IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v283y2023ics036054422302580x.html
   My bibliography  Save this article

Multi-timescale optimization scheduling of regional integrated energy system based on source-load joint forecasting

Author

Listed:
  • Ma, Xin
  • Peng, Bo
  • Ma, Xiangxue
  • Tian, Changbin
  • Yan, Yi

Abstract

The uncertainty of renewable energy output randomness and multiple load demand uncertainty significantly increases the complexity of optimization and scheduling in regional integrated energy systems (RIES), rendering traditional optimization methods insufficient. This paper proposes a multi-scale optimization and scheduling strategy for RIES based on source-load forecasting. Firstly, considering the coupling characteristics of sources and loads, we construct a Multi-Task Multi-Head-based Source-Load Joint Forecasting Model (MTMH-MTL), which provides effective predictive data for optimization and scheduling. Secondly, to further mitigate the impacts of source and load randomness, we develop a multi-time scale optimization model, which plans unit output in two stages, namely, day-ahead and intra-day, through a rolling mechanism. Finally, case simulations confirm that the proposed approach flexibly reduces the influence of randomness on system operations. Compared to traditional methods, our approach reduces economic indicators by 9.17%, environmental indicators by 5.66%, significantly enhances RIES energy utilization, ensures dynamic user demand fulfillment, and strengthens energy stability.

Suggested Citation

  • Ma, Xin & Peng, Bo & Ma, Xiangxue & Tian, Changbin & Yan, Yi, 2023. "Multi-timescale optimization scheduling of regional integrated energy system based on source-load joint forecasting," Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:energy:v:283:y:2023:i:c:s036054422302580x
    DOI: 10.1016/j.energy.2023.129186
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422302580X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.129186?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Jin, Xiaolong & Mu, Yunfei & Jia, Hongjie & Wu, Jianzhong & Xu, Xiandong & Yu, Xiaodan, 2016. "Optimal day-ahead scheduling of integrated urban energy systems," Applied Energy, Elsevier, vol. 180(C), pages 1-13.
    2. Li, Chuang & Li, Guojie & Wang, Keyou & Han, Bei, 2022. "A multi-energy load forecasting method based on parallel architecture CNN-GRU and transfer learning for data deficient integrated energy systems," Energy, Elsevier, vol. 259(C).
    3. Yin, Linfei & Cao, Xinghui & Liu, Dongduan, 2023. "Weighted fully-connected regression networks for one-day-ahead hourly photovoltaic power forecasting," Applied Energy, Elsevier, vol. 332(C).
    4. Li, Peng & Wang, Zixuan & Wang, Jiahao & Guo, Tianyu & Yin, Yunxing, 2021. "A multi-time-space scale optimal operation strategy for a distributed integrated energy system," Applied Energy, Elsevier, vol. 289(C).
    5. Niu, Dongxiao & Yu, Min & Sun, Lijie & Gao, Tian & Wang, Keke, 2022. "Short-term multi-energy load forecasting for integrated energy systems based on CNN-BiGRU optimized by attention mechanism," Applied Energy, Elsevier, vol. 313(C).
    6. Zhu, Jizhong & Dong, Hanjiang & Zheng, Weiye & Li, Shenglin & Huang, Yanting & Xi, Lei, 2022. "Review and prospect of data-driven techniques for load forecasting in integrated energy systems," Applied Energy, Elsevier, vol. 321(C).
    7. Yu, Min & Niu, Dongxiao & Zhao, Jinqiu & Li, Mingyu & Sun, Lijie & Yu, Xiaoyu, 2023. "Building cooling load forecasting of IES considering spatiotemporal coupling based on hybrid deep learning model," Applied Energy, Elsevier, vol. 349(C).
    8. Lv, Chaoxian & Yu, Hao & Li, Peng & Wang, Chengshan & Xu, Xiandong & Li, Shuquan & Wu, Jianzhong, 2019. "Model predictive control based robust scheduling of community integrated energy system with operational flexibility," Applied Energy, Elsevier, vol. 243(C), pages 250-265.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yin, Boyi & Zhu, Wenjiang & Tang, Cheng & Wang, Can & Xu, Xinhai, 2025. "Hierarchical optimal scheduling of IES considering SOFC degradation, internal and external uncertainties," Applied Energy, Elsevier, vol. 381(C).
    2. Chen, Wenhao & Rong, Fei & Lin, Chuan, 2025. "A multi-energy loads forecasting model based on dual attention mechanism and multi-scale hierarchical residual network with gated recurrent unit," Energy, Elsevier, vol. 320(C).
    3. Yueyang Xu & Yibo Wang & Chuang Liu & Jian Xiong & Mo Zhou & Yang Du, 2025. "Adaptive Robust Optimal Scheduling of Combined Heat and Power Microgrids Based on Photovoltaic Mechanism/Data Fusion-Driven Power Prediction," Energies, MDPI, vol. 18(3), pages 1-23, February.
    4. Jin, Yuhui & Oko, Eni & Zhang, Junli & Shen, Jiong, 2024. "Distributed scheduling of integrated energy system considering the impacts of heating network and load on source-side components," Energy, Elsevier, vol. 313(C).
    5. Ren, Xiaoxiao & Wang, Jinshi & Yang, Sifan & Zhao, Quanbin & Jia, Yifan & Ou, Kejie & Hu, Guangtao & Yan, Junjie, 2025. "A novel multi-objective Stackelberg game model for multi-energy dynamic pricing and flexible scheduling in distributed multi-energy system," Energy, Elsevier, vol. 325(C).
    6. Fan, Guangyao & Yu, Binbin & Sun, Bo & Li, Fan, 2024. "Multi-time-space scale optimization for a hydrogen-based regional multi-energy system," Applied Energy, Elsevier, vol. 371(C).
    7. Wu, Kang & Jiang, Mian & Huang, Yisheng & Dai, Zhong & Wang, Xiaoming & Duan, Zhixuan & Wang, Yuqing & Li, Guiqiang, 2025. "Optimization of multi-objective capacity allocation and performance analysis for integrated energy systems considering hydrogen storage," Energy, Elsevier, vol. 325(C).
    8. Wang, Weichen & Sun, Jingchao & Yan, Su & Yuan, Yuxing & Xiao, Tianshun & Chen, Baoqi & Du, Tao & Na, Hongming, 2025. "Multi-objective optimization analysis of on-demand design in multi-source heating system for low carbon development," Energy, Elsevier, vol. 324(C).
    9. Wang, Mengxue & Zhao, Haoran & Liu, Chunyang & Ma, Dazhong & Jiang, Yibao & Yang, Futao, 2025. "Refined multi-time scale optimal scheduling of dynamic integrated energy system based on superposition of energy flow response," Applied Energy, Elsevier, vol. 380(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lin, Zhengyang & Lin, Tao & Li, Jun & Li, Chen, 2025. "A novel short-term multi-energy load forecasting method for integrated energy system based on two-layer joint modal decomposition and dynamic optimal ensemble learning," Applied Energy, Elsevier, vol. 378(PA).
