100181, Mar. 2019. [CrossRef] [Web of Science Times Cited 31] [SCOPUS Times Cited 46] [4] M. Sun, Y. Wang, G. Strbac, and C. Kang, "Probabilistic peak load estimation in smart cities using smart meter data," IEEE Trans. Ind. Electron., vol. 66, no. 2, pp. 1608-1618, Feb. 2019. [CrossRef] [Web of Science Times Cited 40] [SCOPUS Times Cited 49] [5] S. Mohammad Hoseini Mirzaei, B. Ganji, and S. Abbas Taher, "Performance improvement of distribution networks using the demand response resources," IET Gener. Transm. Distrib., vol. 13, no. 18, pp. 4171-4179, Sep. 2019. [CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 8] [6] A. Malik, N. Haghdadi, I. MacGill, and J. Ravishankar, "Appliance level data analysis of summer demand reduction potential from residential air conditioner control," Appl. Energy, vol. 235, pp. 776-785, Feb. 2019. [CrossRef] [Web of Science Times Cited 39] [SCOPUS Times Cited 49] [7] T. Li and M. Dong, "Real-time residential-side joint energy storage management and load scheduling with renewable integration," IEEE Trans. Smart Grid, vol. 9, no. 1, pp. 283-298, Jan. 2018. [CrossRef] [Web of Science Times Cited 64] [SCOPUS Times Cited 90] [8] B. Najafi, S. Moaveninejad, and F. Rinaldi, "Data analytics for energy disaggregation: Methods and applications," in Big Data Application in Power Systems, Elsevier, 2018, pp. 377-408. [CrossRef] [SCOPUS Times Cited 45] [9] Y. Wang et al., "Energy management of smart micro-grid with response loads and distributed generation considering demand response," J. Clean. Prod., vol. 197, pp. 1069-1083, Oct. 2018. [CrossRef] [Web of Science Times Cited 132] [SCOPUS Times Cited 163] [10] S. Nan, M. Zhou, and G. Li, "Optimal residential community demand response scheduling in smart grid," Appl. Energy, vol. 210, pp. 1280-1289, Jan. 2018. [CrossRef] [Web of Science Times Cited 216] [SCOPUS Times Cited 266] [11] M. A. Z. Alvarez, K. Agbossou, A. Cardenas, S. Kelouwani, and L. Boulon, "Demand response strategy applied to residential electric water heaters using dynamic programming and K-means clustering," IEEE Trans. Sustain. Energy, vol. 11, no. 1, pp. 524-533, Jan. 2020. [CrossRef] [Web of Science Times Cited 52] [SCOPUS Times Cited 74] [12] A. Satre-Meloy, M. Diakonova, and P. Grunewald, "Cluster analysis and prediction of residential peak demand profiles using occupant activity data," Appl. Energy, vol. 260, p. 114246, Feb. 2020. [CrossRef] [Web of Science Times Cited 78] [SCOPUS Times Cited 97] [13] M. Sun, Y. Wang, F. Teng, Y. Ye, G. Strbac, and C. Kang, "Clustering-based residential baseline estimation: A probabilistic perspective," IEEE Trans. Smart Grid, vol. 10, no. 6, pp. 6014-6028, Nov. 2019. [CrossRef] [Web of Science Times Cited 55] [SCOPUS Times Cited 71] [14] L. Chen, Y. Yang, and Q. Xu, "A two-stage control strategy of large-scale residential air conditionings considering comfort sensitivity of differentiated population," IEEE Access, vol. 7, pp. 126344-126354, 2019. [CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 12] [15] S. Lin, F. Li, E. Tian, Y. Fu, and D. Li, "Clustering load profiles for demand response applications," IEEE Trans. Smart Grid, vol. 10, no. 2, pp. 1599-1607, Mar. 2019. [CrossRef] [Web of Science Times Cited 77] [SCOPUS Times Cited 120] [16] J. Iria and F. Soares, "A cluster-based optimization approach to support the participation of an aggregator of a larger number of prosumers in the day-ahead energy market," Electr. Power Syst. Res., vol. 168, pp. 324-335, Mar. 2019. [CrossRef] [Web of Science Times Cited 42] [SCOPUS Times Cited 47] [17] M. Brolin and C. Sandels, "Controlling a retailer's shortâterm financial risk exposure using demand response," IET