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Tsinghua Science and Technology 2015, 20(2): 117-129 https://doi.org/10.1109/TST.2015.7085625
Open Access | Issue | Published: 23 April 2015

Load Profiling and Its Application to Demand Response: A Review

Show Author's Information Yi Wang,  Qixin Chen,  Chongqing Kang( ),  Mingming Zhang,  Ke Wang,  Yun Zhao
Department of Electrical Engineering, Tsinghua University, Beijing 100084, China.
Electric Power Research Institute, China Southern Power Grid Co., Ltd., Guangzhou 510080, China
Keywords:
data mining, Advanced Metering Infrastructure (AMI), load profiling, demand response, customer segmentation
Cite this article:
Wang Y, Chen Q, Kang C, et al. Load Profiling and Its Application to Demand Response: A Review. Tsinghua Science and Technology, 2015, 20(2): 117-129. https://doi.org/10.1109/TST.2015.7085625
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Abstract Full text About this article

The smart grid has been revolutionizing electrical generation and consumption through a two-way flow of power and information. As an important information source from the demand side, Advanced Metering Infrastructure (AMI) has gained increasing popularity all over the world. By making full use of the data gathered by AMI, stakeholders of the electrical industry can have a better understanding of electrical consumption behavior. This is a significant strategy to improve operation efficiency and enhance power grid reliability. To implement this strategy, researchers have explored many data mining techniques for load profiling. This paper performs a state-of-the-art, comprehensive review of these data mining techniques from the perspectives of different technical approaches including direct clustering, indirect clustering, clustering evaluation criteria, and customer segmentation. On this basis, the prospects for implementing load profiling to demand response applications, price-based and incentive-based, are further summarized. Finally, challenges and opportunities of load profiling techniques in future power industry, especially in a demand response world, are discussed.


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About this article

Load Profiling and Its Application to Demand Response: A Review

Show Author's information Yi Wang, Qixin Chen, Chongqing Kang( ), Mingming Zhang, Ke Wang, Yun Zhao
Department of Electrical Engineering, Tsinghua University, Beijing 100084, China.
Electric Power Research Institute, China Southern Power Grid Co., Ltd., Guangzhou 510080, China

Abstract

The smart grid has been revolutionizing electrical generation and consumption through a two-way flow of power and information. As an important information source from the demand side, Advanced Metering Infrastructure (AMI) has gained increasing popularity all over the world. By making full use of the data gathered by AMI, stakeholders of the electrical industry can have a better understanding of electrical consumption behavior. This is a significant strategy to improve operation efficiency and enhance power grid reliability. To implement this strategy, researchers have explored many data mining techniques for load profiling. This paper performs a state-of-the-art, comprehensive review of these data mining techniques from the perspectives of different technical approaches including direct clustering, indirect clustering, clustering evaluation criteria, and customer segmentation. On this basis, the prospects for implementing load profiling to demand response applications, price-based and incentive-based, are further summarized. Finally, challenges and opportunities of load profiling techniques in future power industry, especially in a demand response world, are discussed.

Keywords: data mining, Advanced Metering Infrastructure (AMI), load profiling, demand response, customer segmentation

References(63)

