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The interactive dispatch strategy for thermostatically controlled loads based on the source–load collaborative evolution

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  • Song, Yuguang
  • Chen, Fangjian
  • Xia, Mingchao
  • Chen, Qifang

Abstract

With the urbanization and the decarbonization of the heating sector, thermostatically controlled loads (TCLs) with a rising energy consumption proportion, have become important demand response (DR) resources, which can provide considerable regulation flexibility for transition to renewable energy. Depending on the measurement or the forecast, TCLs dispatch methods established from the static perspective, failed to sufficiently consider the impact of DR chain reactions and the varying characteristics of TCLs response flexibility, which lead to the deterioration of the reliability and the availability of its dispatch potential. To address these issues, this paper proposes an interactive dispatch strategy based on the source–load collaborative evolution. Firstly, in light of system dynamics, a response evolution dynamic model is established by dividing of TCLs response process into multiple subsystems, which constitutes a comprehensive picture of TCLs response dynamics. Secondly, TCLs response flexibility is evaluated from the current and the evolution perspectives, which can enhance the reliability and stability of TCLs response by considering the potential response flexibility variations caused by the chain reaction of responses. Thirdly, an interactive dispatch strategy is devised by synthesizing of the source–load collaborative evolution dynamics from the dimensions of the elaborate state and the equivalent energy, which not only improves the dispatch performance, but also reduces the computational complexity. Based on the established strategy, TCLs response flexibility can be guided explicitly and quantitatively, like the energy storage in the dispatch, which improves the controllability and feasibility of TCLs response in DR service. Finally, the validity of the proposed strategy is verified via the comparison of it with the methods based on the static response mode.

