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Study on multi-type flexible load control method of active distribution network based on dynamic time-sharing electricity price

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  • Cui, Jindong
  • Ran, Zihan
  • Shen, Wei
  • Xin, Yechun

Abstract

Accurate load control is the basis of safe operation and optimal dispatching in active distribution network, and the demand response system of flexible electricity price mechanism and incentive mechanism is the premise of load control. Aiming at the complexity increase of load control caused by the integration of conventional loads, electric vehicles, and micro-grid multi-type user loads in the existing active distribution network, this study comprehensively takes the power consumption willingness of multiple typesof users in active distribution network into account for the first time. This study dominated by the user willingness selects a typical active distribution network load-related data onto a certain area to conduct a calculation example analysis. Firstly, a multi-type user load demand response model of active distribution network is constructed. Then, in order to achieve flexible control of active distribution network loads, a dynamic time-of-use electricity price game model considering the target function and the willingness of multiple usersis proposed, in order to achieve the maximum of user satisfaction and interests of power supply enterprises. Furthermore, the example results are compared with the intrinsic electricity price.Finally, the simulation results show that the time-of-use price established by the multi-type user load game is effective inguiding users to adjust their power consumption habits, making the load curve shift, controlling the electricity load within the maximum power supply limit, and thus alleviating the pressure of power grid expansion.

Suggested Citation

  • Cui, Jindong & Ran, Zihan & Shen, Wei & Xin, Yechun, 2024. "Study on multi-type flexible load control method of active distribution network based on dynamic time-sharing electricity price," Applied Energy, Elsevier, vol. 357(C).
  • Handle: RePEc:eee:appene:v:357:y:2024:i:c:s0306261923018433
    DOI: 10.1016/j.apenergy.2023.122479
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    References listed on IDEAS

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    1. Ehsan, Ali & Yang, Qiang, 2019. "State-of-the-art techniques for modelling of uncertainties in active distribution network planning: A review," Applied Energy, Elsevier, vol. 239(C), pages 1509-1523.
    2. Seshu Kumar, R. & Phani Raghav, L. & Koteswara Raju, D. & Singh, Arvind R., 2021. "Impact of multiple demand side management programs on the optimal operation of grid-connected microgrids," Applied Energy, Elsevier, vol. 301(C).
    3. A. Jain & A. Mani & A. S. Siddiqui, 2019. "Network architecture for demand response implementation in smart grid," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(6), pages 1389-1402, December.
    4. Hafeez, Ghulam & Alimgeer, Khurram Saleem & Khan, Imran, 2020. "Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid," Applied Energy, Elsevier, vol. 269(C).
    5. Wadim Strielkowski & Dalia Streimikiene & Alena Fomina & Elena Semenova, 2019. "Internet of Energy (IoE) and High-Renewables Electricity System Market Design," Energies, MDPI, vol. 12(24), pages 1-17, December.
    6. Jesus Nieto-Martin & Timoleon Kipouros & Mark Savill & Jennifer Woodruff & Jevgenijs Butans, 2018. "Technoeconomic Distribution Network Planning Using Smart Grid Techniques with Evolutionary Self-Healing Network States," Complexity, Hindawi, vol. 2018, pages 1-18, October.
    7. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2019. "Single and Multi-Sequence Deep Learning Models for Short and Medium Term Electric Load Forecasting," Energies, MDPI, vol. 12(1), pages 1-21, January.
    Full references (including those not matched with items on IDEAS)

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