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Smart Management of PEV Charging Enhanced by PEV Load Forecasting

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Plug In Electric Vehicles in Smart Grids

Part of the book series: Power Systems ((POWSYS))

Abstract

According to the U.K. Department for Transport, the 97 % of transport energy consumption comes from the usage of oil. Therefore, a fuel diversification is needed to improve the energy security, and plug-in electric vehicles (PEVs) seem promising in giving an alternative solution. However, PEV owners need electric power from the grid in order to recharge the batteries of their vehicles. PEV charging load is a new type of demand, influenced by additional factors such as travel and driving patterns. Average travel distance within a day, the connection and disconnection time and the PEV’s power consumption will directly affect the daily load curve. This chapter proposes a decentralized control algorithm to manage the PEV charging requests. The aim of the control algorithm is to achieve a valley-filling effect on the demand curve, avoiding a potential increase in the peak demand. The proposed model includes an algorithm for PEV short term load forecasting. This forecast contributes to the effectiveness of the control model. Through different case studies, the performance of the proposed model is evaluated and the value of the PEV load forecasting as part of the PEV load management process is illustrated.

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References

  1. Department for Transport (2011) Transport energy and environment statistics. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/8947/energy-2011.pdf. Accessed 12 July 2014

  2. Cipcigan LM, Wells P, Nieuwenhuis P, Davies H, Whitmarsh L, Papadopoulos P (2012) Electricity as a transportation fuel: bridging the gaps in the electric vehicles value chain. Paper presented at the EVVC-2012 European electric vehicle congress, Brussels, 19–22 November 2012

    Google Scholar 

  3. Papadopoulos P, Skarvelis-Kazakos S, Grau I, Cipcigan LM, Jenkins N (2010) Predicting electric vehicle impacts on residential distribution networks with distributed generation. Paper presented at the IEEE vehicle power and propulsion conference, Lille, France, 1–3 September 2010

    Google Scholar 

  4. Papadopoulos P, Akizu O, Cipcigan LM, Jenkins N, Zabala E (2011) Electricity demand with electric cars in 2030: comparing Great Britain and Spain. Proc Inst Mech Eng Part A: J Power Energy (PIA) 225(A):551–566

    Google Scholar 

  5. Amin SM, Wollenberg BF (2005) Toward a smart grid: power delivery for the 21st century. IEEE Power Energy Mag 3(5):34–41

    Article  Google Scholar 

  6. Morrow K, Karner D, Francfort J (2008) Plug-in hybrid electric vehicle charging infrastructure review. US Department of Energy. http://avt.inl.gov/pdf/phev/phevInfrastructureReport08.pdf. Accessed 15 July 2014

  7. Huang Y, Liu J, Shen X, Dai T (2013) The interaction between the large-scale EVs and the power grid. Smart Grid Renew Energy 4(2):137–143. doi:10.4236/sgre.2013.42017

    Article  Google Scholar 

  8. Eichinger F, Pathmaperuma D, Vogt H, Muller E (2013) Data analysis challenges in the future energy domain. In: Yu T, Chawla N, Simoff S (eds) Computational intelligent data analysis for sustainable development. Chapman and Hall/CRC, Boca Raton, pp 182–233

    Google Scholar 

  9. Tugberk Isyapar M (2013) Classification of electricity customers based on real consumption values using data mining and machine learning techniques and its corresponding applications. Ph.D. dissertation, Middle East Technical University

    Google Scholar 

  10. Schuller A, Rieger F (2013) Assessing the economic potential of electric vehicles to provide ancillary services: the case of Germany. Zeitschrift für Energiewirtschaft 37(3):177–194

    Article  Google Scholar 

  11. Tomic J, Kempton W (2007) Using fleets of electric-drive vehicles for grid support. J Power Sources 168(2):459–468

    Article  Google Scholar 

  12. Lopes JAP, Soares FJ, Almeida PMR (2011) Integration of electric vehicles in the electric power system. Proc IEEE 99(1):168–183

    Article  Google Scholar 

  13. Kempton W, Letendre SE (1997) Electric vehicles as a new power source for electric utilities. Transp Res Part D: Transp Environ 2(3):157–175

    Article  Google Scholar 

  14. Karfopoulos E, Marmaras C, Hatziargyriou N (2012) Charging control model for EV supplier aggregator. Paper presented at the 3rd IEEE PES international conference and exhibition on innovative smart grid technologies (ISGT Europe), Berlin, 14–17 October 2012

