Skip to main content
SpringerLink
Log in

Decomposition of Aggregate Electricity Demand into the Seasonal-Thermal Components for Demand-Side Management Applications in “Smart Grids”

  • Conference paper
  • First Online:
Data Analytics for Renewable Energy Integration (DARE 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10097))

  • 1139 Accesses

Abstract

Aggregate active and reactive power demands measured at 84 Scottish medium-voltage (MV) buses are used in this paper for the correlation and regression analysis, aimed at demand profiling and load decomposition. Demand profiles are presented with respect to the long-term seasonal variations, medium-term weekly and short-term diurnal cycles, allowing for the characterisation and presentation of load behaviour at different time-scales. The linear relationships between active and reactive power demands, temperature and power factor variations are quantified using regression analysis, on a per-hour of the day basis, as well as using a sliding-window regression approach for estimating relative coefficients within a seasonal moving window. The paper presents three different approaches for the decomposition of aggregate network demand into the temperature-dependent loads (i.e. thermal heating and cooling loads) and temperature-independent loads, providing important basic information for the application of the “smart grid” functionalities, such as demand-side management, or balancing of variable energy flows from renewable generation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Paisios, A., Djokic, S.: Load decomposition and profiling for “smart grid” demand-side management applications. In: ECML PKDD 2013 Workshop on Data Analytics for Renewable Energy Integration (DARE 2013), Prague, Czech Republic (2013)

    Google Scholar 

  2. Hor, S.L., Watson, S., Majithia, S.: Analyzing the impact of weather variables on monthly electricity demand. IEEE Trans. Power Syst. 20, 2078–2085 (2005)

    Article  Google Scholar 

  3. Noureddine, A.H., Alouani, A.T., Chandrasekaran, A.: A new technique for short-term residential electric load forecasting including weather and lifestyle influences. In: Proceedings of the 35th Midwest Symposium on Circuits and Systems, UK, vol. 2, pp. 1419–1427 (1992)

    Google Scholar 

  4. GPG310 - Degree Days for Energy Management - A Practical Introduction, Government Action Energy Program, New Action Energy, Carbon Trust, London (2004)

    Google Scholar 

  5. Rahman, S., Hazim, O.: A generalized knowledge-based short-term load-forecasting technique. IEEE Trans. Power Syst. 8, 508–514 (1993). IEEE Press

    Article  Google Scholar 

  6. Lane, I.E., Beute, N.: A model of the domestic hot water load. IEEE Trans. Power Syst. 11, 1850–1855 (1996)

    Article  Google Scholar 

  7. Xu, Z., Ostergaard, J., Togeby, M., Marcus-Moller, C.: Design and modeling of thermostatically controlled loads as frequency controlled reserve. In: Power Engineering Society General Meeting, pp. 1–6 (2007)

    Google Scholar 

  8. Park, D.C., El-Sharkawi, M.A., Marks, R.J., Atlas, L.E., Damborg, M.J.: Electric load forecasting using an artificial neural network. IEEE Trans. Power Syst. 6(2), 442–449 (1991)

    Article  Google Scholar 

  9. Rahman, S., Hazim, O.: A generalized knowledge-based short-term load-forecasting technique. IEEE Trans. Power Syst. 8, 508–514 (1993). IEEE Press

    Article  Google Scholar 

  10. Al-Hamadi, H.M., Soliman, S.A.: Long-term/mid-term electric load forecasting based on short-term correlation and annual growth. Electr. Power Syst. Res. 74, 353–361 (2005)

    Article  Google Scholar 

  11. Al-Alawi, S.M., Islam, S.M.: Principles of electricity demand forecasting - part I methodologies. Power Eng. J. 10, 139–143 (1996)

    Article  Google Scholar 

  12. Hyndman, R.J., Fan, S.: Density forecasting for long-term peak electricity demand. IEEE Trans. Power Syst. 25, 1142–1153 (2010). IEEE Press

    Article  Google Scholar 

  13. Laughman, C., Lee, K., Cox, R., Shaw, S., Leeb, S., Norford, L., Armstrong, P.: Power signature analysis. IEEE Power Energy Mag. 1(2), 56–63 (2003)

    Article  Google Scholar 

  14. Liang, J., Ng, S., Kendall, G., Cheng, J.: Load signature study-part I: basic concept, structure, and methodology. IEEE Trans. Power Deliv. 25(2), 551–560 (2010)

