Uncertainty-Based Models for Optimal Management of Energy Hubs Considering Demand Response

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Uncertainty-Based Models for Optimal Management of Energy Hubs Considering Demand Response. / Najafi, Arsalan; Marzband, Mousa; Mohammadi-Ivatloo, Behnam; Contreras, Javier; Pourakbari-Kasmaei, Mahdi; Lehtonen, Matti; Godina, Radu.

julkaisussa: Energies, Vuosikerta 12, Nro 8, 1413, 12.04.2019.

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

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Najafi, A, Marzband, M, Mohammadi-Ivatloo, B, Contreras, J, Pourakbari-Kasmaei, M, Lehtonen, M & Godina, R 2019, 'Uncertainty-Based Models for Optimal Management of Energy Hubs Considering Demand Response', Energies, Vuosikerta. 12, Nro 8, 1413. https://doi.org/10.3390/en12081413

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Najafi, Arsalan ; Marzband, Mousa ; Mohammadi-Ivatloo, Behnam ; Contreras, Javier ; Pourakbari-Kasmaei, Mahdi ; Lehtonen, Matti ; Godina, Radu. / Uncertainty-Based Models for Optimal Management of Energy Hubs Considering Demand Response. Julkaisussa: Energies. 2019 ; Vuosikerta 12, Nro 8.

Bibtex - Lataa

@article{1a1e7c00bd9e47b5bc14632b8188633a,
title = "Uncertainty-Based Models for Optimal Management of Energy Hubs Considering Demand Response",
abstract = "Energy hub (EH) is a concept that is commonly used to describe multi-carrier energy systems. New advances in the area of energy conversion and storage have resulted in the development of EHs. The efficiency and capability of power systems can be improved by using EHs. This paper proposes an Information Gap Decision Theory (IGDT)-based model for EH management, taking into account the demand response (DR). The proposed model is applied to a semi-realistic case study with large consumers within a day ahead of the scheduling time horizon. The EH has some inputs including real-time (RT) and day-ahead (DA) electricity market prices, wind turbine generation, and natural gas network data. It also has electricity and heat demands as part of the output. The management of the EH is investigated considering the uncertainty in RT electricity market prices and wind turbine generation. The decisions are robust against uncertainties using the IGDT method. DR is added to the decision-making process in order to increase the flexibility of the decisions made. The numerical results demonstrate that considering DR in the IGDT-based EH management system changes the decision-making process. The results of the IGDT and stochastic programming model have been shown for more comprehension.",
keywords = "demand response, energy hub, information gap decision theory, stochastic programming, Energy hub, Demand response, Information gap decision theory, Stochastic programming",
author = "Arsalan Najafi and Mousa Marzband and Behnam Mohammadi-Ivatloo and Javier Contreras and Mahdi Pourakbari-Kasmaei and Matti Lehtonen and Radu Godina",
year = "2019",
month = "4",
day = "12",
doi = "10.3390/en12081413",
language = "English",
volume = "12",
journal = "Energies",
issn = "1996-1073",
publisher = "MDPI AG",
number = "8",

}

RIS - Lataa

TY - JOUR

T1 - Uncertainty-Based Models for Optimal Management of Energy Hubs Considering Demand Response

AU - Najafi, Arsalan

AU - Marzband, Mousa

AU - Mohammadi-Ivatloo, Behnam

AU - Contreras, Javier

AU - Pourakbari-Kasmaei, Mahdi

AU - Lehtonen, Matti

AU - Godina, Radu

PY - 2019/4/12

Y1 - 2019/4/12

N2 - Energy hub (EH) is a concept that is commonly used to describe multi-carrier energy systems. New advances in the area of energy conversion and storage have resulted in the development of EHs. The efficiency and capability of power systems can be improved by using EHs. This paper proposes an Information Gap Decision Theory (IGDT)-based model for EH management, taking into account the demand response (DR). The proposed model is applied to a semi-realistic case study with large consumers within a day ahead of the scheduling time horizon. The EH has some inputs including real-time (RT) and day-ahead (DA) electricity market prices, wind turbine generation, and natural gas network data. It also has electricity and heat demands as part of the output. The management of the EH is investigated considering the uncertainty in RT electricity market prices and wind turbine generation. The decisions are robust against uncertainties using the IGDT method. DR is added to the decision-making process in order to increase the flexibility of the decisions made. The numerical results demonstrate that considering DR in the IGDT-based EH management system changes the decision-making process. The results of the IGDT and stochastic programming model have been shown for more comprehension.

AB - Energy hub (EH) is a concept that is commonly used to describe multi-carrier energy systems. New advances in the area of energy conversion and storage have resulted in the development of EHs. The efficiency and capability of power systems can be improved by using EHs. This paper proposes an Information Gap Decision Theory (IGDT)-based model for EH management, taking into account the demand response (DR). The proposed model is applied to a semi-realistic case study with large consumers within a day ahead of the scheduling time horizon. The EH has some inputs including real-time (RT) and day-ahead (DA) electricity market prices, wind turbine generation, and natural gas network data. It also has electricity and heat demands as part of the output. The management of the EH is investigated considering the uncertainty in RT electricity market prices and wind turbine generation. The decisions are robust against uncertainties using the IGDT method. DR is added to the decision-making process in order to increase the flexibility of the decisions made. The numerical results demonstrate that considering DR in the IGDT-based EH management system changes the decision-making process. The results of the IGDT and stochastic programming model have been shown for more comprehension.

KW - demand response

KW - energy hub

KW - information gap decision theory

KW - stochastic programming

KW - Energy hub

KW - Demand response

KW - Information gap decision theory

KW - Stochastic programming

UR - http://www.scopus.com/inward/record.url?scp=85065464224&partnerID=8YFLogxK

U2 - 10.3390/en12081413

DO - 10.3390/en12081413

M3 - Article

VL - 12

JO - Energies

JF - Energies

SN - 1996-1073

IS - 8

M1 - 1413

ER -

ID: 33286960