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  • Song, Yuguang & Chen, Fangjian & Xia, Mingchao & Chen, Qifang, 2022. "The interactive dispatch strategy for thermostatically controlled loads based on the source–load collaborative evolution," Applied Energy, Elsevier, vol. 309(C).
  • Handle: RePEc:eee:appene:v:309:y:2022:i:c:s0306261921016305
    DOI: 10.1016/j.apenergy.2021.118395
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    as
    1. Zhang, Lingxi & Good, Nicholas & Mancarella, Pierluigi, 2019. "Building-to-grid flexibility: Modelling and assessment metrics for residential demand response from heat pump aggregations," Applied Energy, Elsevier, vol. 233, pages 709-723.
    2. Clauß, John & Stinner, Sebastian & Sartori, Igor & Georges, Laurent, 2019. "Predictive rule-based control to activate the energy flexibility of Norwegian residential buildings: Case of an air-source heat pump and direct electric heating," Applied Energy, Elsevier, vol. 237(C), pages 500-518.
    3. Rocha, Helder R.O. & Honorato, Icaro H. & Fiorotti, Rodrigo & Celeste, Wanderley C. & Silvestre, Leonardo J. & Silva, Jair A.L., 2021. "An Artificial Intelligence based scheduling algorithm for demand-side energy management in Smart Homes," Applied Energy, Elsevier, vol. 282(PA).
    4. Joe, Jaewan & Karava, Panagiota, 2019. "A model predictive control strategy to optimize the performance of radiant floor heating and cooling systems in office buildings," Applied Energy, Elsevier, vol. 245(C), pages 65-77.
    5. Zeng, Yuan & Zhang, Ruiwen & Wang, Dong & Mu, Yunfei & Jia, Hongjie, 2019. "A regional power grid operation and planning method considering renewable energy generation and load control," Applied Energy, Elsevier, vol. 237(C), pages 304-313.
    6. Kremers, Enrique & González de Durana, José Marı´a & Barambones, Oscar, 2013. "Emergent synchronisation properties of a refrigerator demand side management system," Applied Energy, Elsevier, vol. 101(C), pages 709-717.
    7. Hu, Maomao & Xiao, Fu, 2018. "Price-responsive model-based optimal demand response control of inverter air conditioners using genetic algorithm," Applied Energy, Elsevier, vol. 219(C), pages 151-164.
    8. Manfren, Massimiliano & Nastasi, Benedetto & Groppi, Daniele & Astiaso Garcia, Davide, 2020. "Open data and energy analytics - An analysis of essential information for energy system planning, design and operation," Energy, Elsevier, vol. 213(C).
    9. Peng, Yuzhen & Rysanek, Adam & Nagy, Zoltán & Schlüter, Arno, 2018. "Using machine learning techniques for occupancy-prediction-based cooling control in office buildings," Applied Energy, Elsevier, vol. 211(C), pages 1343-1358.
    10. Chen, Cong & Sun, Hongbin & Shen, Xinwei & Guo, Ye & Guo, Qinglai & Xia, Tian, 2019. "Two-stage robust planning-operation co-optimization of energy hub considering precise energy storage economic model," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    11. Zhou, Yue & Wang, Chengshan & Wu, Jianzhong & Wang, Jidong & Cheng, Meng & Li, Gen, 2017. "Optimal scheduling of aggregated thermostatically controlled loads with renewable generation in the intraday electricity market," Applied Energy, Elsevier, vol. 188(C), pages 456-465.
    12. Wei, Congying & Wu, Qiuwei & Xu, Jian & Sun, Yuanzhang & Jin, Xiaolong & Liao, Siyang & Yuan, Zhiyong & Yu, Li, 2020. "Distributed scheduling of smart buildings to smooth power fluctuations considering load rebound," Applied Energy, Elsevier, vol. 276(C).
    13. Behboodi, Sahand & Chassin, David P. & Djilali, Ned & Crawford, Curran, 2018. "Transactive control of fast-acting demand response based on thermostatic loads in real-time retail electricity markets," Applied Energy, Elsevier, vol. 210(C), pages 1310-1320.
    14. Bampoulas, Adamantios & Saffari, Mohammad & Pallonetto, Fabiano & Mangina, Eleni & Finn, Donal P., 2021. "A fundamental unified framework to quantify and characterise energy flexibility of residential buildings with multiple electrical and thermal energy systems," Applied Energy, Elsevier, vol. 282(PA).
    15. Li, Jinghua & Zhou, Jiasheng & Chen, Bo, 2020. "Review of wind power scenario generation methods for optimal operation of renewable energy systems," Applied Energy, Elsevier, vol. 280(C).
    16. Benedetto Nastasi & Massimiliano Manfren & Michel Noussan, 2020. "Open Data and Energy Analytics," Energies, MDPI, vol. 13(9), pages 1-3, May.
    17. Kazmi, Hussain & Suykens, Johan & Balint, Attila & Driesen, Johan, 2019. "Multi-agent reinforcement learning for modeling and control of thermostatically controlled loads," Applied Energy, Elsevier, vol. 238(C), pages 1022-1035.
    18. Xie, Dunjian & Hui, Hongxun & Ding, Yi & Lin, Zhenzhi, 2018. "Operating reserve capacity evaluation of aggregated heterogeneous TCLs with price signals," Applied Energy, Elsevier, vol. 216(C), pages 338-347.
    19. Afzalan, Milad & Jazizadeh, Farrokh, 2019. "Residential loads flexibility potential for demand response using energy consumption patterns and user segments," Applied Energy, Elsevier, vol. 254(C).
    20. Müller, F.L. & Jansen, B., 2019. "Large-scale demonstration of precise demand response provided by residential heat pumps," Applied Energy, Elsevier, vol. 239(C), pages 836-845.
    21. Qi, Ning & Cheng, Lin & Xu, Helin & Wu, Kuihua & Li, XuLiang & Wang, Yanshuo & Liu, Rui, 2020. "Smart meter data-driven evaluation of operational demand response potential of residential air conditioning loads," Applied Energy, Elsevier, vol. 279(C).
    22. Junker, Rune Grønborg & Azar, Armin Ghasem & Lopes, Rui Amaral & Lindberg, Karen Byskov & Reynders, Glenn & Relan, Rishi & Madsen, Henrik, 2018. "Characterizing the energy flexibility of buildings and districts," Applied Energy, Elsevier, vol. 225(C), pages 175-182.
    23. Xia, Mingchao & Song, Yuguang & Chen, Qifang, 2019. "Hierarchical control of thermostatically controlled loads oriented smart buildings," Applied Energy, Elsevier, vol. 254(C).
    24. Dong, Bing & Li, Zhaoxuan & Taha, Ahmad & Gatsis, Nikolaos, 2018. "Occupancy-based buildings-to-grid integration framework for smart and connected communities," Applied Energy, Elsevier, vol. 219(C), pages 123-137.
    25. Ding, Yi & Cui, Wenqi & Zhang, Shujun & Hui, Hongxun & Qiu, Yiwei & Song, Yonghua, 2019. "Multi-state operating reserve model of aggregate thermostatically-controlled-loads for power system short-term reliability evaluation," Applied Energy, Elsevier, vol. 241(C), pages 46-58.
    26. Du, Yan & Zandi, Helia & Kotevska, Olivera & Kurte, Kuldeep & Munk, Jeffery & Amasyali, Kadir & Mckee, Evan & Li, Fangxing, 2021. "Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning," Applied Energy, Elsevier, vol. 281(C).
    27. Nik, Vahid M. & Moazami, Amin, 2021. "Using collective intelligence to enhance demand flexibility and climate resilience in urban areas," Applied Energy, Elsevier, vol. 281(C).
    28. Lakshmanan, Venkatachalam & Marinelli, Mattia & Hu, Junjie & Bindner, Henrik W., 2016. "Provision of secondary frequency control via demand response activation on thermostatically controlled loads: Solutions and experiences from Denmark," Applied Energy, Elsevier, vol. 173(C), pages 470-480.
    29. Luchnikov, I. & Métivier, D. & Ouerdane, H. & Chertkov, M., 2021. "Super-relaxation of space–time-quantized ensemble of energy loads to curtail their synchronization after demand response perturbation," Applied Energy, Elsevier, vol. 285(C).
    30. Vivian, Jacopo & Quaggiotto, Davide & Zarrella, Angelo, 2020. "Increasing the energy flexibility of existing district heating networks through flow rate variations," Applied Energy, Elsevier, vol. 275(C).
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    5. Li, Li & Dong, Mi & Song, Dongran & Yang, Jian & Wang, Qibing, 2022. "Distributed and real-time economic dispatch strategy for an islanded microgrid with fair participation of thermostatically controlled loads," Energy, Elsevier, vol. 261(PB).

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