Optimal scheduling of regional integrated energy systems with hot dry rock enhanced geothermal system based on information gap decision theory

Author:

Liu Qingfeng1,Mohamed Mohamed A.2ORCID,Ilinca Adrian3ORCID,Annuk Andres4,Nasr Emad Abouel5ORCID

Affiliation:

1. College of Electrical and New Energy China Three Gorges University Yichang China

2. Electrical Engineering Department, Faculty of Engineering Minia University Minia Egypt

3. Department of Mechanical Engineering École de Technologie Supérieure Montréal Québec Canada

4. Institute of Forestry and Engineering Estonian University of Life Sciences Tartu Estonia

5. Department of Industrial Engineering, College of Engineering King Saud University Riyadh Saudi Arabia

Abstract

AbstractHot dry rock (HDR) is regarded as a promising resource of geothermal energy and becomes an important field for future geothermal development due to its advantages of high temperature, wide distribution and huge reserves. At present, HDR research is mainly focused on the modeling and efficiency evaluation of power generation cycle, but its relationship with the source side of the system has not been considered in the field of integrated energy systems. Therefore, this paper proposes a day‐ahead scheduling method for regional integrated energy systems (RIES) with HDR based on information gap decision theory (IGDT). First, the heat transfer system model of HDR is established according to the energy flow model and basic structure of the HDR enhanced geothermal system (EGS). Second, a comprehensive geothermal energy system scheduling model is established from HDR based on the energy hub modeling structure. Then, the IGDT is introduced to analyze the renewable energy output uncertainty in the model. Finally, through a real RIES analysis, the simulation results verified the correctness and effectiveness of the proposed model. The scheduling cost was ¥47,073 when EGS participated in the scheduling. Access to EGS reduced the system's total 24‐h energy purchase by 8305 kW, natural gas consumption by 3051.9 m3, and total carbon emissions by 742.28 kg. The latter emphasized that the proposed model achieves the purpose of reducing the system cost, saving energy and reducing emissions.

Funder

King Saud University

Publisher

Wiley

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