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
SN - 1996-1073
VL - 12
JO - Energies
JF - Energies
IS - 8
M1 - 1413
ER -