IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v267y2020ics0306261920303913.html
   My bibliography  Save this article

Optimal dispatch based on prediction of distributed electric heating storages in combined electricity and heat networks

Author

Listed:
  • Wang, Haixin
  • Yang, Junyou
  • Chen, Zhe
  • Li, Gen
  • Liang, Jun
  • Ma, Yiming
  • Dong, Henan
  • Ji, Huichao
  • Feng, Jiawei

Abstract

The volatility of wind power generations could significantly challenge the economic and secure operation of combined electricity and heat networks. To tackle this challenge, this paper proposes a framework of optimal dispatch with distributed electric heating storage based on a correlation-based long short-term memory prediction model. The prediction model of distributed electric heating storage is developed to model its behavior characteristics which are obtained by the auto-correlation and correlation analysis with external factors including weather and time-of-use price. An optimal dispatch model of combined electricity and heat networks is then formulated and resolved by a constraint reduction technique with clustering and classification. Our method is verified through numerous simulations. The results show that, compared with the state-of-the-art techniques of support vector machine and recurrent neural networks, the mean absolute percentage error with the proposed correlation-based long short-term memory can be reduced by 1.009 and 0.481 respectively. Compared with conventional method, the peak wind power curtailment with dispatching distributed electric heating storage is reduced by nearly 30% and 50% in two cases respectively.

Suggested Citation

  • Wang, Haixin & Yang, Junyou & Chen, Zhe & Li, Gen & Liang, Jun & Ma, Yiming & Dong, Henan & Ji, Huichao & Feng, Jiawei, 2020. "Optimal dispatch based on prediction of distributed electric heating storages in combined electricity and heat networks," Applied Energy, Elsevier, vol. 267(C).
    • Handle: RePEc:eee:appene:v:267:y:2020:i:c:s0306261920303913
    DOI: 10.1016/j.apenergy.2020.114879
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920303913
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2020.114879?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
    2. Zhou, Yue & Wang, Chengshan & Wu, Jianzhong & Wang, Jidong & Cheng, Meng & Li, Gen, 2017. "Optimal scheduling of aggregated thermostatically controlled loads with renewable generation in the intraday electricity market," Applied Energy, Elsevier, vol. 188(C), pages 456-465.
    3. Zhou, Yue & Wu, Jianzhong & Long, Chao, 2018. "Evaluation of peer-to-peer energy sharing mechanisms based on a multiagent simulation framework," Applied Energy, Elsevier, vol. 222(C), pages 993-1022.
    4. Afrasiabi, Mousa & Mohammadi, Mohammad & Rastegar, Mohammad & Kargarian, Amin, 2019. "Multi-agent microgrid energy management based on deep learning forecaster," Energy, Elsevier, vol. 186(C).
    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. Ma, Yiming & Wang, Haixin & Hong, Feng & Yang, Junyou & Chen, Zhe & Cui, Haoqian & Feng, Jiawei, 2021. "Modeling and optimization of combined heat and power with power-to-gas and carbon capture system in integrated energy system," Energy, Elsevier, vol. 236(C).
    2. He, Zhaoyu & Guo, Weimin & Zhang, Peng, 2022. "Performance prediction, optimal design and operational control of thermal energy storage using artificial intelligence methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    3. Keke Wang & Dongxiao Niu & Min Yu & Yi Liang & Xiaolong Yang & Jing Wu & Xiaomin Xu, 2021. "Analysis and Countermeasures of China’s Green Electric Power Development," Sustainability, MDPI, vol. 13(2), pages 1-22, January.
    4. Zhang, Yijie & Ma, Tao & Yang, Hongxing, 2022. "Grid-connected photovoltaic battery systems: A comprehensive review and perspectives," Applied Energy, Elsevier, vol. 328(C).
    5. Jiaan Zhang & Chenyu Liu & Leijiao Ge, 2022. "Short-Term Load Forecasting Model of Electric Vehicle Charging Load Based on MCCNN-TCN," Energies, MDPI, vol. 15(7), pages 1-25, April.
    6. Zhilin Lyu & Quan Liu & Bin Liu & Lijun Zheng & Jiaqi Yi & Yongfa Lai, 2022. "Optimal Dispatch of Regional Integrated Energy System Group including Power to Gas Based on Energy Hub," Energies, MDPI, vol. 15(24), pages 1-22, December.
    7. Ji, Huichao & Wang, Haixin & Yang, Junyou & Feng, Jiawei & Yang, Yongyue & Okoye, Martin Onyeka, 2021. "Optimal schedule of solid electric thermal storage considering consumer behavior characteristics in combined electricity and heat networks," Energy, Elsevier, vol. 234(C).
    8. Haichuan Zhao & Ning Yan & Zuoxia Xing & Lei Chen & Libing Jiang, 2020. "Thermal Calculation and Experimental Investigation of Electric Heating and Solid Thermal Storage System," Energies, MDPI, vol. 13(20), pages 1-20, October.
    9. Yin, Linfei & Tao, Min, 2023. "Balanced broad learning prediction model for carbon emissions of integrated energy systems considering distributed ground source heat pump heat storage systems and carbon capture & storage," Applied Energy, Elsevier, vol. 329(C).
    10. Wang, Weijun & Dong, Zeyuan, 2021. "Economic benefits assessment of urban wind power central heating demonstration project considering the quantification of environmental benefits: A case from northern China," Energy, Elsevier, vol. 225(C).
    11. Chen, Maozhi & Lu, Hao & Chang, Xiqiang & Liao, Haiyan, 2023. "An optimization on an integrated energy system of combined heat and power, carbon capture system and power to gas by considering flexible load," Energy, Elsevier, vol. 273(C).
    12. Jiawei Feng & Junyou Yang & Haixin Wang & Huichao Ji & Martin Onyeka Okoye & Jia Cui & Weichun Ge & Bo Hu & Gang Wang, 2020. "Optimal Dispatch of High-Penetration Renewable Energy Integrated Power System Based on Flexible Resources," Energies, MDPI, vol. 13(13), pages 1-19, July.
    13. Yin, Linfei & Tao, Min, 2022. "Correlational broad learning for optimal scheduling of integrated energy systems considering distributed ground source heat pump heat storage systems," Energy, Elsevier, vol. 239(PE).
    14. Shang, Ce & Ge, Yuyou & Zhai, Suwei & Huo, Chao & Li, Wenyun, 2023. "Combined heat and power storage planning," Energy, Elsevier, vol. 279(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. Dong, Jingya & Song, Chunhe & Liu, Shuo & Yin, Huanhuan & Zheng, Hao & Li, Yuanjian, 2022. "Decentralized peer-to-peer energy trading strategy in energy blockchain environment: A game-theoretic approach," Applied Energy, Elsevier, vol. 325(C).
