Optimal operation of smart houses by a real-time rolling horizon algorithm

N.G. Paterakis, I.N. Pappi, J.P.S. Catalão, O. Erdinc

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

8 Citations (Scopus)
2 Downloads (Pure)


In this paper, a novel real-time rolling horizon optimization framework for the optimal operation of a smart household is presented. A home energy management system (HEMS) model based on mixed-integer linear programming (MILP) is developed in order to minimize the energy procurement cost considering that the household is enrolled in a dynamic pricing tariff scheme. Several assets such as a photovoltaic (PV) installation, an electric vehicle (EV) and controllable appliances are considered. Additionally, the energy from the PV and the EV can be used either to satisfy the household demand or can be sold back to the grid. The uncertainty of the PV production is estimated using time-series models and performing forecasts on a rolling basis. Also, appropriate distribution is used in order to model the uncertainty related to the EV. Besides, several parameters can be updated in real-time in order to reflect changes in demand and consider the end-user's preferences. The optimization algorithm is executed on a regular basis in order to improve the results against uncertainty.

Original languageEnglish
Title of host publication2016 IEEE Power and Energy Society General Meeting (PESGM)
Place of PublicationPiscataway
PublisherIEEE Computer Society
ISBN (Electronic)9781509041688
Publication statusPublished - 10 Nov 2016
Event2016 IEEE Power and Energy Society General Meeting (PESGM 2016) - Sheraton Boston Hotel, Boston, United States
Duration: 17 Jul 201621 Jul 2016


Conference2016 IEEE Power and Energy Society General Meeting (PESGM 2016)
Abbreviated titlePESGM 2016
CountryUnited States
Internet address


  • Demand response
  • Electric vehicle
  • Energy management systems
  • Photovoltaics
  • Real-time optimization
  • Real-time pricing
  • Rolling optimization
  • Uncertainty

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