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.
Highlights
Electricity energy is procured in various ways including purchasing power from the DA and RT electricity markets, generating power by using wind turbine and the combined heat and power (CHP) units, and using the energy storage system (ESS)
Heat is procured in two ways, from the CHP and the boiler units
This paper has proposed an Information Gap Decision Theory (IGDT)-based model for solving the Energy hub (EH) management problem by considering a demand response (DR) program for the problem faced by large consumers of power systems to procure their energies
Summary
Operations of energy hub are conducted using various devices such as combined heat and power (CHP) units, electrical and thermal energy storage, boilers, power electronic devices, etc. The low efficiency of plants operating with fossil fuels have encouraged researchers to use this concept for using and combining different types of energy [2]. The emergence of this concept in a restructured electricity. The decisions will be more complicated in the presence of uncertainties arisen by different types of technology, and it is essential to consider them in the problem to increase the robustness of the solution
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