@Article{cmes.2024.048672,
AUTHOR = {Hao Qi, Mohamed Sharaf, Andres Annuk, Adrian Ilinca, Mohamed A. Mohamed},
TITLE = {A Distributionally Robust Optimization Scheduling Model for Regional Integrated Energy Systems Considering Hot Dry Rock Co-Generation},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {140},
YEAR = {2024},
NUMBER = {2},
PAGES = {1387--1404},
URL = {http://www.techscience.com/CMES/v140n2/56553},
ISSN = {1526-1506},
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.},
DOI = {10.32604/cmes.2024.048672}
}