[1]
USA Department of Energy, Smart grid, http://energy.gov/oe/technology-development/smart-grid, 2014.
[2]
Wang, J. Meng, K. Cao, J. Chen, Z. Gao, L. and Lin, C. Electricity services based dependability model of power grid communication networking, Tsinghua Science and Technology, vol. 19, no. 2, pp. 121-132, 2014.
DOI Google Scholar
[3]
IEE Report, Utility-scale smart meter deployments, plans, & proposals, http://www.edisonfoundation.net/iee/Documents/IEE SmartMeterRollouts_0512.pdf, 2012.
[4]
Ma X. and Liang, Q. Current situation and development tendency of smart meters, (in Chinese), Energy and Energy Conservation,vol. 40, no. 3, pp. 169-174, 2014.
Google Scholar
[5]
Jiang, R. Lu, R. Wang, Y. Luo, J. Shen, C. and Shen, X. Energy-theft detection issues for advanced metering infrastructure in smart grid, Tsinghua Science and Technology, vol. 19, no. 2, pp. 105-120, 2014.
DOI Google Scholar
[6]
Conejo, A. J. Morales, J. M. and Baringo, L. Real-time demand response model, IEEE Transactions on Smart Grid, vol. 1, no. 3, pp. 236-242, 2010.
DOI Google Scholar
[7]
Rahimi F. and Ipakchi, A. Demand response as a market resource under the smart grid paradigm, IEEE Transactions on Smart Grid, vol. 1, no. 1, pp. 82-88, 2010.
DOI Google Scholar
[8]
Prahastono, I. King, D. and Ozveren, C. S. A review of electricity load profile classification methods, in Universities Power Engineering Conference, 2007. UPEC 2007. 42nd International. IEEE, 2007, pp. 1187-1191.
[9]
Chicco, G. Overview and performance assessment of the clustering methods for electrical load pattern grouping, Energy, vol. 42, no. 1, pp. 68-80, 2012.
DOI Google Scholar
[10]
Mcloughlin, F. Duffy, A. and Conlon, M. Evaluation of time series techniques to characterise domestic electricity demand, Energy, vol. 50, no. 1, pp. 120-130, 2013.
DOI Google Scholar
[11]
Zhou, K. Yang, S. and Shen, C. A review of electric load classification in smart grid environment, Renewable and Sustainable Energy Reviews, vol. 24, pp. 103-110, 2013.
DOI Google Scholar
[12]
Tsekouras, G. J. Kotoulas, P. B. Tsirekis, C. D. Dialynas, E. N. and Hatziargyriou, N. D. A pattern recognition methodology for evaluation of load profiles and typical days of large electricity customers, Electric Power Systems Research, vol. 78, no. 9, pp. 1494-1510, 2008.
DOI Google Scholar
[13]
Chicco, G. Napoli, R. and Piglione, F. Comparisons among clustering techniques for electricity customer classification, IEEE Transactions on Power Systems, vol. 21, no. 2, pp. 933-940, 2006.
DOI Google Scholar
[14]
Chicco, G. Napoli, R. and Piglione, F. Load pattern-based classification of electricity customers, IEEE Transactions on Power Systems, vol. 19, no. 2, pp. 1232-1239, 2004.
DOI Google Scholar
[15]
Chicco, G. Ionel, O. and Porumb, R. Electrical load pattern grouping based on centroid model with ant colony clustering, IEEE Transactions on Power Systems, vol. 28, no. 2, pp. 1706-1715, 2013.
DOI Google Scholar
[16]
Chicco G. and Ilie, I. S. Support vector clustering of electrical load pattern data, IEEE Transactions on Power Systems, vol. 24, no. 3, pp. 1619-1628, 2009.
DOI Google Scholar
[17]
Mutanen, A. Ruska, M. Repo, S. and Jarventausta, P. Customer classification and load profiling method for distribution systems, IEEE Transactions on Power Delivery, vol. 26, no. 3, pp. 1755-1753, 2011.
DOI Google Scholar
[18]
Ramos, S. Vale, Z. Santana, J. and Duarte, J. Data mining contributions to characterize MV consumers and to improve the suppliers-consumers settlements, in Power Engineering Society General Meeting, IEEE, 2007, pp. 1-8.
[19]
Tsekouras, G. J. Hatziargyriou, N. D. and Dialynas, E. N. Two-stage pattern recognition of load curves for classification of electricity customers, IEEE Transactions on Power Systems, vol. 22, no. 3, pp. 1120-1128, 2007.
DOI Google Scholar
[20]
Piao, M. Shon, H. S. Lee, J. Y. and Ryu, K. H. Subspace projection method based clustering analysis in load profiling, IEEE Transactions on Power Systems, vol. 29, no. 6, pp. 2628-2635, 2014.
DOI Google Scholar
[21]
Abrahams M. and Kattenfeld, M. Two-stage fuzzy clustering approach for load profiling, in Universities Power Engineering Conference (UPEC), 2009 Proceedings of the 44th International. IEEE, 2009, pp. 1-5.
[22]
Koivisto, M. Heine, P. Mellin, I. and Lehtonen, M. Clustering of connection points and load modeling in distribution systems, IEEE Transactions on Power Systems, vol. 21, no. 2, pp. 933-940, 2013.
Google Scholar
[23]
Abrahams M. and Kattenfeld, M. Comparisons among clustering techniques for electricity customer classification, IEEE Transactions on Power Systems, vol. 21, no. 2, pp. 933-940, 2006.
