Skip to main content

Intelligent Management of Residential Load

  • Chapter
  • First Online:
Power Systems Research and Operation

Abstract

The chapter presents research into application of residential load management systems for balancing separate power system segments operation at distribution level. Power consumption is controlled through implementation of intelligent load management systems for separate large consumers that allow interruptions in power supply and changes in consumed power. Intellectualization of the load management system under consideration is performed on the basis of a proposed end-user’s load management scheme; the basic characteristics and type of the possible resource for residential demand control are specified. Particular features of connecting the considered large power consumers to the electrical grid are shown, algorithms of the obtained controlled load resource management described. The general system effect is assessed for the increased power system balancing capabilities resulted from implementation of the demand control based solutions at the ancillary services market.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover 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. Distributed energy resources and electricity balancing: visions for future organization. Retrieved from https://cadmus.eui.eu/bitstream/handle/1814/74246/RSC_RPR_2022_2_FSR.pdf?sequence=1. (2022)

  2. Electrical Distribution Network. Retrieved from https://download.schneider-electric.com/files?p_Doc_Ref=998-21588309&mkt_tok=MTc4LUdZRC02NjgAAAGI7AwLNT2HZRaE07rcj_hiAe_ZqHpUkVF4U1PVmeNJdSNhPdHyS2CglmuIXj8SP3TCiT7L2JNpFlIJoiNmqh_aRUf1C2xnUjSIfUFMKa3uYqf32PLgzZpc. (2021)

  3. Balancing the energy system. Technological challenges and innovative solutions. Retrieved from https://enerhodzherela.com.ua/analityka [in Ukrainian]

  4. Digital energy. Why it is the future of energy markets. Retrieved from https://enerhodzherela.com.ua/analityka [in Ukrainian]

  5. Cummulative Sum of Energy Storage Installations by Year. Retrieved from https://sandia.gov/ess-ssl/gesdb/public/statistics.html (2021)

  6. U.S. DOE Energy Storage Handbook. Retrieved from https://www.sandia.gov/ess/publications/doe-oe-resources/eshb (2021)

  7. South Australia’s Big Battery. Retrieved from https://hornsdalepowerreserve.com.au

  8. Hoschle, H., Dupont, B., Vingerhoets, P., Belmans, R.: Networked business model for dynamic pricing in the electricity market. In: International Conference on the European Energy Market, EEM 6607387 (2013)

    Google Scholar 

  9. Lazurenko, O., Cherkashyna, H.: Method of electricity supply to household consumers. Utility model patent of Ukraine UA №108869, 10.08.2016, Bul. № 15 (Ukrainian)

    Google Scholar 

  10. Lazurenko, O., Lysenko, L., Cherkashyna, H., Shokarov, D.: On the principles of restoring Ukraine's electricity system. Power Eng. Econ. Tech. Ecol. (4), 70–76 (2022) (Ukrainian). https://doi.org/10.20535/1813-5420.4.2022.273415

  11. Oprea, S.V., Bâra, A., Ifrim, G.: Flattening the electricity consumption peak and reducing the electricity payment for residential consumers in the context of smart grid by means of shifting optimization algorithm. Comput. Ind. Eng. 122, 125–139 (2018). https://doi.org/10.1016/j.cie.2018.05.053

  12. Ifrim, G., Oprea, S.V., Bara, A.: Peak shaving algorithms for residential consumers: a comparative study. In: 24th International Conference on System Theory, Control and Computing, ICSTCC 2020-Proceedings, pp. 37–42 (2020). https://doi.org/10.1109/ICSTCC50638.2020.9259750

  13. Vanthournout, K., Dupont B., Foubert; W., Stuckens, C., Claessens, S.: An automated residential demand response pilot experiment, based on day-ahead dynamic pricing. Appl. Energy 155, 195–203 (2015). https://doi.org/10.1016/j.apenergy.2015.05.100

  14. Shariatzadeh, F., Mandal, P., Srivastava, A.K.: Demand response for sustainable energy systems: a review, application and implementation strategy. Renew. Sustain. Energy Rev. 45, 343–350 (2015). https://doi.org/10.1016/j.rser.2015.01.062

  15. Pourramezan, A., Samadi, M.: A novel approach for incorporating incentive-based and price-based demand response programs in long-term generation investment planning. Int. J. Electr. Power Energy Syst. 142(Part B) (2022). https://doi.org/10.1016/j.ijepes.2022.108315

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Halyna Cherkashyna .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Lazurenкo, O., Lysenko, L., Makhotilo, K., Cherkashyna, H., Cherneshchuk, I. (2024). Intelligent Management of Residential Load. In: Kyrylenko, O., Denysiuk, S., Strzelecki, R., Blinov, I., Zaitsev, I., Zaporozhets, A. (eds) Power Systems Research and Operation. Studies in Systems, Decision and Control, vol 512. Springer, Cham. https://doi.org/10.1007/978-3-031-44772-3_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-44772-3_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-44771-6

  • Online ISBN: 978-3-031-44772-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics