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A model-based predictive dispatch strategy for unlocking and optimizing the building energy flexibilities of multiple resources in electricity markets of multiple services

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  • Tang, Hong
  • Wang, Shengwei

Abstract

In recent years, demand side measures have been increasingly considered to provide flexibility services in different timescales (seconds, minutes, or longer timescale) and thereby improve the reliability and overall energy efficiency of power systems. However, the existing studies about multiple grid flexibility services only focus on the generation or storage resources, without considering the variety, controllability, and flexibility of different loads. Few studies have investigated the economic benefits and contributions of building energy flexibilities with fast and slow response speeds to different flexibility services. Therefore, this study develops a novel model-based predictive dispatch strategy for hybrid building energy systems to maximize the economic benefits in electricity markets of multiple services. The energy flexibilities of buildings are transformed to the bids for energy trading, peak charge and ancillary services in the electricity market. The system characteristics and the comfort (or preferences) of occupants regarding multiple flexibility resources, including dimmable lighting systems, HVAC systems, electrical vehicles and stationary batteries integrated with PV are considered. Tests are conducted to evaluate the performance of the strategy and the impacts on building operation, using real-time TRNSYS-MATLAB co-simulation. Test results show that electricity costs can be reduced by up to 26.1% when fully utilizing multiple revenue streams in an electricity market. The impacts of uncertain and high-granularity grid control signals on the indoor environment, the charging requirements of EVs and state of charge (SOC) of battery are negligible while the expected building power modulation following the real-time power grid control signals can be achieved.

Suggested Citation

  • Tang, Hong & Wang, Shengwei, 2022. "A model-based predictive dispatch strategy for unlocking and optimizing the building energy flexibilities of multiple resources in electricity markets of multiple services," Applied Energy, Elsevier, vol. 305(C).
    • Handle: RePEc:eee:appene:v:305:y:2022:i:c:s0306261921012058
    DOI: 10.1016/j.apenergy.2021.117889
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    References listed on IDEAS

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    3. Tang, Hong & Wang, Shengwei, 2023. "Game-theoretic optimization of demand-side flexibility engagement considering the perspectives of different stakeholders and multiple flexibility services," Applied Energy, Elsevier, vol. 332(C).
    4. Liao, Wei & Xiao, Fu & Li, Yanxue & Zhang, Hanbei & Peng, Jinqing, 2024. "A comparative study of demand-side energy management strategies for building integrated photovoltaics-battery and electric vehicles (EVs) in diversified building communities," Applied Energy, Elsevier, vol. 361(C).
    5. Li, Jiamei & Ai, Qian & Yin, Shuangrui & Hao, Ran, 2022. "An aggregator-oriented hierarchical market mechanism for multi-type ancillary service provision based on the two-loop Stackelberg game," Applied Energy, Elsevier, vol. 323(C).
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    7. Fu, Yangyang & O'Neill, Zheng & Wen, Jin & Pertzborn, Amanda & Bushby, Steven T., 2022. "Utilizing commercial heating, ventilating, and air conditioning systems to provide grid services: A review," Applied Energy, Elsevier, vol. 307(C).
    8. Tang, Hong & Wang, Shengwei, 2023. "Life-cycle economic analysis of thermal energy storage, new and second-life batteries in buildings for providing multiple flexibility services in electricity markets," Energy, Elsevier, vol. 264(C).
    9. Song, Yuguang & Xia, Mingchao & Chen, Qifang & Chen, Fangjian, 2023. "A data-model fusion dispatch strategy for the building energy flexibility based on the digital twin," Applied Energy, Elsevier, vol. 332(C).
    10. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).
    11. Tang, Hong & Wang, Shengwei, 2022. "Multi-level optimal dispatch strategy and profit-sharing mechanism for unlocking energy flexibilities of non-residential building clusters in electricity markets of multiple flexibility services," Renewable Energy, Elsevier, vol. 201(P1), pages 35-45.

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