Shaping aggregated load profiles based on optimized local scheduling of home appliances


We present a new method to control an aggregated electric load profile by exploiting the flexibilities provided by residential homes. The method is based on a common energy price combined with inclining block rates, broadcasted to all households allowing them to minimize their energy provisioning cost. The distributed home energy management systems receive the price signal and use mixed integer linear programming for optimal scheduling of load, storage, and generation devices. The method provides excellent scalability as well as autonomy for home owners and avoids load synchronization effects. As proof of concept, an optimization algorithm for determining a day-ahead price is applied in two case studies. An excellent conformance between a given reference load profile and the resulting aggregated load profile of all households is demonstrated.

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  1. 1.

    rp is the subunit of the Swiss franc (100 rp \(=\) 1 CHF).


  1. 1.

    Baccino F, Massucco S, Silvestro F, Grillo S (2014) Management strategy for unbalanced lv distribution network with electric vehicles, heat pumps and domestic photovoltaic penetration. In: 2014 IEEE PES general meeting | conference exposition, pp 1–5. doi:10.1109/PESGM.2014.6939158

  2. 2.

    Borenstein S (2005) The long-run efficiency of real-time electricity pricing. Energy J 26(3), 93–116.

  3. 3.

    Chen X, Wei T, Hu S (2013) Uncertainty-aware household appliance scheduling considering dynamic electricity pricing in smart home. IEEE Trans Smart Grid 4(2):932–941. doi:10.1109/TSG.2012.2226065

    Article  Google Scholar 

  4. 4.

    Doostizadeh M., Ghasemi H (2012) A day-ahead electricity pricing model based on smart metering and demand-side management. Energy 46(1): 221–230 .

  5. 5.

    Elektroautos im Vergleich (2017) Website.

  6. 6.

    Faruqui A, Sergici S (2009) Household response to dynamic pricing: a survey of the experimental evidence. Brattle Group. doi:10.1016/

    Google Scholar 

  7. 7.

    Gyamfi S, Krumdieck S, Urmee T (2013) Residential peak electricity demand response-Highlights of some behavioural issues. Renew Sustain Energy Rev 25:71–77. doi:10.1016/j.rser.2013.04.006.

  8. 8.

    Herter K, Wayland S (2010) Residential response to critical-peak pricing of electricity: California evidence. Energy 35(4):1561–1567. doi:10.1016/

  9. 9.

    Heussen K, Koch S, Ulbig A, Andersson G (2012) Unified system-level modeling of intermittent renewable energy sources and energy storage for power system operation. IEEE Syst J 6(1):140–151. doi:10.1109/JSYST.2011.2163020

    Article  Google Scholar 

  10. 10.

    Ipakchi A, Albuyeh F (2009) Grid of the future. IEEE Power Energy Mag 7(2):52–62. doi:10.1109/MPE.2008.931384

    Article  Google Scholar 

  11. 11.

    Javaid N, Khan I, Ullah MN, Mahmood A, Farooq MU (2013) A survey of home energy management systems in future smart grid communications. In: 2013 eighth international conference on broadband and wireless computing, communication and applications, pp 459–464. doi:10.1109/BWCCA.2013.80

  12. 12.

    Mattlet B, Maun JC (2016) Assessing the benefits for the distribution system of a scheduling of flexible residential loads. In: 2016 IEEE international energy conference (ENERGYCON), pp 1–6. doi:10.1109/ENERGYCON.2016.7513932

  13. 13.

    Milelli A (2012) Wärmepumpe: Worauf es ankommt. Online article.

  14. 14.

    Mohsenian-Rad AH, Leon-Garcia A (2010) Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE Trans Smart Grid 1(2):120–133. doi:10.1109/TSG.2010.2055903.

  15. 15.

    Nipkow J (2013) Elektrische Wassererwärmung in der Schweiz—Statistische Daten, Abschtzung des Elektrizitätsverbrauchs, Ersatzmechanismen, Potenzial Wärmepumpenboiler. Tech rep, Swiss Federal Office of Energy SFOE.

  16. 16.

    Nipkow J (2013) Typischer Haushalt-Stromverbrauch. Tech rep, Swiss Federal Office of Energy SFOE.

  17. 17.

    Paterakis NG, Pappi IN, Catalo JPS, Erdinc O (2015) Optimal operational and economical coordination strategy for a smart neighborhood. In: 2015 IEEE eindhoven powertech, pp 1–6. doi:10.1109/PTC.2015.7232511

  18. 18.

    Paterakis NG, Erdin O, Bakirtzis AG, Catalo JPS (2015) Optimal household appliances scheduling under day-ahead pricing and load-shaping demand response strategies. IEEE Trans Ind Inform 11(6):1509–1519. doi:10.1109/TII.2015.2438534

  19. 19.

    Pendlermobilität in der Schweiz. Tech rep, Swiss Federal Statistical Office (2016).

  20. 20.

    Qayyum FA, Naeem M, Khwaja AS, Anpalagan A (2015) Appliance scheduling optimization in smart home networks comprising of smart appliances and a photovoltaic panel. In: 2015 IEEE electrical power and energy conference (EPEC), pp 457–462. doi:10.1109/EPEC.2015.7379994

  21. 21.

    Reiss PC, White MW (2005) Household electricity demand. Revisited. Rev Exonomic Stud 72(3):853–883. doi:10.1111/0034-6527.00354.

  22. 22.

    Schulz N, Bichsel J, Wache H, Farooq AA, Hoffmann C, Lammel B, Mettler F (2015) Smart stability–market-economic interaction of smart hhome for improved power network stability. In: Proceedings of international conference CISBAT 2015—future buildings and districts—sustainability from nano to urban scale, pp 487–492. doi:10.5075/epfl-cisbat2015-487-492

  23. 23.

    Soland M, Loosli S, Koch J, Christ O (2017) Acceptance among residential electricity consumers regarding scenarios of a transformed energy system in switzerland–a focus group study. Energy Effic. doi:10.1007/s12053-017-9548-x

    Google Scholar 

  24. 24.

    Solardachrechner (2017) Onlinetool.

  25. 25.

    Standard load profiles: German association of energy and water industries (BDEW) (2011).

  26. 26.

    Yao L, Damiran Z, Lim WH (2017) Energy management optimization scheme for smart home considering different types of appliances. In: 2017 IEEE international conference on environment and electrical engineering and 2017 IEEE industrial and commercial power systems Europe (EEEIC / I CPS Europe), pp 1–6. doi:10.1109/EEEIC.2017.7977565

  27. 27.

    Zhao Z, Lee WC, Shin Y, Song KB (2013) An optimal power scheduling method for demand response in home energy management system. IEEE Trans Smart Grid 4(3):1391–1400. doi:10.1109/TSG.2013.2251018

    Article  Google Scholar 

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This work has been supported by the Strategic Initiative ‘Energy Chance’ funded by the University of Applied Sciences Northwestern Switzerland.

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Correspondence to Christoph Hunziker.

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Hunziker, C., Schulz, N. & Wache, H. Shaping aggregated load profiles based on optimized local scheduling of home appliances. Comput Sci Res Dev 33, 61–70 (2018).

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  • HEMS
  • Real-time price
  • Inclining block rates
  • Demand response
  • Distributed load management
  • MILP