Research on low carbon economic dispatch of integrated energy system based on source-grid-load-storage collaboration
•The integrated strategy of source network load and storage optimization is adopted.•A comprehensive optimization method of step demand response incentive is proposed.•An adaptive optimization step demand response mechanism is proposed.•Integrate deep learning and intelligent control to reduce predi...
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| Published in: | Energy and buildings Vol. 349; p. 116434 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
15.12.2025
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| ISSN: | 0378-7788 |
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| Abstract | •The integrated strategy of source network load and storage optimization is adopted.•A comprehensive optimization method of step demand response incentive is proposed.•An adaptive optimization step demand response mechanism is proposed.•Integrate deep learning and intelligent control to reduce prediction error.
To reduce the impact of the weak stability of renewable energy, fully invoking the flexible load resources on the load side, and achieving low carbon economic dispatch of integrated energy systems, this paper proposes an optimization approach with source-grid-load-storage collaboration. Firstly, deep learning was used to build the next moment of wind power and photovoltaic prediction model. Based on this model, predictive control functions for wind and photovoltaic power generation are established from the objective function optimization-seeking perspective. Secondly, combining the coupling relationship and flexibility characteristics with the energy conversion of electric, heat, and cool loads, a stepped demand response incentive mechanism is introduced, and adaptive optimization is carried out for three parameters: compensation base price, interval length, and price growth rate. Finally, a multi-objective optimization model of the integrated energy system is developed by combining the different interests of the supply and load sides, solved by the multi-objective grey wolf optimization algorithm. Compared with the system optimized by a single multi-objective grey wolf optimization algorithm, the total cost and carbon emission reduction rates during the system optimization period were 8.89 % and 6.29 %, respectively. |
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| AbstractList | •The integrated strategy of source network load and storage optimization is adopted.•A comprehensive optimization method of step demand response incentive is proposed.•An adaptive optimization step demand response mechanism is proposed.•Integrate deep learning and intelligent control to reduce prediction error.
To reduce the impact of the weak stability of renewable energy, fully invoking the flexible load resources on the load side, and achieving low carbon economic dispatch of integrated energy systems, this paper proposes an optimization approach with source-grid-load-storage collaboration. Firstly, deep learning was used to build the next moment of wind power and photovoltaic prediction model. Based on this model, predictive control functions for wind and photovoltaic power generation are established from the objective function optimization-seeking perspective. Secondly, combining the coupling relationship and flexibility characteristics with the energy conversion of electric, heat, and cool loads, a stepped demand response incentive mechanism is introduced, and adaptive optimization is carried out for three parameters: compensation base price, interval length, and price growth rate. Finally, a multi-objective optimization model of the integrated energy system is developed by combining the different interests of the supply and load sides, solved by the multi-objective grey wolf optimization algorithm. Compared with the system optimized by a single multi-objective grey wolf optimization algorithm, the total cost and carbon emission reduction rates during the system optimization period were 8.89 % and 6.29 %, respectively. |
| ArticleNumber | 116434 |
| Author | Yun, Chengbo Huang, Lizhuang Zhang, Yiying Gao, Yuefen |
| Author_xml | – sequence: 1 givenname: Yuefen surname: Gao fullname: Gao, Yuefen email: gaoyuefen@163.com organization: Department of Power Engineering, North China Electric Power University, Baoding 071003 Hebei, China – sequence: 2 givenname: Lizhuang surname: Huang fullname: Huang, Lizhuang organization: Department of Power Engineering, North China Electric Power University, Baoding 071003 Hebei, China – sequence: 3 givenname: Chengbo surname: Yun fullname: Yun, Chengbo email: yun15690865572@163.com organization: China United Network Communications Co., Ltd., Sanmenxia Branch, Sanmenxia 472000 Henan, China – sequence: 4 givenname: Yiying surname: Zhang fullname: Zhang, Yiying organization: Department of Power Engineering, North China Electric Power University, Baoding 071003 Hebei, China |
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| Cites_doi | 10.1016/j.eng.2022.01.010 10.1016/j.apenergy.2020.115017 10.1016/j.cja.2025.103758 10.1016/j.energy.2021.120940 10.1016/j.eswa.2015.10.039 10.1016/j.enconman.2022.116085 10.1016/j.jclepro.2021.129009 10.1016/j.ijepes.2022.108600 10.1016/j.enconman.2020.113736 10.1016/j.epsr.2021.107394 10.1016/j.enconman.2019.112441 10.1016/j.epsr.2023.109169 10.3390/agriculture11121274 10.3390/en14248434 10.1002/er.6699 10.1016/j.ijepes.2022.108902 10.1016/j.energy.2021.120256 10.1016/j.energy.2022.124967 10.3390/su15054058 10.1016/j.energy.2023.127644 10.1016/j.energy.2019.01.036 10.1007/s00521-022-07704-5 10.1016/j.apenergy.2016.03.090 10.1080/15325008.2016.1138343 10.1016/j.renene.2023.05.082 10.1016/j.ijepes.2022.108832 |
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| Keywords | Deep learning Multi-objective grey wolf optimization algorithm Source-grid-load-storage collaboration Integrated energy systems Carbon emissions |
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| SubjectTerms | Carbon emissions Deep learning Integrated energy systems Multi-objective grey wolf optimization algorithm Source-grid-load-storage collaboration |
| Title | Research on low carbon economic dispatch of integrated energy system based on source-grid-load-storage collaboration |
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