DOI Google Scholar
[24]
Hinton G. E. and Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks, Science, vol. 313, no. 5786, pp. 504-507, 2006.
DOI Google Scholar
[25]
Varga, E. D. Beretka, S. F. Noce, C. and Sapienza, G. Robust real-time load profile encoding and classification framework for efficient power systems operation, IEEE Transactions on Power Systems, (To be published). .
DOI Google Scholar
[26]
Tenenbaum, J. B. Mapping a manifold of perceptual observations, Advances in Neural Information Processing Systems, pp. 682-688, 1998.
Google Scholar
[27]
Saul J. K. and Roweis, S. T. Think globally, fit locally: Unsupervised learning of low dimensional manifolds, The Journal of Machine Learning Research, vol. 4, pp. 119-155, 2003.
Google Scholar
[28]
Zhong S. and Tam, K. Hierarchical classification of load profiles based on their characteristic attributes in frequency domain, IEEE Transactions on Power Systems, (To be published). .
DOI Google Scholar
[29]
Verdu, S. V. Garcia, M. O. Senabre, C. Marin, A. G. and Franco, F. J. G. Classification, filtering, and identification of electrical customer load patterns through the use of self-organizing maps, IEEE Transactions on Power Systems, vol. 21, no. 4, pp. 1672-1682, 2006.
DOI Google Scholar
[30]
Xiao, Y. Yang, J. Que, H. Li, M. J. and Gao, Q. Application of wavelet-based clustering approach to load profiling on AMI measurements, in Electricity Distribution (CICED), 2014 China International Conference on, IEEE, 2014, pp. 1537-1540.
[31]
Abrahams M. and Kattenfeld, M. A symbolic representation of time series, with implications for streaming algorithms, Behav. Ecol. Sociobiol., vol. 40, no. 3, pp. 169-174, 1997.
DOI Google Scholar
[32]
Notaristefano, A. Chicco, G. and Piglione, F. Data size reduction with symbolic aggregate approximation for electrical load pattern grouping, Generation, Transmission & Distribution, IET, vol. 7, no. 2, pp. 108-117, 2013.
DOI Google Scholar
[33]
Albert A. and Rajagopal, R. Smart meter driven segmentation: What your consumption says about you, IEEE Transactions on Power Systems, vol. 28, no. 4, pp. 4019-4030, 2013.
DOI Google Scholar
[34]
Lhermittea, S. Verbesseltc, J. Verstraetenb, W. W. and Coppine, P. A comparison of time series similarity measures for classification and change detection of ecosystem dynamics, Remote Sensing of Environment, vol. 115, no. 12, pp. 3129-3152, 2011.
DOI Google Scholar
[35]
Granell, R. Axon, C. J. and Wallom, D. C. H. Impacts of raw data temporal resolution using selected clustering methods on residential electricity load profiles, IEEE Transactions on Power Systems, (To be published). .
DOI Google Scholar
[36]
Mahmoudi-Kohan, N. Eghbal, M. and Moghaddam, M. P. Customer recognition-based demand response implementation by an electricity retailer, in Universities Power Engineering Conference (AUPEC), 2011 21st Australasian, IEEE, 2011, pp. 1-6.
[37]
Chicco, G. Napoli, R. Postolache, P. Scutariu, M. and Toader, C. Customer characterization options for improving the tariff offer, IEEE Transactions on Power Systems, vol. 18, no. 1, pp. 381-387, 2003.
DOI Google Scholar
[38]
Panapakidis, I. P. Alexiadis, M. C. and Papagiannis, G. K. Deriving the optimal number of clusters in the electricity consumer segmentation procedure, in European Energy Market (EEM), 2013 10th International Conference on the, IEEE, 2013, pp. 1-8.
[39]
Carpanetoa, E. Chiccoa, G. Napolia, R. and Scutariub, M. Electricity customer classification using frequency–domain load pattern data, International Journal of Electrical Power & Energy Systems, vol. 28, no. 1, pp. 13-20, 2013.
DOI Google Scholar
[40]
Chicco G. and Akilimali, J. S. Renyi entropy-based classification of daily electrical load patterns, IET Generation, Transmission & Distribution, vol. 4, no. 6, pp. 736-745, 2010.
DOI Google Scholar
[41]
Gerbec D. and Gasperic, S. Determining the load profiles of consumers based on fuzzy logic and probability neural networks, IEE Proceedings - Generation, vol. 151, no. 3, pp. 395-400, 2004.
DOI Google Scholar
[42]
Ramos, S. Vale, Z. Santana, J. and Duarte, J. Data mining contributions to characterize MV consumers and to improve the suppliers-consumers settlements, in Power Engineering Society General Meeting, 2007, IEEE, 2007, pp. 1-8.
[43]
Mahmoudi-Kohan, N. Moghaddam, M. P. and Bidaki, S. M. Evaluating performance of WFA K-means and modified follow the leader methods for clustering load curves, in Power Systems Conference and Exposition, 2009, PSCE’09, IEEE/PES, 2009, pp. 1-5,
[44]
