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

Research output: Contribution to journalArticleScientificpeer-review

Researchers

Research units

  • University of Tabriz
  • Islamic Azad University
  • Northumbria University
  • Islamic Azad University
  • University of Castilla-La Mancha
  • NOVA University Lisbon

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.

Details

Original languageEnglish
Article number1413
Number of pages20
JournalEnergies
Volume12
Issue number8
Publication statusPublished - 12 Apr 2019
MoE publication typeA1 Journal article-refereed

    Research areas

  • demand response, energy hub, information gap decision theory, stochastic programming, Energy hub, Demand response, Information gap decision theory, Stochastic programming

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