Gener. Transm. Distrib., vol. 13, no. 22, pp. 5160-5170, Nov. 2019. [CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 5] [18] Y. Ma, C. Li, J. Zhou, and Y. Zhang, "Comprehensive stochastic optimal scheduling in residential micro energy grid considering pumped-storage unit and demand response," J. Energy Storage, vol. 32, p. 101968, Dec. 2020. [CrossRef] [Web of Science Times Cited 31] [SCOPUS Times Cited 35] [19] H. Chamandoust, G. Derakhshan, S. M. Hakimi, and S. Bahramara, "Tri-objective scheduling of residential smart electrical distribution grids with optimal joint of responsive loads with renewable energy sources," J. Energy Storage, vol. 27, p. 101112, Feb. 2020. [CrossRef] [Web of Science Times Cited 103] [SCOPUS Times Cited 113] [20] E. Azizi, A. M. Shotorbani, M.-T. Hamidi-Beheshti, B. Mohammadi-Ivatloo, and S. Bolouki, "Residential household non-intrusive load monitoring via smart event-based optimization," IEEE Trans. Consum. Electron., vol. 66, no. 3, pp. 233-241, Aug. 2020. [CrossRef] [Web of Science Times Cited 36] [SCOPUS Times Cited 48] [21] F. Wang, K. Li, C. Liu, Z. Mi, M. Shafie-Khah, and J. P. S. Catalao, "Synchronous Pattern Matching Principle-Based Residential Demand Response Baseline Estimation: Mechanism analysis and approach description," IEEE Trans. Smart Grid, vol. 9, no. 6, pp. 6972-6985, Nov. 2018. [CrossRef] [Web of Science Times Cited 151] [SCOPUS Times Cited 173] [22] F. Elghitani and W. Zhuang, "Aggregating a large number of residential appliances for demand response applications," IEEE Trans. Smart Grid, vol. 9, no. 5, pp. 5092-5100, Sep. 2018. [CrossRef] [Web of Science Times Cited 51] [SCOPUS Times Cited 70] [23] L. Zhao, Z. Yang, and W.-J. Lee, "The impact of Time-of-Use (TOU) rate structure on consumption patterns of the residential customers," IEEE Trans. Ind. Appl., vol. 53, no. 6, pp. 5130-5138, Nov. 2017. [CrossRef] [Web of Science Times Cited 55] [SCOPUS Times Cited 71] [24] A. Al-Wakeel, J. Wu, and N. Jenkins, "K-means based load estimation of domestic smart meter measurements," Appl. Energy, vol. 194, pp. 333-342, May 2017. [CrossRef] [Web of Science Times Cited 107] [SCOPUS Times Cited 142] [25] S. Haben, C. Singleton, and P. Grindrod, "Analysis and clustering of residential customers energy behavioral demand using smart meter data," IEEE Trans. Smart Grid, vol. 7, no. 1, pp. 136-144, Jan. 2016. [CrossRef] [Web of Science Times Cited 234] [SCOPUS Times Cited 298] [26] J. Thakur and B. Chakraborty, "Demand side management in developing nations: A mitigating tool for energy imbalance and peak load management," Energy, vol. 114, pp. 895-912, Nov. 2016. [CrossRef] [Web of Science Times Cited 63] [SCOPUS Times Cited 74] [27] F. Luo, G. Ranzi, W. Kong, G. Liang, and Z. Y. Dong, "Personalized residential energy usage recommendation system based on load monitoring and collaborative filtering," IEEE Trans. Ind. Inform., vol. 17, no. 2, pp. 1253-1262, Feb. 2021. [CrossRef] [Web of Science Times Cited 30] [SCOPUS Times Cited 40] Web of Science® Citations for all references: 1,815 TCR SCOPUS® Citations for all references: 2,365 TCR Web of Science® Average Citations per reference: 65 ACR SCOPUS® Average Citations per reference: 84 ACR TCR = Total Citations for References / ACR = Average Citations per Reference We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more Citations for references updated on 2024-12-07 19:11 in 186 seconds. Note1: Web of Science® is a registered trademark of Clarivate Analytics. Note2: SCOPUS® is a registered trademark of Elsevier B.V. Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site. |
Copyright ©2001-2024
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