Zhang, T. Zhang, G. Lu, J. Feng, X. and Yang, W. A new index and classification approach for load pattern analysis of large electricity customers, IEEE Transactions on Power Systems, vol. 27, no. 1, pp. 153-160, 2012.
DOI Google Scholar
[45]
Chang R. F. and Lu, C. N. Load profiling and its applications in power market, in Power Engineering Society General Meeting, 2003, IEEE, 2003, pp. 1-8.
[46]
Figueiredo, V. Rodrigues, F. Vale, Z. and Gouveia, J. B. An electric energy consumer characterization framework based on data mining techniques, IEEE Transactions on Power Systems, vol. 20, no. 2, pp. 596-602, 2005.
DOI Google Scholar
[47]
Ramos S. and Vale, Z. Data mining techniques to support the classification of MV electricity customers, in Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century, 2008, IEEE, 2008, pp. 1-8.
[48]
Li, R. Gu, C. Li, F. Shaddick, G. and Dale, M. Development of low voltage network templates—Part I: Substation clustering and classification, IEEE Transactions on Power Systems, (To be published). .
DOI Google Scholar
[49]
Li, R. Gu, C. Li, F. Shaddick, G. and Dale, M. Development of low voltage network templates—Part II: Peak load estimation by clusterwise regression, IEEE Transactions on Power Systems, (To be published). .
DOI Google Scholar
[50]
Mcloughlin, F. Duffy, A. and Conlon, M. A clustering approach to domestic electricity load profile characterization using smart metering data, Applied Energy, vol. 141, pp. 190-199, 2015.
DOI Google Scholar
[51]
O Connella, N. Pinsonb, P. Madsena, H. and O Malleyc, M. Benefits and challenges of electrical demand response: A critical review, Renewable and Sustainable Energy Reviews, vol. 39, pp. 686-699, 2014.
DOI Google Scholar
[52]
Mahmoudi-Kohana, N. Parsa Moghaddamb, M. Sheikh-El-Eslamib, M. K. and Shayesteha, E. A three-stage strategy for optimal price offering by a retailer based on clustering techniques, International Journal of Electrical Power & Energy Systems, vol. 32, no. 10, pp. 1135-1142, 2010.
DOI Google Scholar
[53]
Mahmoudi-Kohana, N. Parsa Moghaddamb, M. and Sheikh-El-Eslamib, M. K. An annual framework for clustering-based pricing for an electricity retailer, Electric Power Systems Research, vol. 80, no. 9, pp. 1042-1048, 2010.
DOI Google Scholar
[54]
Panapakidis, I. P. Simoglou, C. K. Alexiadis, M. C. and Papagiannis, G. K. Determination of the optimal electricity selling price of a retailer via load profiling, in Universities Power Engineering Conference (UPEC), 2012 47th International, IEEE, 2012, pp. 1-6.
[55]
Maigha and Crow, M. L. Clustering-based methodology for optimal residential time of use design structure, in North American Power Symposium (NAPS), 2014, IEEE, 2014, pp. 1-6.
[56]
Kwac, J. Flora, J. and Rajagopal, R. Household energy consumption segmentation using hourly data, IEEE Transactions on Smart Grid, vol. 5, no. 1, pp. 420-430, 2014.
DOI Google Scholar
[57]
Kwac, J. Tan, C.-W. Sintov, N. Flora, J. and Rajagopal, R. Utility customer segmentation based on smart meter data: Empirical study, in Smart Grid Communications (SmartGridComm), 2013 IEEE International Conference on, IEEE, 2013, pp. 720-725.
[58]
Albert A. and Rajagopal, R. Thermal profiling of residential energy use, IEEE Transactions on Power System, vol. 30, no. 2, pp. 602-611, 2015.
DOI Google Scholar
[59]
Labeeuw, W. Stragier, J. and Deconinck, G. Potential of active demand reduction with residential wet appliances: A case study for Belgium, IEEE Transactions on Smart Grid, vol. 6, no. 1, pp. 315-323, 2015.
DOI Google Scholar
[60]
Chen, Z. Dong, W. Li, H. Zhang, P. Chen, X. and Cao, J. Collaborative network security in multi-tenant data center for cloud computing, Tsinghua Science and Technology, vol. 19, no. 1, pp. 82-94, 2014.
DOI Google Scholar
[61]
Wu, X. Zhu, X. Wu, G. and Ding, W. Data mining with big data, IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 1, pp. 97-107, 2014.
DOI Google Scholar
[62]
Jeremy, R. The Third Industrial Revolution: How Lateral Power Is Transforming Energy, the Economy, and the World. New York, USA: Palgrave Macmillan, 2011.
[63]
Cao J. and Yang, M. Energy internet—Towards smart grid 2.0, in Networking and Distributed Computing (ICNDC), 2013 Fourth International Conference on, IEEE, 2013, pp. 105-110.
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Received: 09 February 2015
Accepted: 09 March 2015
Published: 23 April 2015
Issue date: April 2015

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© The author(s) 2015

Acknowledgements

This work was supported by the National Science Fund for Distinguished Young Scholars (No. 51325702).

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