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Virtual-real interaction control of hybrid load system for low-carbon energy services

Author

Listed:
  • Wu, Xin
  • Yao, Lijuan
  • Pi, Tanxin
  • Liu, Yuhang
  • Li, Xiang
  • Gong, Gangjun

Abstract

Effectively aggregating demand-side loads is an essential way to promote the adjustment of energy structure. The electric water heaters (EWHs) and air conditioners (ACs) with great regulation potential make up a large part of demand-side loads. A system consisting of many EWHs and ACs presents hybrid characteristics of random and disorder due to the heterogeneity of physical parameters, which increases the difficulty of control. In this paper, a control system in layers based on virtual-real interaction is proposed to solve the problem. The control system consists of four layers: embedded control, aggregation control, cluster control and object control, which form a closed-loop. Starting from the embedded control layer, the bottom-up aggregation model is constructed to form a stable and controllable response system with considerable power transfer capacity. And from the object control layer, the top-down control model is built to stably adjust heterogeneous loads without influence on the comfort of users in a short-time scale. During the aggregation and control, aggregation characteristics of EWHs and ACs are modeled as the stochastic battery to describe the aggregation flexibility, and the complementarity of aggregation responses between EWHs and ACs is relied on to coordinate the load clusters. Moreover, the switching control model is constructed to manage the EWH cluster and develop the aggregation characteristic of EWHs. With the extensibility, the control algorithm is not limited to the application of EWHs and ACs. Finally, a tracking simulation for smoothing the clean energy output is provided to verify the effectiveness of the virtual-real interaction algorithm in layers.

Suggested Citation

  • Wu, Xin & Yao, Lijuan & Pi, Tanxin & Liu, Yuhang & Li, Xiang & Gong, Gangjun, 2023. "Virtual-real interaction control of hybrid load system for low-carbon energy services," Applied Energy, Elsevier, vol. 330(PB).
  • Handle: RePEc:eee:appene:v:330:y:2023:i:pb:s0306261922015768
    DOI: 10.1016/j.apenergy.2022.120319
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    References listed on IDEAS

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    1. Good, Nicholas, 2019. "Using behavioural economic theory in modelling of demand response," Applied Energy, Elsevier, vol. 239(C), pages 107-116.
    2. Wu, Xin & Liang, Kaixin & Jiao, Dian, 2019. "Air conditioner group collaborative method under multi-layer information interaction structure," Energy, Elsevier, vol. 186(C).
    3. Muhssin, Mazin T. & Cipcigan, Liana M. & Sami, Saif Sabah & Obaid, Zeyad Assi, 2018. "Potential of demand side response aggregation for the stabilization of the grids frequency," Applied Energy, Elsevier, vol. 220(C), pages 643-656.
    4. Lin, Boqiang & Li, Zheng, 2022. "Towards world's low carbon development: The role of clean energy," Applied Energy, Elsevier, vol. 307(C).
    5. Lynch, Muireann Á. & Nolan, Sheila & Devine, Mel T. & O’Malley, Mark, 2019. "The impacts of demand response participation in capacity markets," Applied Energy, Elsevier, vol. 250(C), pages 444-451.
    6. Boßmann, Tobias & Eser, Eike Johannes, 2016. "Model-based assessment of demand-response measures—A comprehensive literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1637-1656.
    7. Mitrašinović, Aleksandar M., 2021. "Photovoltaics advancements for transition from renewable to clean energy," Energy, Elsevier, vol. 237(C).
    8. Zheng, Shunlin & Sun, Yi & Li, Bin & Qi, Bing & Zhang, Xudong & Li, Fei, 2021. "Incentive-based integrated demand response for multiple energy carriers under complex uncertainties and double coupling effects," Applied Energy, Elsevier, vol. 283(C).
    9. Wei, Congying & Xu, Jian & Liao, Siyang & Sun, Yuanzhang & Jiang, Yibo & Zhang, Zhen, 2018. "Coordination optimization of multiple thermostatically controlled load groups in distribution network with renewable energy," Applied Energy, Elsevier, vol. 231(C), pages 456-467.
    10. Hui, Hongxun & Ding, Yi & Liu, Weidong & Lin, You & Song, Yonghua, 2017. "Operating reserve evaluation of aggregated air conditioners," Applied Energy, Elsevier, vol. 196(C), pages 218-228.
    Full references (including those not matched with items on IDEAS)

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