Adaptive Data-Driven Peak Shaving in Smart Grid Electricity Energy by Advanced Metering Infrastructure Data Analytics

Document Type : Original Article

Authors

1 SMRL, CIPCE, School of ECE, University of Tehran, Tehran, Iran

2 Faculty of Electrical and Computer Engineering, K.N.Toosi University of Technology, Tehran, Iran

Abstract

In this paper, a novel procedure is proposed to identify the most efficient group of customers for participating in the peak shaving from utility companies’ point of view. This procedure is based on the smart meter data in advanced metering infrastructure (AMI), data mining and pattern recognition algorithms. Studies implies that customers with different consumption behaviors show different effects on the peak load. Consumption pattern recognition in addition to considering networks condition from consumers’ distribution point of view culminates in the most efficient group of them for this aim. The most efficient selection is made when the expected load profile is achieved by affecting the least number of customer as possible. The analysis and results of this paper confirm effectiveness of the proposed data-driven method. This method is able to reduce the number of affected customers in a peak shaving program by identifying the most efficient group of customers in a near real-time data exchanging in the grid. It should be noted that, the proposed method is implemented on a real dataset related to the Irish anonymized households’ consumption data which is provided from Irish Social Science Data Archive (ISSDA).

Keywords


[1]      International Energy Agency, World Energy Outlook, 2009, Available: http:// www.world energyoutlook.org/docs/weo 2009/ WEO 2009 es English.pdf.
[2]      ع. شهسواری، پایان‌نامه کارشناسی ارشد، "رویکرد تقویت ترمیم گر به خودترمیم شبکه‌ی هوشمند با مدل‌سازی قابلیت اطمینان شبکه هوشمند"، دکتر ح. لسانی(راهنما)، ع. فریدونیان(مشاور)، دانشگاه تهران، 1392
[3]      A.Fereidunian, H.Lesani, C.Lucas, “Distribution System Reconfiguration Using Pattern Recognizer Neural Networks”, International Journal of Engineering (IJE), Vol. 15, No. 2, pp. 135-144, 2002.
[4]      W.Gellings, Clark, “The Concept of Demand-Side Management for Electric Utilities”, In Proc of the IEEE, Vol. 73, No. 10, October 1995.
[5]      U.S. Department of Energy, “Benefits of Demand Response in energy markets and recommendations for achieving them”, Report to the United State Congress, February 2006, available online: http://eetd.lbl.gov.
[6]      ح. اعلمی، م. پارسا مقدم، غ. ر. یوسفی، "مدل‌سازی پاسخگویی بار مبتنی بر ضرایب حساسیت قیمتی تقاضا"، رساله دکتری، دانشگاه تربیت مدرس، بهار 1389
[7]      A.Safdarian, M.Fotuhi-Firuzabad, M.Lehtonen, “A distributed algorithm for managing residential demand response in smart grid”, IEEE trans, Industrial Informatics, Vol. 10, pp. 2385-2393, Nov. 2014.
[8]      A.Safdarian, M.Fotuhi-Firuzabad, M.Lehtonen, “optimal residential load management in smart grid: a decentralized framework”, IEEE trans, smart grid, Vol. 7, pp. 1836-1845, Nov. 2016.
[9]      S. Roy, B. Bedanta, S. Dawnee, “Advanced Metering Infrastructure for real-time load management in a Smart Grid”, International Conf, Power and Advanced Control Engineering (ICPACE), Bangalore, pp. 104-108, Aug. 2015.
