IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i11p4507-d1163305.html
   My bibliography  Save this article

A Bi-Level Optimal Operation Model for Small-Scale Active Distribution Networks Considering the Coupling Fluctuation of Spot Electricity Prices and Renewable Energy Sources

Author

Listed:
  • Yu Shi

    (Power Economic Research Institute of Jilin Electric Power Co., Ltd., Changchun 130021, China)

  • Fei Lv

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China)

  • Xuefeng Gao

    (Power Economic Research Institute of Jilin Electric Power Co., Ltd., Changchun 130021, China)

  • Minglei Jiang

    (Power Economic Research Institute of Jilin Electric Power Co., Ltd., Changchun 130021, China)

  • Huan Luo

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China)

  • Ruhang Xu

    (School of Economics and Management, North China Electric Power University, Beijing 102206, China
    Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Beijing 102206, China)

Abstract

As the penetration rate of variable renewable energy such as wind power increases in the power system, the composition and balance of the system also change gradually. The intermittency of renewable energy poses great stability challenges to the traditional centralized generation and load-oriented transmission and distribution methods. Therefore, the Active Distribution Network Operator (ADNO) with distributed installation at the local level has a good application prospect in the new scenario. However, ADNO needs to improve its operational efficiency based on the types of local generation and storage devices and the nature of the market environment. To address this issue, this paper proposes a forecasting method that considers the coupling fluctuations of spot electricity prices and renewable energy, and a bi-level optimization operation method based on the Stackelberg game for optimizing the operation of small-scale ADNO under high wind power penetration rate. Simulation results show that the proposed methods achieve greater positive impact on the operational efficiency of ADNO than conventional methods. In addition, the proposed methods ensure the long-term profitability of ADNO, even with fluctuations in external factors.

Suggested Citation

  • Yu Shi & Fei Lv & Xuefeng Gao & Minglei Jiang & Huan Luo & Ruhang Xu, 2023. "A Bi-Level Optimal Operation Model for Small-Scale Active Distribution Networks Considering the Coupling Fluctuation of Spot Electricity Prices and Renewable Energy Sources," Energies, MDPI, vol. 16(11), pages 1-26, June.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4507-:d:1163305
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/11/4507/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/11/4507/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fei Chen & Dong Liu & Xiaofang Xiong, 2017. "Research on Stochastic Optimal Operation Strategy of Active Distribution Network Considering Intermittent Energy," Energies, MDPI, vol. 10(4), pages 1-23, April.
    2. Alipour, Mohammadali & Aghaei, Jamshid & Norouzi, Mohammadali & Niknam, Taher & Hashemi, Sattar & Lehtonen, Matti, 2020. "A novel electrical net-load forecasting model based on deep neural networks and wavelet transform integration," Energy, Elsevier, vol. 205(C).
    3. Gangqiang Li & Huaizhi Wang & Shengli Zhang & Jiantao Xin & Huichuan Liu, 2019. "Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach," Energies, MDPI, vol. 12(13), pages 1-17, July.
    4. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    5. Jun Wang & Xiaodong Zheng & Nengling Tai & Wei Wei & Lingfang Li, 2019. "Resilience-Oriented Optimal Operation Strategy of Active Distribution Network," Energies, MDPI, vol. 12(17), pages 1-15, September.
    6. Dimitra G. Kyriakou & Fotios D. Kanellos, 2022. "Optimal Operation of Microgrids Comprising Large Building Prosumers and Plug-in Electric Vehicles Integrated into Active Distribution Networks," Energies, MDPI, vol. 15(17), pages 1-26, August.
    7. Hassan, Muhammed A. & Bailek, Nadjem & Bouchouicha, Kada & Nwokolo, Samuel Chukwujindu, 2021. "Ultra-short-term exogenous forecasting of photovoltaic power production using genetically optimized non-linear auto-regressive recurrent neural networks," Renewable Energy, Elsevier, vol. 171(C), pages 191-209.
    8. Umut Ugurlu & Ilkay Oksuz & Oktay Tas, 2018. "Electricity Price Forecasting Using Recurrent Neural Networks," Energies, MDPI, vol. 11(5), pages 1-23, May.
    9. Jiang, Tao & Li, Zening & Jin, Xiaolong & Chen, Houhe & Li, Xue & Mu, Yunfei, 2018. "Flexible operation of active distribution network using integrated smart buildings with heating, ventilation and air-conditioning systems," Applied Energy, Elsevier, vol. 226(C), pages 181-196.
    10. Ruhang, Xu, 2020. "Efficient clustering for aggregate loads: An unsupervised pretraining based method," Energy, Elsevier, vol. 210(C).
    11. Pengwei Cong & Wei Tang & Lu Zhang & Bo Zhang & Yongxiang Cai, 2017. "Day-Ahead Active Power Scheduling in Active Distribution Network Considering Renewable Energy Generation Forecast Errors," Energies, MDPI, vol. 10(9), pages 1-20, August.
    12. Tostado-Véliz, Marcos & Hasanien, Hany M. & Rezaee Jordehi, Ahmad & Turky, Rania A. & Gómez-González, Manuel & Jurado, Francisco, 2023. "An Interval-based privacy – Aware optimization framework for electricity price setting in isolated microgrid clusters," Applied Energy, Elsevier, vol. 340(C).
    Full references (including those not matched with items on IDEAS)

