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Peak regulation strategies for ground source heat pump demand response of based on load forecasting: A case study of rural building in China

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Listed:
  • Meng, Qinglong
  • Wei, Ying'an
  • Fan, Jingjing
  • Li, Yanbo
  • Zhao, Fan
  • Lei, Yu
  • Sun, Hang
  • Jiang, Le
  • Yu, Lingli

Abstract

The increase in electricity consumption in rural areas has led to an overall increase in the peak load in both winter and summer, challenging the reliability of low-voltage distribution networks. A typical rural house in Xi ‘an area of Shaanxi Province is considered a case study. To aid the investigation, a ground source heat pump system is developed, installed, and used as the experimental platform. The simulation model of the rural residential building's energy consumption is established, and the demand response peak regulation strategy for the rural residential building in winter is investigated. Using the developed model, two demand response peak regulation models, Direct Compressor Control Mechanism (DCCM) and Thermostat Set-point Control Mechanism (TSCM) are formulated. Combined with a long-term and short-term memory neural network power load forecasting model, a simulation is carried out to analyze and study the indoor temperature, operating energy consumption, and other parameters under the two peak regulation scenarios. The proposed strategy is assessed and evaluated. Compared with the baseline model, the proposed strategy allows a reduction in the total building operating energy consumption of 41.9 % for the DCCM model and 17.2 % for the TSCM model.

Suggested Citation

  • Meng, Qinglong & Wei, Ying'an & Fan, Jingjing & Li, Yanbo & Zhao, Fan & Lei, Yu & Sun, Hang & Jiang, Le & Yu, Lingli, 2024. "Peak regulation strategies for ground source heat pump demand response of based on load forecasting: A case study of rural building in China," Renewable Energy, Elsevier, vol. 224(C).
  • Handle: RePEc:eee:renene:v:224:y:2024:i:c:s0960148124001241
    DOI: 10.1016/j.renene.2024.120059
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    1. Xiong, Chengyan & Meng, Qinglong & Wei, Ying'an & Luo, Huilong & Lei, Yu & Liu, Jiao & Yan, Xiuying, 2023. "A demand response method for an active thermal energy storage air-conditioning system using improved transactive control: On-site experiments," