An Exergy-Enhanced Improved IGDT-Based Optimal Scheduling Model for Electricity–Hydrogen Urban Integrated Energy Systems
Urban integrated energy systems (UIESs) play a critical role in facilitating low-carbon and high-efficiency energy transitions. However, existing scheduling strategies predominantly focus on energy quantity and cost, often neglecting the heterogeneity of energy quality across electricity, heat, gas,...
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MDPI AG
2025-07-01
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| Online Access: | https://www.mdpi.com/1099-4300/27/7/748 |
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| author | Min Xie Lei Qing Jia-Nan Ye Yan-Xuan Lu |
| author_facet | Min Xie Lei Qing Jia-Nan Ye Yan-Xuan Lu |
| author_sort | Min Xie |
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| description | Urban integrated energy systems (UIESs) play a critical role in facilitating low-carbon and high-efficiency energy transitions. However, existing scheduling strategies predominantly focus on energy quantity and cost, often neglecting the heterogeneity of energy quality across electricity, heat, gas, and hydrogen. This paper presents an exergy-enhanced stochastic optimization framework for the optimal scheduling of electricity–hydrogen urban integrated energy systems (EHUIESs) under multiple uncertainties. By incorporating exergy efficiency evaluation into a Stochastic Optimization–Improved Information Gap Decision Theory (SOI-IGDT) framework, the model dynamically balances economic cost with thermodynamic performance. A penalty-based iterative mechanism is introduced to track exergy deviations and guide the system toward higher energy quality. The proposed approach accounts for uncertainties in renewable output, load variation, and Hydrogen-enriched compressed natural gas (HCNG) combustion. Case studies based on a 186-bus UIES coupled with a 20-node HCNG network show that the method improves exergy efficiency by up to 2.18% while maintaining cost robustness across varying confidence levels. These results underscore the significance of integrating exergy into real-time robust optimization for resilient and high-quality energy scheduling. |
| format | Article |
| id | doaj-art-cd4bd799d2614aeb98b629fd89908c26 |
| institution | Kabale University |
| issn | 1099-4300 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Entropy |
| spelling | doaj-art-cd4bd799d2614aeb98b629fd89908c262025-08-20T03:36:14ZengMDPI AGEntropy1099-43002025-07-0127774810.3390/e27070748An Exergy-Enhanced Improved IGDT-Based Optimal Scheduling Model for Electricity–Hydrogen Urban Integrated Energy SystemsMin Xie0Lei Qing1Jia-Nan Ye2Yan-Xuan Lu3School of Electric Power, South China University of Technology, Guangzhou 510641, ChinaSchool of Electric Power, South China University of Technology, Guangzhou 510641, ChinaSchool of Electric Power, South China University of Technology, Guangzhou 510641, ChinaSchool of Electric Power, South China University of Technology, Guangzhou 510641, ChinaUrban integrated energy systems (UIESs) play a critical role in facilitating low-carbon and high-efficiency energy transitions. However, existing scheduling strategies predominantly focus on energy quantity and cost, often neglecting the heterogeneity of energy quality across electricity, heat, gas, and hydrogen. This paper presents an exergy-enhanced stochastic optimization framework for the optimal scheduling of electricity–hydrogen urban integrated energy systems (EHUIESs) under multiple uncertainties. By incorporating exergy efficiency evaluation into a Stochastic Optimization–Improved Information Gap Decision Theory (SOI-IGDT) framework, the model dynamically balances economic cost with thermodynamic performance. A penalty-based iterative mechanism is introduced to track exergy deviations and guide the system toward higher energy quality. The proposed approach accounts for uncertainties in renewable output, load variation, and Hydrogen-enriched compressed natural gas (HCNG) combustion. Case studies based on a 186-bus UIES coupled with a 20-node HCNG network show that the method improves exergy efficiency by up to 2.18% while maintaining cost robustness across varying confidence levels. These results underscore the significance of integrating exergy into real-time robust optimization for resilient and high-quality energy scheduling.https://www.mdpi.com/1099-4300/27/7/748exergy efficiencystochastic optimizationSOI-IGDTmultiple uncertaintiesintegrated energy systems |
| spellingShingle | Min Xie Lei Qing Jia-Nan Ye Yan-Xuan Lu An Exergy-Enhanced Improved IGDT-Based Optimal Scheduling Model for Electricity–Hydrogen Urban Integrated Energy Systems Entropy exergy efficiency stochastic optimization SOI-IGDT multiple uncertainties integrated energy systems |
| title | An Exergy-Enhanced Improved IGDT-Based Optimal Scheduling Model for Electricity–Hydrogen Urban Integrated Energy Systems |
| title_full | An Exergy-Enhanced Improved IGDT-Based Optimal Scheduling Model for Electricity–Hydrogen Urban Integrated Energy Systems |
| title_fullStr | An Exergy-Enhanced Improved IGDT-Based Optimal Scheduling Model for Electricity–Hydrogen Urban Integrated Energy Systems |
| title_full_unstemmed | An Exergy-Enhanced Improved IGDT-Based Optimal Scheduling Model for Electricity–Hydrogen Urban Integrated Energy Systems |
| title_short | An Exergy-Enhanced Improved IGDT-Based Optimal Scheduling Model for Electricity–Hydrogen Urban Integrated Energy Systems |
| title_sort | exergy enhanced improved igdt based optimal scheduling model for electricity hydrogen urban integrated energy systems |
| topic | exergy efficiency stochastic optimization SOI-IGDT multiple uncertainties integrated energy systems |
| url | https://www.mdpi.com/1099-4300/27/7/748 |
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