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

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

doi:10.1109/PESGM.2016.7741507

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

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

Electrical Energy Systems

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

13 Citations (Scopus)

4 Downloads (Pure)

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
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	https://doi.org/10.1109/PESGM.2016.7741507
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
Abbreviated title	PESGM 2016
Country/Territory	United States
City	Boston
Period	17/07/16 → 21/07/16
Internet address	http://www.pes-gm.org/2016

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

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

Access to Document

10.1109/PESGM.2016.7741507

Cite this

@inproceedings{944876df933446b996e096335125e6c0,

title = "Optimal operation of smart houses by a real-time rolling horizon algorithm",

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.",

keywords = "Demand response, Electric vehicle, Energy management systems, Photovoltaics, Real-time optimization, Real-time pricing, Rolling optimization, Uncertainty",

author = "N.G. Paterakis and I.N. Pappi and J.P.S. Catal{\~a}o and O. Erdinc",

year = "2016",

month = nov,

day = "10",

doi = "10.1109/PESGM.2016.7741507",

language = "English",

pages = "1--5",

booktitle = "2016 IEEE Power and Energy Society General Meeting (PESGM)",

publisher = "IEEE Computer Society",

address = "United States",

note = "2016 IEEE Power and Energy Society General Meeting, PESGM 2016, PESGM 2016 ; Conference date: 17-07-2016 Through 21-07-2016",

url = "http://www.pes-gm.org/2016",

}

Paterakis, NG, Pappi, IN, Catalão, JPS & Erdinc, O 2016, Optimal operation of smart houses by a real-time rolling horizon algorithm. in 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, Massachusetts, United States, 17/07/16. https://doi.org/10.1109/PESGM.2016.7741507

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 - https://www.scopus.com/pages/publications/85001837143

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

T2 - 2016 IEEE Power and Energy Society General Meeting, PESGM 2016

Y2 - 17 July 2016 through 21 July 2016

ER -

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

Abstract

Conference

UN SDGs

Keywords

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