IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i24p4701-d1000257.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/24/4701/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/24/4701/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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).
    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. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    2. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
    3. Zhu, Ziqing & Hu, Ze & Chan, Ka Wing & Bu, Siqi & Zhou, Bin & Xia, Shiwei, 2023. "Reinforcement learning in deregulated energy market: A comprehensive review," Applied Energy, Elsevier, vol. 329(C).
    4. Wu, Di & Ma, Xu & Balducci, Patrick & Bhatnagar, Dhruv, 2021. "An economic assessment of behind-the-meter photovoltaics paired with batteries on the Hawaiian Islands," Applied Energy, Elsevier, vol. 286(C).
    5. Wang, Kaifeng & Ye, Lin & Yang, Shihui & Deng, Zhanfeng & Song, Jieying & Li, Zhuo & Zhao, Yongning, 2023. "A hierarchical dispatch strategy of hybrid energy storage system in internet data center with model predictive control," Applied Energy, Elsevier, vol. 331(C).
    6. Hernandez-Matheus, Alejandro & Löschenbrand, Markus & Berg, Kjersti & Fuchs, Ida & Aragüés-Peñalba, Mònica & Bullich-Massagué, Eduard & Sumper, Andreas, 2022. "A systematic review of machine learning techniques related to local energy communities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    7. Grace Muriithi & Sunetra Chowdhury, 2021. "Optimal Energy Management of a Grid-Tied Solar PV-Battery Microgrid: A Reinforcement Learning Approach," Energies, MDPI, vol. 14(9), pages 1-24, May.
    8. Gao, Fang & Hu, Rongzhao & Yin, Linfei, 2023. "Variable boundary reinforcement learning for maximum power point tracking of photovoltaic grid-connected systems," Energy, Elsevier, vol. 264(C).
    9. Fan Li & Dan Wang & Dong Liu & Songheng Yang & Ke Sun & Zhongjian Liu & Haoyang Yu & Jishuo Qin, 2023. "A Comprehensive Review on Energy Storage System Optimal Planning and Benefit Evaluation Methods in Smart Grids," Sustainability, MDPI, vol. 15(12), pages 1-25, June.
    10. Li, Sichen & Hu, Weihao & Cao, Di & Chen, Zhe & Huang, Qi & Blaabjerg, Frede & Liao, Kaiji, 2023. "Physics-model-free heat-electricity energy management of multiple microgrids based on surrogate model-enabled multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 346(C).
    11. Eleni Stai & Josua Stoffel & Gabriela Hug, 2022. "Computing Day-Ahead Dispatch Plans for Active Distribution Grids Using a Reinforcement Learning Based Algorithm," Energies, MDPI, vol. 15(23), pages 1-22, November.
    12. Liu, Yixin & Shi, Haoqi & Guo, Li & Xu, Tao & Zhao, Bo & Wang, Chengshan, 2022. "Towards long-period operational reliability of independent microgrid: A risk-aware energy scheduling and stochastic optimization method," Energy, Elsevier, vol. 254(PB).
    13. Wang, Yi & Qiu, Dawei & Strbac, Goran, 2022. "Multi-agent deep reinforcement learning for resilience-driven routing and scheduling of mobile energy storage systems," Applied Energy, Elsevier, vol. 310(C).
    14. McIlwaine, Neil & Foley, Aoife M. & Morrow, D. John & Al Kez, Dlzar & Zhang, Chongyu & Lu, Xi & Best, Robert J., 2021. "A state-of-the-art techno-economic review of distributed and embedded energy storage for energy systems," Energy, Elsevier, vol. 229(C).
    15. Huo, Yuchong & Bouffard, François & Joós, Géza, 2022. "Integrating learning and explicit model predictive control for unit commitment in microgrids," Applied Energy, Elsevier, vol. 306(PA).

    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:jmathe:v:10:y:2022:i:24:p:4701-:d:1000257. 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.