KO algorithm-based bi-level optimal scheduling of electricity-carbon-hydrogen coupling systems with flexible resources for renewable energy integration
As renewable energy penetration keeps growing, its intermittent nature poses huge challenges on the reliable and flexible operation of power systems. The uncertainty and reverse peak shaving characteristics of renewable energy further exacerbate issues of load shedding and renewable energy curtailme...
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| Language: | English |
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Elsevier
2025-09-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025028804 |
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| author | Jing Liu Zicheng Guo Yalong Li |
| author_facet | Jing Liu Zicheng Guo Yalong Li |
| author_sort | Jing Liu |
| collection | DOAJ |
| description | As renewable energy penetration keeps growing, its intermittent nature poses huge challenges on the reliable and flexible operation of power systems. The uncertainty and reverse peak shaving characteristics of renewable energy further exacerbate issues of load shedding and renewable energy curtailment. Additionally, both carbon trading and hydrogen energy which play important roles in reducing carbon emissions have attracted great attention. A novel bi-level optimal scheduling model of the electricity-carbon-hydrogen coupling system is proposed considering carbon trading, load flexibility and deep peak shaving. The model simultaneously minimizes load shedding, renewable energy curtailment and the operation cost. Moreover, the Kepler optimization (KO) algorithm is applied and benchmarked against flower pollination (FP) algorithm and particle swarm optimization (PSO) algorithm. Finally, comparative studies confirm that incorporating flexible resources (flexible load and deep peak shaving) significantly reduce renewable curtailment, eliminate load shedding, and lower operation cost. Furthermore, the newly introduced KO algorithm demonstrates superior performance over both FP and PSO algorithms across all evaluated metrics. |
| format | Article |
| id | doaj-art-90470205f0a548a18dd3744d73c0c38c |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-90470205f0a548a18dd3744d73c0c38c2025-08-23T04:49:02ZengElsevierResults in Engineering2590-12302025-09-012710681610.1016/j.rineng.2025.106816KO algorithm-based bi-level optimal scheduling of electricity-carbon-hydrogen coupling systems with flexible resources for renewable energy integrationJing Liu0Zicheng Guo1Yalong Li2School of Mechanical and Electrical Engineering, China University of Mining & Technology-Beijing, Beijing 100083, China; The Key Laboratory of Intelligent Mining and Robotics, Ministry of Emergency Management, Beijing 100083, China; Corresponding author.School of Mechanical and Electrical Engineering, China University of Mining & Technology-Beijing, Beijing 100083, ChinaSchool of Mechanical and Electrical Engineering, China University of Mining & Technology-Beijing, Beijing 100083, ChinaAs renewable energy penetration keeps growing, its intermittent nature poses huge challenges on the reliable and flexible operation of power systems. The uncertainty and reverse peak shaving characteristics of renewable energy further exacerbate issues of load shedding and renewable energy curtailment. Additionally, both carbon trading and hydrogen energy which play important roles in reducing carbon emissions have attracted great attention. A novel bi-level optimal scheduling model of the electricity-carbon-hydrogen coupling system is proposed considering carbon trading, load flexibility and deep peak shaving. The model simultaneously minimizes load shedding, renewable energy curtailment and the operation cost. Moreover, the Kepler optimization (KO) algorithm is applied and benchmarked against flower pollination (FP) algorithm and particle swarm optimization (PSO) algorithm. Finally, comparative studies confirm that incorporating flexible resources (flexible load and deep peak shaving) significantly reduce renewable curtailment, eliminate load shedding, and lower operation cost. Furthermore, the newly introduced KO algorithm demonstrates superior performance over both FP and PSO algorithms across all evaluated metrics.http://www.sciencedirect.com/science/article/pii/S2590123025028804Electricity-carbon-hydrogen coupling systemshydrogen integrationRenewable energyDeep peak shavingKepler optimization algorithm |
| spellingShingle | Jing Liu Zicheng Guo Yalong Li KO algorithm-based bi-level optimal scheduling of electricity-carbon-hydrogen coupling systems with flexible resources for renewable energy integration Results in Engineering Electricity-carbon-hydrogen coupling systems hydrogen integration Renewable energy Deep peak shaving Kepler optimization algorithm |
| title | KO algorithm-based bi-level optimal scheduling of electricity-carbon-hydrogen coupling systems with flexible resources for renewable energy integration |
| title_full | KO algorithm-based bi-level optimal scheduling of electricity-carbon-hydrogen coupling systems with flexible resources for renewable energy integration |
| title_fullStr | KO algorithm-based bi-level optimal scheduling of electricity-carbon-hydrogen coupling systems with flexible resources for renewable energy integration |
| title_full_unstemmed | KO algorithm-based bi-level optimal scheduling of electricity-carbon-hydrogen coupling systems with flexible resources for renewable energy integration |
| title_short | KO algorithm-based bi-level optimal scheduling of electricity-carbon-hydrogen coupling systems with flexible resources for renewable energy integration |
| title_sort | ko algorithm based bi level optimal scheduling of electricity carbon hydrogen coupling systems with flexible resources for renewable energy integration |
| topic | Electricity-carbon-hydrogen coupling systems hydrogen integration Renewable energy Deep peak shaving Kepler optimization algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025028804 |
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