    2. Xie, Xiangmin & Ding, Yuhao & Sun, Yuanyuan & Zhang, Zhisheng & Fan, Jianhua, 2024. "A novel time-series probabilistic forecasting method for multi-energy loads," Energy, Elsevier, vol. 306(C).
    3. Hu, Rong & Zhou, Kaile & Lu, Xinhui, 2025. "Integrated loads forecasting with absence of crucial factors," Energy, Elsevier, vol. 322(C).
    4. Fan, Pengdan & Wang, Dan & Wang, Wei & Zhang, Xiuyu & Sun, Yuying, 2024. "A novel multi-energy load forecasting method based on building flexibility feature recognition technology and multi-task learning model integrating LSTM," Energy, Elsevier, vol. 308(C).
    5. Xu, Jing & Wang, Xiaoying & Gu, Yujiong & Ma, Suxia, 2023. "A data-based day-ahead scheduling optimization approach for regional integrated energy systems with varying operating conditions," Energy, Elsevier, vol. 283(C).
    6. Yan, Qin & Lu, Zhiying & Liu, Hong & He, Xingtang & Zhang, Xihai & Guo, Jianlin, 2024. "Short-term prediction of integrated energy load aggregation using a bi-directional simple recurrent unit network with feature-temporal attention mechanism ensemble learning model," Applied Energy, Elsevier, vol. 355(C).
    7. Ren, Xiaoxiao & Tian, Xin & Wang, Kai & Yang, Sifan & Chen, Weixiong & Wang, Jinshi, 2025. "Enhanced load forecasting for distributed multi-energy system: A stacking ensemble learning method with deep reinforcement learning and model fusion," Energy, Elsevier, vol. 319(C).
    8. Wang, Danhao & Peng, Daogang & Huang, Dongmei & Zhao, Huirong & Qu, Bogang, 2025. "MMEMformer: A multi-scale memory-enhanced transformer framework for short-term load forecasting in integrated energy systems," Energy, Elsevier, vol. 322(C).
    9. Zhou, Yuan & Wang, Jiangjiang & Yang, Mingxu & Xu, Hangwei, 2023. "Hybrid active and passive strategies for chance-constrained bilevel scheduling of community multi-energy system considering demand-side management and consumer psychology," Applied Energy, Elsevier, vol. 349(C).
    10. Zheng, Lingwei & Zhou, Xingqiu & Qiu, Qi & Yang, Lan, 2020. "Day-ahead optimal dispatch of an integrated energy system considering time-frequency characteristics of renewable energy source output," Energy, Elsevier, vol. 209(C).
    11. Chen, Haoyu & Huang, Hai & Zheng, Yong & Yang, Bing, 2024. "A load forecasting approach for integrated energy systems based on aggregation hybrid modal decomposition and combined model," Applied Energy, Elsevier, vol. 375(C).
    12. Yin, Linfei & Ju, Linyi, 2025. "ShuffleTransformerMulti-headAttentionNet network for user load forecasting," Energy, Elsevier, vol. 322(C).
    13. Li, Peng & Wang, Jiahao & Jia, Hongjie & Li, Jianfeng & Pan, Youpeng, 2024. "Operation optimization of community integrated energy system: Rationality evaluation of operation scheme and a new solution approach," Applied Energy, Elsevier, vol. 375(C).
    14. Li, Ke & Mu, Yuchen & Yang, Fan & Wang, Haiyang & Yan, Yi & Zhang, Chenghui, 2023. "A novel short-term multi-energy load forecasting method for integrated energy system based on feature separation-fusion technology and improved CNN," Applied Energy, Elsevier, vol. 351(C).
    15. Ma, Tengfei & Pei, Wei & Xiao, Hao & Kong, Li & Mu, Yunfei & Pu, Tianjiao, 2020. "The energy management strategies based on dynamic energy pricing for community integrated energy system considering the interactions between suppliers and users," Energy, Elsevier, vol. 211(C).
    16. Zhou, Yuan & Wang, Jiangjiang & Wei, Changqi & Li, Yuxin, 2024. "A novel two-stage multi-objective dispatch model for a distributed hybrid CCHP system considering source-load fluctuations mitigation," Energy, Elsevier, vol. 300(C).
    17. Hu, Likun & Cao, Yi & Yin, Linfei, 2024. "Long-term price guidance mechanism for integrated energy systems based on gated recurrent unit - vision transformer prediction and fractional-order stochastic dynamic calculus control," Energy, Elsevier, vol. 312(C).
    18. Guo, Jiacheng & Wu, Di & Wang, Yuanyuan & Wang, Liming & Guo, Hanyuan, 2023. "Co-optimization method research and comprehensive benefits analysis of regional integrated energy system," Applied Energy, Elsevier, vol. 340(C).
    19. Yifei Chen & Zhihan Fu, 2023. "Multi-Step Ahead Forecasting of the Energy Consumed by the Residential and Commercial Sectors in the United States Based on a Hybrid CNN-BiLSTM Model," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    20. Liu, Dewen & Luo, Zhao & Qin, Jinghui & Wang, Hua & Wang, Gang & Li, Zhao & Zhao, Weijie & Shen, Xin, 2023. "Low-carbon dispatch of multi-district integrated energy systems considering carbon emission trading and green certificate trading," Renewable Energy, Elsevier, vol. 218(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:283:y:2023:i:c:s036054422302580x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.