Information Gap Decision Theory for Scheduling of Electricity-Gas Systems in the Presence of Demand Response

Amir  Talebi; Amir  Mirzapour-Kamanaj; Masoud  Agabalaye-Rahvar; Behnam  Mohammadi-Ivatloo; Kazem Zare; Amjad Anvari-Moghaddam

doi:10.1109/EEEIC/ICPSEurope51590.2021.9584543

Information Gap Decision Theory for Scheduling of Electricity-Gas Systems in the Presence of Demand Response

Amir Talebi , Amir Mirzapour-Kamanaj, Masoud Agabalaye-Rahvar, Behnam Mohammadi-Ivatloo, Kazem Zare, Amjad Anvari-Moghaddam^*

^*Kontaktforfatter

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

1 Citationer (Scopus)

72 Downloads (Pure)

Abstract

High-level integration of wind energy in power networks has raised the need for flexible units. Gas-fuel generators (GFG) with fast startup/shutdown and high ramp rate capability can provide the required flexibility in the operation of wind energy. However, the operation of GFGs can be affected by the limitations of the fuel transmission system. Demand response programs can decrease the effect of fuel transmission system restrictions in the operation of GFGs and as well as, increase the integration of wind energy into the electricity network. In this work, a scheduling model for electricity and gas networks considering demand response programs is presented. Uncertainties pertain to wind energy and demand response program are addressed in this model. Moreover, power to gas technology is used to prevent wind curtailment. This scheduling model is based on the information gap decision theory (IGDT) that can assess the level of risk pertains to uncertainties. The proposed framework has been simulated on two different networks to represent the effectiveness of the model.

Originalsprog	Engelsk
Titel	2021 IEEE International Conference on Environment and Electrical Engineering : EEEIC 2021
Antal sider	6
Forlag	IEEE Press
Publikationsdato	2021
Sider	1-6
ISBN (Trykt)	978-1-6654-3614-4
ISBN (Elektronisk)	978-1-6654-3613-7
DOI	https://doi.org/10.1109/EEEIC/ICPSEurope51590.2021.9584543
Status	Udgivet - 2021
Begivenhed	2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) - Bari, Italien Varighed: 7 sep. 2021 → 10 sep. 2021

Konference

Konference	2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)
Land/Område	Italien
By	Bari
Periode	07/09/2021 → 10/09/2021

Adgang til dokumentet

10.1109/EEEIC/ICPSEurope51590.2021.9584543

revisedversionAccepteret manuskript, 1,04 MB

AUB Link

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HeatReFlex: Green and Flexible District Heating/Cooling
Anvari-Moghaddam, A., Guerrero, J. M., Nami, H. & Mohammadiivatloo, B.
Danida Fellowship Centre
01/05/2019 → 30/04/2022
Projekter: Projekt › Forskning

Integrated Energy Systems Laboratory
AAU Energi
Facilitet: Laboratorie

Citationsformater

Talebi , A., Mirzapour-Kamanaj, A., Agabalaye-Rahvar, M., Mohammadi-Ivatloo, B., Zare, K., & Anvari-Moghaddam, A. (2021). Information Gap Decision Theory for Scheduling of Electricity-Gas Systems in the Presence of Demand Response. I 2021 IEEE International Conference on Environment and Electrical Engineering : EEEIC 2021 (s. 1-6). IEEE Press. https://doi.org/10.1109/EEEIC/ICPSEurope51590.2021.9584543

@inproceedings{f650a9e6d9cb42a79f6503be60aaf227,

title = "Information Gap Decision Theory for Scheduling of Electricity-Gas Systems in the Presence of Demand Response",

abstract = "High-level integration of wind energy in power networks has raised the need for flexible units. Gas-fuel generators (GFG) with fast startup/shutdown and high ramp rate capability can provide the required flexibility in the operation of wind energy. However, the operation of GFGs can be affected by the limitations of the fuel transmission system. Demand response programs can decrease the effect of fuel transmission system restrictions in the operation of GFGs and as well as, increase the integration of wind energy into the electricity network. In this work, a scheduling model for electricity and gas networks considering demand response programs is presented. Uncertainties pertain to wind energy and demand response program are addressed in this model. Moreover, power to gas technology is used to prevent wind curtailment. This scheduling model is based on the information gap decision theory (IGDT) that can assess the level of risk pertains to uncertainties. The proposed framework has been simulated on two different networks to represent the effectiveness of the model.",

keywords = "electricity and gas networks, demand response, Power to Gas, Information gap decision theory (IGDT)",

author = "Amir Talebi and Amir Mirzapour-Kamanaj and Masoud Agabalaye-Rahvar and Behnam Mohammadi-Ivatloo and Kazem Zare and Amjad Anvari-Moghaddam",

year = "2021",

doi = "10.1109/EEEIC/ICPSEurope51590.2021.9584543",

language = "English",

isbn = "978-1-6654-3614-4",

pages = "1--6",

booktitle = "2021 IEEE International Conference on Environment and Electrical Engineering",

publisher = "IEEE Press",

note = "2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) ; Conference date: 07-09-2021 Through 10-09-2021",