[10]      م. کجوری نفت‌چالی، ح. لسانی، ع. فریدونیان، "داده‌کاوی در انباره داده زیرساخت اندازه‌گیری پیشرفته"، پایان‌نامه کارشناسی ارشد، دانشگاه تهران، تابستان 1395
[11]      M.spinoza, C.joie, R.belmanse, B.de moor, ‘’Short-term load forcasting, profile identification, and customer segmentation: a metodology based on time-series’’, IEEE Transactions on Power systems, Vol.20. No.3, AUGUST 2005.
[12]      م. کجوری نفت‌چالی، ع. فریدونیان، ح. لسانی، "شناسایی تغییرات در رفتار مصرفی مشترکین با استفاده از خوشه‌بندی فازی"، پنجمین کنفرانس شبکه‌های هوشمند(SGC 2016)، ایران، تهران، دانشگاه علم و صنعت، 1394
[13]      J. Kwac,, J. Flora, and R. Rajagopal,”Household  Energy  Consumption  Segmentation  using  Hourly  data”, IEEE Trans. Smart Grid, Vol. 5, Jan. 2014.
[14]      M.K.Naftchali, A.Fereidunian, H.Lesani, “Identifying Susceptible Consumers for Demand Response and Energy Efficiency Policies by Time-Series Analysis and Supplementary Approaches”, 24th Iranian Conference Electric and Electronic (ICEE 2016), Shiraz, Iran, 2016.
[15]      م. کجوری، ع. فریدونیان، ح. لسانی، " استفاده از الگوریتم خوشه‌بندی طیفی برای شناسایی الگوی مصرف مشترکین و تعیین تعرفه‌های بهینه مصرفی در شبکه برق"، پنجمین کنفرانس منطقه‌ای سیرد، پژوهشگاه نیرو، دی‌ماه 1395
[16]      H.nishihara, I.Taniguchi, S.Kato, and M.Fukui, “A Real-Time Power Distribution based on Load/Generation Forecasting for Peak Shaving”, 11th International Conference on New Circuits and Systems (NEWCAS), 2013 IEEE , Paris, France, 16-19 June 2013.
[17]      J. Dong, F. Gao, X. Guan, Q. Zhai, and J. Wu, “Storage Sizing with Peak Shaving Policy for Wind farm based on Cyclic Markov Chain Model”, IEEE Transactions on Sustainable Energy, Vol.8, pp. 978-989, Issue: 3, July 2017
[18]      A. Rahimi, M. Zarghami, M. Vaziri, S.Vadhva, “A Simple and Effective Approach for Peak Shaving Using Battery Storage Systems”, North American Power Symposium (NAPS), 2013, Manhattan, KS, USA, 22-24 Sept. 2013
[19]      J. Zupancic, E.Lakic, T. Medved, and A. F. Gubina, “Advanced Peak Shaving Control Strategies for Battery Storage Operation in Low Voltage Distribution Network”,  PowerTech, 2017 IEEE Manchester, Manchester, united kingdom, 18-22 June 2017
[20]      Z. Taylor, H. Akhavan-Hejazi, E. Cortez, L. Alvarez, S. Ula, M. Barth, and H. Mohsenian-Rad, “Customer-Side SCADA-Assisted Large Battery Operation Optimization for Distribution Feeder Peak Load Shaving”, IEEE Transactions on Smart Grid, 2017
[21]      S. Khatiri-Doost, M. Amirahmadi, “Peak Shaving and Power Losses Minimization by Coordination of Plug-in Electric Vehicles Charging and Discharging in Smart Grids”, Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), 2017 IEEE International Conference on, milan, italy, 6-9 June 2017.11.08
[22]      Z. Wang, and S. Wang, “Grid Power Peak Shaving and Vally Filling Using Vehicle-to-Grid Systems”, IEEE Transactions on Power Delivery, vol. 28, pp. 1822-1829, 2013.
[23]      H. Turker, A. Hably, S. Bacha, “Housing Peak Shaving Algorithm (HPSA) with Plug-in Hybrid Electric Vehicles (phevs): Vehicle-to-Home (v2h) and Vehicle-to-Grid (v2g) Concepts”, Power Engineering, Energy and Electrical Drives (POWERENG), 2013 Fourth International Conference on, Istanbul, turkey, 13-17 May 2013