    Article  Google Scholar 

  15. Capasso, A., Grattieri, W., Lamedica, R., Prudenzi, A.: A bottom-up approach to residential load modeling. IEEE Trans. Power Syst. 9(2), 957–964 (1994)

    Article  Google Scholar 

  16. Paatero, J.V., Lund, P.D.: A model for generating household electricity load profiles. Int. J. Energy Res. 30(5), 273–290 (2006)

    Article  Google Scholar 

  17. Stokes, M., Rylatt, M., Lomas, K.: A simple model of domestic lighting demand. Energy Build. 36(2), 103–116 (2004)

    Article  Google Scholar 

  18. Collin, A.J., Tsagarakis, G., Kiprakis, A.E., McLaughlin, S.: Multi-scale electrical load modelling for demand-side management. In: 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe) (2012)

    Google Scholar 

  19. Collin, A.J., Hernando-Gil, I., Acosta, J.L., Djokic, S.Z.: An 11 KV steady state residential aggregate load model. Part 1: aggregation methodology. In: IEEE Trondheim PowerTech (2011)

    Google Scholar 

  20. Xu, Y., Milanovic, J.V.: Artificial-intelligence-based methodology for load disaggregation at bulk supply point. IEEE Trans. Power Syst. 30(2), 795–803 (2014)

    Article  Google Scholar 

  21. Hobby, J.D., Tucci, G.H.: Analysis of the residential, commercial and industrial electricity consumption. In: 2011 IEEE PES Innovative Smart Grid Technologies Asia (ISGT), Perth, WA, pp. 1–7 (2011)

    Google Scholar 

  22. Paisios, A., Ferguson, A., Djokic, S.: Solar analemma for assessing variations in electricity demands at MV buses. In: Med Power 2016 Conference (2016)

    Google Scholar 

  23. Buda, A., Jarynowski, A.: Life-time of Correlations and its Applications, vol. 1 (2010)

    Google Scholar 

  24. Steel, R.G.D., Torrie, J.H.: Principles and Procedures of Statistics, New York, pp. 187–287 (1960)

    Google Scholar 

  25. Energy Consumption in the UK, Department of Energy and Climate Change, UK Government, London (2012)

    Google Scholar 

  26. Pout, C., MacKenzie, F., Olloqui, E.: The impact of changing energy use patterns in buildings on peak electricity demand in the UK, building research establishment. Technical report 243 752 (2008)

    Google Scholar 

  27. Hayes, B.P., H.-Gil, I., Collin, A.J., Harrison, G., Djokic, S.Z.: Optimal power flow for maximizing network benefits from demand-side management. IEEE Trans. Power Syst. 29(4), 1739–1747 (2014)

    Article  Google Scholar 

  28. Djokic, S.Z., Papic, I.: Smart grid implementation of demand side management and micro-generation. Int. J. Energy Optim. Eng. 1(2), 1–19 (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The University of Edinburgh, Edinburgh, Scotland, UK

    Andreas Paisios & Sasa Djokic

Authors
  1. Andreas Paisios
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sasa Djokic
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sasa Djokic .

Editor information

Editors and Affiliations

  1. Masdar Institute of Science and Technology , Abu Dhabi, United Arab Emirates

    Wei Lee Woon

  2. Electrical Eng. & Computer Science, Masdar Inst. of Science &Technology Electrical Eng. & Computer Science, Abu Dhabi, United Arab Emirates

    Zeyar Aung

  3. Informatik, Universität Oldenburg Informatik, Oldenburg, Germany

    Oliver Kramer

  4. Massachusetts Institute of Technology , Cambridge, Massachusetts, USA

    Stuart Madnick

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Paisios, A., Djokic, S. (2017). Decomposition of Aggregate Electricity Demand into the Seasonal-Thermal Components for Demand-Side Management Applications in “Smart Grids”. In: Woon, W., Aung, Z., Kramer, O., Madnick, S. (eds) Data Analytics for Renewable Energy Integration. DARE 2016. Lecture Notes in Computer Science(), vol 10097. Springer, Cham. https://doi.org/10.1007/978-3-319-50947-1_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-50947-1_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50946-4

  • Online ISBN: 978-3-319-50947-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation