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ADPA Optimization for Real-Time Energy Management Using Deep Learning

Author

Listed:
  • Zhengdong Wan

    (Energy Development Research Institute, China Southern Power Grid, Guangzhou 510530, China)

  • Yan Huang

    (Energy Development Research Institute, China Southern Power Grid, Guangzhou 510530, China)

  • Liangzheng Wu

    (Energy Development Research Institute, China Southern Power Grid, Guangzhou 510530, China)

  • Chengwei Liu

    (Central Southern China Electric Power Design Institute Co., Ltd. of China Power Engineering Consulting Group, Wuhan 430071, China)

Abstract

The current generation of renewable energy remains insufficient to meet the demands of users within the network, leading to the necessity of curtailing flexible loads and underscoring the urgent need for optimized microgrid energy management. In this study, the deep learning-based Adaptive Dynamic Programming Algorithm (ADPA) was introduced to integrate real-time pricing into the optimization of demand-side energy management for microgrids. This approach not only achieved a dynamic balance between supply and demand, along with peak shaving and valley filling, but it also enhanced the rationality of energy management strategies, thereby ensuring stable microgrid operation. Simulations of the Real-Time Electricity Price (REP) management model under demand-side response conditions validated the effectiveness and feasibility of this approach in microgrid energy management. Based on the deep neural network model, optimization of the objective function was achieved with merely 54 epochs, suggesting a highly efficient computational process. Furthermore, the integration of microgrid energy management with the REP conformed to the distributed multi-source power supply microgrid energy management and scheduling and improved the efficiency of clean energy utilization significantly, supporting the implementation of national policies aimed at the development of a sustainable power grid.

Suggested Citation

  • Zhengdong Wan & Yan Huang & Liangzheng Wu & Chengwei Liu, 2024. "ADPA Optimization for Real-Time Energy Management Using Deep Learning," Energies, MDPI, vol. 17(19), pages 1-13, September.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:19:p:4821-:d:1486296
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    References listed on IDEAS

    as
    1. Turdybek, Balgynbek & Tostado-Véliz, Marcos & Mansouri, Seyed Amir & Rezaee Jordehi, Ahmad & Jurado, Francisco, 2024. "A local electricity market mechanism for flexibility provision in industrial parks involving Heterogenous flexible loads," Applied Energy, Elsevier, vol. 359(C).
    2. Guo, Zhilong & Xu, Wei & Yan, Yue & Sun, Mei, 2023. "How to realize the power demand side actively matching the supply side? ——A virtual real-time electricity prices optimization model based on credit mechanism," Applied Energy, Elsevier, vol. 343(C).
    3. Wang, Dongxue & Fan, Ruguo & Yang, Peiwen & Du, Kang & Xu, Xiaoxia & Chen, Rongkai, 2024. "Research on floating real-time pricing strategy for microgrid operator in local energy market considering shared energy storage leasing," Applied Energy, Elsevier, vol. 368(C).
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