### Abstract

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 language | English |
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Title of host publication | 2016 IEEE Power and Energy Society General Meeting (PESGM) |

Place of Publication | Piscataway |

Publisher | IEEE Computer Society |

Pages | 1-5 |

ISBN (Electronic) | 9781509041688 |

DOIs | |

Publication status | Published - 10 Nov 2016 |

Event | 2016 IEEE Power and Energy Society General Meeting (PESGM 2016) - Sheraton Boston Hotel, Boston, United States Duration: 17 Jul 2016 → 21 Jul 2016 http://www.pes-gm.org/2016 |

### Conference

Conference | 2016 IEEE Power and Energy Society General Meeting (PESGM 2016) |
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Abbreviated title | PESGM 2016 |

Country | United States |

City | Boston |

Period | 17/07/16 → 21/07/16 |

Internet address |

### Fingerprint

### Keywords

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

### Cite this

*2016 IEEE Power and Energy Society General Meeting (PESGM)*(pp. 1-5). Piscataway: IEEE Computer Society. https://doi.org/10.1109/PESGM.2016.7741507

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*2016 IEEE Power and Energy Society General Meeting (PESGM) .*IEEE Computer Society, Piscataway, pp. 1-5, 2016 IEEE Power and Energy Society General Meeting (PESGM 2016), Boston, United States, 17/07/16. https://doi.org/10.1109/PESGM.2016.7741507

**Optimal operation of smart houses by a real-time rolling horizon algorithm.** / Paterakis, N.G.; Pappi, I.N.; Catalão, J.P.S.; Erdinc, O.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Optimal operation of smart houses by a real-time rolling horizon algorithm

AU - Paterakis, N.G.

AU - Pappi, I.N.

AU - Catalão, J.P.S.

AU - Erdinc, O.

PY - 2016/11/10

Y1 - 2016/11/10

N2 - 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.

AB - 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.

KW - Demand response

KW - Electric vehicle

KW - Energy management systems

KW - Photovoltaics

KW - Real-time optimization

KW - Real-time pricing

KW - Rolling optimization

KW - Uncertainty

UR - http://www.scopus.com/inward/record.url?scp=85001837143&partnerID=8YFLogxK

U2 - 10.1109/PESGM.2016.7741507

DO - 10.1109/PESGM.2016.7741507

M3 - Conference contribution

AN - SCOPUS:85001837143

SP - 1

EP - 5

BT - 2016 IEEE Power and Energy Society General Meeting (PESGM)

PB - IEEE Computer Society

CY - Piscataway

ER -