}

Talebi , A, Mirzapour-Kamanaj, A, Agabalaye-Rahvar, M, Mohammadi-Ivatloo, B, Zare, K & Anvari-Moghaddam, A 2021, Information Gap Decision Theory for Scheduling of Electricity-Gas Systems in the Presence of Demand Response. i 2021 IEEE International Conference on Environment and Electrical Engineering : EEEIC 2021. IEEE Press, s. 1-6, 2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), Bari, Italien, 07/09/2021. https://doi.org/10.1109/EEEIC/ICPSEurope51590.2021.9584543

Information Gap Decision Theory for Scheduling of Electricity-Gas Systems in the Presence of Demand Response. / Talebi , Amir ; Mirzapour-Kamanaj, Amir ; Agabalaye-Rahvar, Masoud et al.
2021 IEEE International Conference on Environment and Electrical Engineering : EEEIC 2021. IEEE Press, 2021. s. 1-6.

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

TY - GEN

T1 - Information Gap Decision Theory for Scheduling of Electricity-Gas Systems in the Presence of Demand Response

AU - Talebi , Amir

AU - Mirzapour-Kamanaj, Amir

AU - Agabalaye-Rahvar, Masoud

AU - Mohammadi-Ivatloo, Behnam

AU - Zare, Kazem

AU - Anvari-Moghaddam, Amjad

PY - 2021

Y1 - 2021

N2 - High-level integration of wind energy in power networks has raised the need for flexible units. Gas-fuel generators (GFG) with fast startup/shutdown and high ramp rate capability can provide the required flexibility in the operation of wind energy. However, the operation of GFGs can be affected by the limitations of the fuel transmission system. Demand response programs can decrease the effect of fuel transmission system restrictions in the operation of GFGs and as well as, increase the integration of wind energy into the electricity network. In this work, a scheduling model for electricity and gas networks considering demand response programs is presented. Uncertainties pertain to wind energy and demand response program are addressed in this model. Moreover, power to gas technology is used to prevent wind curtailment. This scheduling model is based on the information gap decision theory (IGDT) that can assess the level of risk pertains to uncertainties. The proposed framework has been simulated on two different networks to represent the effectiveness of the model.

AB - High-level integration of wind energy in power networks has raised the need for flexible units. Gas-fuel generators (GFG) with fast startup/shutdown and high ramp rate capability can provide the required flexibility in the operation of wind energy. However, the operation of GFGs can be affected by the limitations of the fuel transmission system. Demand response programs can decrease the effect of fuel transmission system restrictions in the operation of GFGs and as well as, increase the integration of wind energy into the electricity network. In this work, a scheduling model for electricity and gas networks considering demand response programs is presented. Uncertainties pertain to wind energy and demand response program are addressed in this model. Moreover, power to gas technology is used to prevent wind curtailment. This scheduling model is based on the information gap decision theory (IGDT) that can assess the level of risk pertains to uncertainties. The proposed framework has been simulated on two different networks to represent the effectiveness of the model.

KW - electricity and gas networks

KW - demand response

KW - Power to Gas

KW - Information gap decision theory (IGDT)

U2 - 10.1109/EEEIC/ICPSEurope51590.2021.9584543

DO - 10.1109/EEEIC/ICPSEurope51590.2021.9584543

M3 - Article in proceeding

SN - 978-1-6654-3614-4

SP - 1

EP - 6

BT - 2021 IEEE International Conference on Environment and Electrical Engineering

PB - IEEE Press

T2 - 2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)

Y2 - 7 September 2021 through 10 September 2021

ER -

Talebi A, Mirzapour-Kamanaj A, Agabalaye-Rahvar M, Mohammadi-Ivatloo B, Zare K, Anvari-Moghaddam A. Information Gap Decision Theory for Scheduling of Electricity-Gas Systems in the Presence of Demand Response. I 2021 IEEE International Conference on Environment and Electrical Engineering : EEEIC 2021. IEEE Press. 2021. s. 1-6 doi: 10.1109/EEEIC/ICPSEurope51590.2021.9584543

Information Gap Decision Theory for Scheduling of Electricity-Gas Systems in the Presence of Demand Response

Abstract

Konference

Adgang til dokumentet

AUB Link

Fingeraftryk

Projekter

HeatReFlex: Green and Flexible District Heating/Cooling

Udstyr

Integrated Energy Systems Laboratory

Citationsformater