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Open Access

ARTICLE

A Distributionally Robust Optimization Scheduling Model for Regional Integrated Energy Systems Considering Hot Dry Rock Co-Generation

Hao Qi1, Mohamed Sharaf2, Andres Annuk3, Adrian Ilinca4, Mohamed A. Mohamed5,*
1 College of Electrical and New Energy, China Three Gorges University, Yichang, 443002, China
2 Department of Industrial Engineering, College of Engineering, King Saud University, Riyadh, 11421, Saudi Arabia
3 Institute of Forestry and Engineering, Estonian University of Life Sciences, Tartu, 51006, Estonia
4 Department of Mechanical Engineering, École de Technologie Supérieure, Montréal, QC H3C 1K3, Canada
5 Electrical Engineering Department, Faculty of Engineering, Minia University, Minia, 61519, Egypt
* Corresponding Author: Mohamed A. Mohamed. Email: dr.mohamed.abdelaziz@mu.edu.eg

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2024.048672

Received 14 December 2023; Accepted 11 March 2024; Published online 10 April 2024

Abstract

Hot dry rock (HDR) is rich in reserve, widely distributed, green, low-carbon, and has broad development potential and prospects. In this paper, a distributionally robust optimization (DRO) scheduling model for a regionally integrated energy system (RIES) considering HDR co-generation is proposed. First, the HDR-enhanced geothermal system (HDR-EGS) is introduced into the RIES. HDR-EGS realizes the thermoelectric decoupling of combined heat and power (CHP) through coordinated operation with the regional power grid and the regional heat grid, which enhances the system wind power (WP) feed-in space. Secondly, peak-hour loads are shifted using price demand response guidance in the context of time-of-day pricing. Finally, the optimization objective is established to minimize the total cost in the RIES scheduling cycle and construct a DRO scheduling model for RIES with HDR-EGS. By simulating a real small-scale RIES, the results show that HDR-EGS can effectively promote WP consumption and reduce the operating cost of the system.

Keywords

Energy harvesting; integrated energy systems; optimum scheduling; time-of-use pricing; demand response; geothermal energy

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