    2. Pia Szichta & Ingela Tietze, 2020. "Sharing Economy in der Elektrizitätswirtschaft: Treiber und Hemmnisse [Title sharing economy in the electricity sector: drivers and barriers]," NachhaltigkeitsManagementForum | Sustainability Management Forum, Springer, vol. 28(3), pages 109-125, December.
    3. Zhou, Yuekuan & Lund, Peter D., 2023. "Peer-to-peer energy sharing and trading of renewable energy in smart communities ─ trading pricing models, decision-making and agent-based collaboration," Renewable Energy, Elsevier, vol. 207(C), pages 177-193.
    4. Georgios Delagrammatikas & Spyridon Roukanas, 2023. "Offshore Wind Farm in the Southeast Aegean Sea and Energy Security," Energies, MDPI, vol. 16(13), pages 1-21, July.
    5. Lyu, Cheng & Jia, Youwei & Xu, Zhao, 2021. "Fully decentralized peer-to-peer energy sharing framework for smart buildings with local battery system and aggregated electric vehicles," Applied Energy, Elsevier, vol. 299(C).
    6. Yang, Mao & Wang, Da & Xu, Chuanyu & Dai, Bozhi & Ma, Miaomiao & Su, Xin, 2023. "Power transfer characteristics in fluctuation partition algorithm for wind speed and its application to wind power forecasting," Renewable Energy, Elsevier, vol. 211(C), pages 582-594.
    7. Min-Hwi Kim & Dong-Won Lee & Deuk-Won Kim & Young-Sub An & Jae-Ho Yun, 2021. "Energy Performance Investigation of Bi-Directional Convergence Energy Prosumers for an Energy Sharing Community," Energies, MDPI, vol. 14(17), pages 1-17, September.
    8. Diaz, Cesar & Ruiz, Fredy & Patino, Diego, 2017. "Modeling and control of water booster pressure systems as flexible loads for demand response," Applied Energy, Elsevier, vol. 204(C), pages 106-116.
    9. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda & Song, Jiakang, 2018. "Deep belief network based k-means cluster approach for short-term wind power forecasting," Energy, Elsevier, vol. 165(PA), pages 840-852.
    10. Ma, Li & Wang, Lingfeng & Liu, Zhaoxi, 2021. "Multi-level trading community formation and hybrid trading network construction in local energy market," Applied Energy, Elsevier, vol. 285(C).
    11. Lefeng, Shi & Shengnan, Lv & Chunxiu, Liu & Yue, Zhou & Cipcigan, Liana & Acker, Thomas L., 2020. "A framework for electric vehicle power supply chain development," Utilities Policy, Elsevier, vol. 64(C).
    12. Byeongtae Ahn, 2022. "Implementation and Early Adoption of an Ethereum-Based Electronic Voting System for the Prevention of Fraudulent Voting," Sustainability, MDPI, vol. 14(5), pages 1-16, March.
    13. João Mello & Cristina de Lorenzo & Fco. Alberto Campos & José Villar, 2023. "Pricing and Simulating Energy Transactions in Energy Communities," Energies, MDPI, vol. 16(4), pages 1-22, February.
    14. Wang, Zibo & Yu, Xiaodan & Mu, Yunfei & Jia, Hongjie, 2020. "A distributed Peer-to-Peer energy transaction method for diversified prosumers in Urban Community Microgrid System," Applied Energy, Elsevier, vol. 260(C).
    15. Davarzani, Sima & Pisica, Ioana & Taylor, Gareth A. & Munisami, Kevin J., 2021. "Residential Demand Response Strategies and Applications in Active Distribution Network Management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    16. Wu, Zhuochun & Xiao, Liye, 2019. "A structure with density-weighted active learning-based model selection strategy and meteorological analysis for wind speed vector deterministic and probabilistic forecasting," Energy, Elsevier, vol. 183(C), pages 1178-1194.
    17. Iria, José & Soares, Filipe & Matos, Manuel, 2019. "Optimal bidding strategy for an aggregator of prosumers in energy and secondary reserve markets," Applied Energy, Elsevier, vol. 238(C), pages 1361-1372.
    18. Meng, Anbo & Zhu, Zibin & Deng, Weisi & Ou, Zuhong & Lin, Shan & Wang, Chenen & Xu, Xuancong & Wang, Xiaolin & Yin, Hao & Luo, Jianqiang, 2022. "A novel wind power prediction approach using multivariate variational mode decomposition and multi-objective crisscross optimization based deep extreme learning machine," Energy, Elsevier, vol. 260(C).
    19. Xiao, Yulong & Zou, Chongzhe & Chi, Hetian & Fang, Rengcun, 2023. "Boosted GRU model for short-term forecasting of wind power with feature-weighted principal component analysis," Energy, Elsevier, vol. 267(C).
    20. Yuki Matsuda & Yuto Yamazaki & Hiromu Oki & Yasuhiro Takeda & Daishi Sagawa & Kenji Tanaka, 2021. "Demonstration of Blockchain Based Peer to Peer Energy Trading System with Real-Life Used PHEV and HEMS Charge Control," Energies, MDPI, vol. 14(22), pages 1-12, November.

    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:appene:v:267:y:2020:i:c:s0306261920303913. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.