[24]      B. J. Claessens, S. Vandael, F.Ruelens, K. De Creamer, and B. Beusen, “Peak Shaving of a Heterogeneous Cluster of Residential Flexibility Carriers Using Reinforcement Learning”, Innovative Smart Grid Technologies Europe (ISGT EUROPE), 2013 4th IEEE/PES,  Lyngby, Denmark, 6-9 Oct. 2013.
[25]      F. Claessen, smart grid control, 1 sd, master thesis Utrecht university, 2012.
[26]      N. Leemput, F. Geth, B. Classens, J. Van Roy, R. Ponnette, and J. Driesen, “Acase Study of Coordinated electric Vehicle charging for Peak Shaving on a low Voltage Grid”, in innovative smart grid technologies (ISGT Europe), 2012, 3rd IEEE PES International Conference AND Exhibition on 2012, pp. 1-7.
[27]      M. G. C. Bosman, Planning in Smart Grid, phd thesisuniversity of twente, 2012
[28]      A. S. Hintz, K. Rajashekara, R. Prasanna, “Controller for Combined Peak-Load Shaving and Capacity Firming Utilizing Multiple Energy Storage Units in Microgrid”, 2016 IEEE Energy Conversion Congress and Exposition (ECCE), 2016
[29]      S. U. Agamah, L. Ekonomou, “Peak Demand Shaving and Load-Levelling Using a Combination of bin Packing and Subset Sum Algorithms for Electrical Energy Storage System Scheduling”, IET Science, Measurement & Technology, vol. 10, pp. 477-484, 2016
[30]      L. Chuan, D. M. K. K. Venkateswara, “Load Profiling of Singapore Buildings for Peak Shaving”, 2014 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), 2014
[31]      Thomas M. Cover, Joy A. Thomas, “Elements of Information Theory”, 2nd edition, Wiley, September, 2006
[32]      K. L. Wu, M. S. Yang, “A Cluster Validity Index for Fuzzy Clustering”, pattern recognition letters, pp. 1275-291, 2005
[33]      J. Han, M. Kamber (2006) “Data Mining concepts and techniques” 2nd edition, Morgan Kaufmann publisher.
[34]      H. Aalami, G. R. Yousefi, M. Parsa Moghadam, “A MADM-Based Support System for DR Program”, 43rd International Universities Power Engineering Conference (UPEC). padova, Italy, 1-4 septamber 2008
[35]      H. Aalami, G. R. Yousefi, M. Parsa Moghadam, “Demand Response Model Considering EDRP and TOU Program”,  Transmission and Distribution Conference and Exposition, Chicago, il, usa, 21-24 april 208.
[36]      P. Teimourzadeh Baboli, M. Eghbal, M. Parsa Moghddam, H. Aalami, “Customer Behavior Based Demand Response Model”, Power and Energy Society General Meeting, san diego, ca, usa, 22-26 july 2012
[37]      ف. محمدی، ح. عبدی، ا. دهنوی، "مسئله توزیع بار اقتصادی هزینه-آلودگی دینامیک همراه با برنامه پاسخگویی بار اضطراری بهینه تحت قیود اثر نقطه-دریچه و ذخیره چرخان"، مجله مهندسی برق دانشگاه تبریز، جلد46، شماره1، بهار1395
[38]      ج. جنتی، د. نظر پور، "مدیریت انرژی پارکینگ هوشمند خودروهای برقی در یک ریز شبکه با در نظر گرفتن اثرات برنامه پاسخگویی بار"، مجله مهندسی برق دانشگاه تبریز، جلد47، شماره2، تابستان96
[39]      Thomas M. Cover, Joy A. Thomas, “Elements of Information Theory”, 2nd edition, Wiley, September, 2006
[40]      f. h. lotfi, r. fallahnejad, “imprecise shannon’s entropy and multi attribute decision making”, January 2010