    Google Scholar 

  15. Rodrigue JP, Comtois C, Slack B (2009) The geography of transport systems. Routledge, London and New York

    Google Scholar 

  16. DuBois & King, Inc. Vermont Energy Investment Corporation (2013) V trans electric vehicle fueling infrastructure plan. http://www.veic.org/docs/Transportation/201307_VTrans_EV_Charging_Plan_Final_Report_web.pdf. Accessed 21 July 2014

  17. Pitt BD (2000) Applications of data mining techniques to electric load profiling. Ph.D. dissertation, Institute of Science and Technology, University of Manchester

    Google Scholar 

  18. Dutta S (2012) Data mining and graph theory focused solutions to smart grid challenges. Ph.D. dissertation, University of Illinois

    Google Scholar 

  19. Selbas R, Sencan A, Kucuksille EU (2011) Data mining method for energy system applications. In: Funatsu K (ed) Knowledge-oriented applications in data mining. InTech, Vienna, p 147–166

    Google Scholar 

  20. Han J, Kamber M, Pei J (2012) Data mining: concepts and techniques. Morgan Kaufmann Publishers, USA

    Google Scholar 

  21. Xydas E, Marmaras C, Cipcigan LM, Hassan AS, Jenkins N (2013) Forecasting Electric Vehicles Charging Demand Using Support Vector Machines. Paper presented at the 48th universities’ power engineering conference (UPEC), Dublin, 2–5 September 2013

    Google Scholar 

  22. Xydas E, Marmaras C, Cipcigan LM, Hassan AS, Jenkins N (2013) Electric vehicle load forecasting using data mining methods. Paper presented at the 4th hybrid and electric vehicle IET conference (HEVC), London, 6–7 November 2013

    Google Scholar 

  23. Boser BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers. In: Haussler D (ed) Proceedings of the 5th annual workshop on computational learning theory (COLT’92), Pittsburgh, USA, 1992

    Google Scholar 

  24. Scholkopf B, Sung K, Burges CJC, Girosi F, Niyogi P, Poggio T, Vapnik V (1997) Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Trans Signal Process 45(11):2758–2765

    Article  Google Scholar 

  25. Osuna E, Freund R, Girosi F (1996) Support vector machines: training and applications. http://dspace.mit.edu/bitstream/handle/1721.1/7290/AIM-1602.pdf?sequence=2. Accessed 12 July 2014

  26. Smola A, Scholkopf B (2001) A tutorial on support vector regression. Stat Comput 14(3):199–222

    Article  MathSciNet  Google Scholar 

  27. Elattar EE, Goulermas J, Wu QH (2010) Electric load forecasting based on locally weighted support vector regression. IEEE Trans Syst Man Cybern Part C Appl Rev 40(4):438–447

    Article  Google Scholar 

  28. Varewyck M, Martens JP (2011) A practical approach to model selection for support vector machines with a Gaussian kernel. IEEE Trans Syst Man Cybern Part B Cybern 41(2):330–340

    Article  Google Scholar 

  29. Ikeda K (2006) Effects of kernel function on Nu support vector machines in extreme cases. IEEE Trans Neural Netw 17(1):1–9

    Article  Google Scholar 

  30. Cherkassky V, Ma Y (2004) Practical selection of SVM parameters and noise estimation for SVM regression. Neural Netw 17(1):113–126

    Article  MATH  Google Scholar 

  31. Muller KR, Mika S, Ratsch G, Tsuda K, Scholkopf B (2001) An introduction to kernel-based learning algorithm. IEEE Trans Neural Netw 12(2):181–201

    Article  Google Scholar 

  32. Wu KP, Wang SD (2009) Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space. Pattern Recogn 42(5):710–717

    Article  MATH  Google Scholar 

  33. Hsu CW, Chang CC, Lin CJ (2003) A practical guide to support vector classification. http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf. Accessed 12 July 2014

  34. Ben-Hur A, Weston J (2010) A user’s guide to support vector machines. In: Carugo O, Eisenhaber F (eds) Data mining techniques for the life sciences. Humana Press, New York, pp 223–239

    Chapter  Google Scholar 

  35. Kecman V (2001) Learning and soft computing: support vector machines, neural networks and fuzzy logic models. MIT Press, Cambridge

    Google Scholar 

  36. Suykens J, Horvath G, Basu S, Micchello C, Vandewalle J (eds) (2003) Advances in learning theory: methods, models and applications. IOS Press, Amsterdam

    Google Scholar 

  37. Shao S, Pipattanasomporn M, Rahman S (2012) Grid integration of electric vehicles and demand response with customer choice. IEEE Trans Smart Grid 3(1):543–550

    Article  Google Scholar 

  38. Jin C, Tang J, Ghosh P (2013) Optimizing electric vehicle charging: a customer’s perspective. IEEE Trans Veh Technol 62(7):2919–2927

