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Economic Dispatch for Smart Buildings with Load Demand of High Volatility Based on Quasi-Quadratic Online Adaptive Dynamic Programming

Author

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  • Kairui Chen

    (School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
    School of Computer & Information, Qiannan Normal University for Nationalities, Guizhou 558000, China)

  • Zhangmou Zhu

    (School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China)

  • Jianhui Wang

    (School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China)

Abstract

In this paper, a quasi-quadratic online adaptive dynamic programming (QOADP) algorithm is proposed to realize optimal economic dispatch for smart buildings. Load demand of high volatility is considered, which is modeled by an uncontrollable state. To reduce residual errors of the approximation structure, a quasi-quadratic-form parametric structure was designed elaborately with a bias term to counteract effects of uncertainties. Based on action-dependent heuristic dynamic programming (ADHDP), an implementation of the QOADP algorithm is presented that involved obtaining optimal economic dispatch for smart buildings. Finally, hardware-in-loop (HIL) experiments were conducted, and the performance of the proposed QOADP algorithm is superior to that of two other typical algorithms.

Suggested Citation

  • Kairui Chen & Zhangmou Zhu & Jianhui Wang, 2022. "Economic Dispatch for Smart Buildings with Load Demand of High Volatility Based on Quasi-Quadratic Online Adaptive Dynamic Programming," Mathematics, MDPI, vol. 10(24), pages 1-15, December.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:24:p:4701-:d:1000257
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    References listed on IDEAS

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    1. Shang, Yuwei & Wu, Wenchuan & Guo, Jianbo & Ma, Zhao & Sheng, Wanxing & Lv, Zhe & Fu, Chenran, 2020. "Stochastic dispatch of energy storage in microgrids: An augmented reinforcement learning approach," Applied Energy, Elsevier, vol. 261(C).
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