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

Arsalan Najafi, Mousa Marzband, Behnam Mohamadi-Ivatloo, Javier Contreras, Mahdi Pourakbari-Kasmaei, Matti Lehtonen and Radu Godina
Additional contact information
Arsalan Najafi: Young Researchers and Elite Club, Sepidan Branch, Islamic Azad University, Sepidan 73611, Iran
Mousa Marzband: Faculty of Engineering and Environment, Department of Maths, Physics and Electrical Engineering, Northumbria University Newcastle, Newcastle upon Tyne NE1 8ST, UK
Behnam Mohamadi-Ivatloo: Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 51999, Iran
Javier Contreras: E.T.S. de Ingenieros Industriales, University of Castilla-La Mancha, 13071 Ciudad Real, Spain
Mahdi Pourakbari-Kasmaei: Department of Electrical Engineering and Automation, Aalto University, Maarintie 8, 02150 Espoo, Finland
Matti Lehtonen: Department of Electrical Engineering and Automation, Aalto University, Maarintie 8, 02150 Espoo, Finland
Radu Godina: UNIDEMI, Department of Mechanical and Industrial Engineering, Faculty of Science and Technology (FCT), Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal

Energies, 2019, vol. 12, issue 8, 1-20

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 (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/8/1413/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/8/1413/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2019:i:8:p:1413-:d:222226

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().