    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. Fachrizal Aksan & Vishnu Suresh & Przemysław Janik & Tomasz Sikorski, 2023. "Load Forecasting for the Laser Metal Processing Industry Using VMD and Hybrid Deep Learning Models," Energies, MDPI, vol. 16(14), pages 1-24, July.
    2. Yuan-Kang Wu & Cheng-Liang Huang & Quoc-Thang Phan & Yuan-Yao Li, 2022. "Completed Review of Various Solar Power Forecasting Techniques Considering Different Viewpoints," Energies, MDPI, vol. 15(9), pages 1-22, May.
    3. Xiao Han & Ming Zhou & Gengyin Li & Kwang Y. Lee, 2017. "Optimal Dispatching of Active Distribution Networks Based on Load Equilibrium," Energies, MDPI, vol. 10(12), pages 1-17, December.
    4. Yu Zhang & Xiaohui Song & Yong Li & Zilong Zeng & Chenchen Yong & Denis Sidorov & Xia Lv, 2020. "Two-Stage Active and Reactive Power Coordinated Optimal Dispatch for Active Distribution Network Considering Load Flexibility," Energies, MDPI, vol. 13(22), pages 1-13, November.
    5. Ma, Zhengjing & Mei, Gang, 2022. "A hybrid attention-based deep learning approach for wind power prediction," Applied Energy, Elsevier, vol. 323(C).
    6. Li, Min & Yang, Yi & He, Zhaoshuang & Guo, Xinbo & Zhang, Ruisheng & Huang, Bingqing, 2023. "A wind speed forecasting model based on multi-objective algorithm and interpretability learning," Energy, Elsevier, vol. 269(C).
    7. Shao, Zhen & Zheng, Qingru & Yang, Shanlin & Gao, Fei & Cheng, Manli & Zhang, Qiang & Liu, Chen, 2020. "Modeling and forecasting the electricity clearing price: A novel BELM based pattern classification framework and a comparative analytic study on multi-layer BELM and LSTM," Energy Economics, Elsevier, vol. 86(C).
    8. Omoyele, Olalekan & Hoffmann, Maximilian & Koivisto, Matti & Larrañeta, Miguel & Weinand, Jann Michael & Linßen, Jochen & Stolten, Detlef, 2024. "Increasing the resolution of solar and wind time series for energy system modeling: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    9. Md. Nazmul Hasan & Rafia Nishat Toma & Abdullah-Al Nahid & M M Manjurul Islam & Jong-Myon Kim, 2019. "Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach," Energies, MDPI, vol. 12(17), pages 1-18, August.
    10. 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).
    11. Norouzi, Mohammadali & Aghaei, Jamshid & Niknam, Taher & Alipour, Mohammadali & Pirouzi, Sasan & Lehtonen, Matti, 2023. "Risk-averse and flexi-intelligent scheduling of microgrids based on hybrid Boltzmann machines and cascade neural network forecasting," Applied Energy, Elsevier, vol. 348(C).
    12. Linlin Yu & Jiafeng Wu & Yuming Cheng & Gaojun Meng & Shuyu Chen & Yang Lu & Ke Xu, 2024. "Control Strategy for Wind Farms-Energy Storage Participation in Primary Frequency Regulation Considering Wind Turbine Operation State," Energies, MDPI, vol. 17(14), pages 1-13, July.
    13. Shengli Liao & Xudong Tian & Benxi Liu & Tian Liu & Huaying Su & Binbin Zhou, 2022. "Short-Term Wind Power Prediction Based on LightGBM and Meteorological Reanalysis," Energies, MDPI, vol. 15(17), pages 1-21, August.
    14. Wang, Xiaodi & Hao, Yan & Yang, Wendong, 2024. "Novel wind power ensemble forecasting system based on mixed-frequency modeling and interpretable base model selection strategy," Energy, Elsevier, vol. 297(C).
    15. Mohamed Massaoudi & Ines Chihi & Lilia Sidhom & Mohamed Trabelsi & Shady S. Refaat & Fakhreddine S. Oueslati, 2021. "Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements," Energies, MDPI, vol. 14(13), pages 1-20, July.
    16. Kalyani Makarand Kurundkar & Geetanjali Abhijit Vaidya, 2023. "Stochastic Security-Constrained Economic Dispatch of Load-Following and Contingency Reserves Ancillary Service Using a Grid-Connected Microgrid during Uncertainty," Energies, MDPI, vol. 16(6), pages 1-25, March.
    17. Chen, Fuhao & Yan, Jie & Liu, Yongqian & Yan, Yamin & Tjernberg, Lina Bertling, 2024. "A novel meta-learning approach for few-shot short-term wind power forecasting," Applied Energy, Elsevier, vol. 362(C).
    18. Wang, Jianzhou & Wang, Shuai & Zeng, Bo & Lu, Haiyan, 2022. "A novel ensemble probabilistic forecasting system for uncertainty in wind speed," Applied Energy, Elsevier, vol. 313(C).
    19. Yang, Hufang & Jiang, Ping & Wang, Ying & Li, Hongmin, 2022. "A fuzzy intelligent forecasting system based on combined fuzzification strategy and improved optimization algorithm for renewable energy power generation," Applied Energy, Elsevier, vol. 325(C).
    20. Liu, Cheng & Wang, Wei & Wang, Zhixia & Ding, Bei & Wu, Zhiqiang & Feng, Jingjing, 2024. "Data-driven modeling and fast adjustment for digital coded metasurfaces database: Application in adaptive electromagnetic energy harvesting," Applied Energy, Elsevier, vol. 365(C).

    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:gam:jeners:v:16:y:2023:i:11:p:4507-:d:1163305. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.