    Article  Google Scholar 

  39. Deilami S, Masoum AS, Moses PS, Masoum MAS (2011) Real-time coordination of plug-in electric vehicle charging in smart grids to minimize power losses and improve voltage profile. IEEE Trans Smart Grid 2(3):456–467

    Article  Google Scholar 

  40. Fan Z (2012) A distributed demand response algorithm and its application to PHEV charging in smart grids. IEEE Trans Smart Grid 3(3):1280–1290

    Article  Google Scholar 

  41. Ota Y, Taniguchi H, Nakajima T, Liyanage KM, Baba J, Yokoyama A (2012) Autonomous distributed V2G (Vehicle-to-Grid) satisfying scheduled charging. IEEE Trans Smart Grid 3(1):559–564

    Article  Google Scholar 

  42. Ma Z, Callaway DS, Hiskens IA (2013) Decentralized charging control of large populations of plug-in electric vehicles. IEEE Trans Control Syst Technol 21(1):67–78

    Article  Google Scholar 

  43. Qi W, Xu Z, Max Shen ZJ, Hu Z, Song Y (2014) Hierarchical coordinated control of plug-in electric vehicles charging in multifamily dwellings. IEEE Trans Smart Grid 5(3):1465–1474

    Article  Google Scholar 

  44. Cao Y, Tang S, Li C, Zhang P, Tan Y, Zhang Z, Li J (2012) An optimized EV charging model considering TOU price and SOC curve. IEEE Trans Smart Grid 3(1):388–393

    Article  Google Scholar 

  45. Xi X, Sioshansi R (2014) Using price-based signals to control plug-in electric vehicle fleet charging. IEEE Trans Smart Grid 5(3):1451–1464

    Article  Google Scholar 

  46. Karfopoulos E, Hatziargyriou N (2013) A multi-agent system for controlled charging of a large population of electric vehicles. IEEE Trans Power Syst 28(2):1196–1204

    Article  Google Scholar 

  47. Gan L, Topcu U, Low SH (2013) Optimal decentralized protocol for electric vehicle charging. IEEE Trans Power Syst 28(2):940–951

    Article  Google Scholar 

  48. Soares J, Ramos S, Vale Z, Morais H, Faria P (2012) Data mining techniques contributions to support electrical vehicle demand response. Paper presented at the 2012 IEEE PES transmission and distribution conference and exposition, Orlando, USA, 7–10 May 2012

    Google Scholar 

  49. Ingram S, Probert S, Jackson K (2003) The impact of small scale embedded generation on the operating parameters of distribution networks. http://webarchive.nationalarchives.gov.uk/20100919181607/www.ensg.gov.uk/assets/22_01_2004_phase1b_report_v10b_web_site_final.pdf. Accessed 12 July 2014

  50. Element Energy Limited (2013) Pathways to high penetration of electric vehicles. http://www.theccc.org.uk/wp-content/uploads/2013/12/CCC-EV-pathways_FINAL-REPORT_17-12-13-Final.pdf. Accessed 12 July 2014

  51. Electricity User Load Profiles by Profile Class (1997) UK Energy Research Centre, Energy Data Centre. http://data.ukedc.rl.ac.uk/browse/edc/Electricity/LoadProfile/data. Accessed 14 July 2014

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Cardiff University, Queen’s Building, The Parade, Cardiff, CF24 3AA, Wales, UK

    E. Xydas, C. Marmaras, L. M. Cipcigan & N. Jenkins

Authors
  1. E. Xydas
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  2. C. Marmaras
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  3. L. M. Cipcigan
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  4. N. Jenkins
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Corresponding author

Correspondence to E. Xydas .

Editor information

Editors and Affiliations

  1. Electrical and Computer Engineering, Curtin University, Perth, West Australia, Australia

    Sumedha Rajakaruna

  2. Electrical and Computer Engineering, Curtin University, Perth, West Australia, Australia

    Farhad Shahnia

  3. Electrical and Computer Engineering, Curtin University, Perth, West Australia, Australia

    Arindam Ghosh

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Xydas, E., Marmaras, C., Cipcigan, L.M., Jenkins, N. (2015). Smart Management of PEV Charging Enhanced by PEV Load Forecasting. In: Rajakaruna, S., Shahnia, F., Ghosh, A. (eds) Plug In Electric Vehicles in Smart Grids. Power Systems. Springer, Singapore. https://doi.org/10.1007/978-981-287-317-0_5

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  • DOI: https://doi.org/10.1007/978-981-287-317-0_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-287-316-3

  • Online ISBN: 978-981-287-317-0

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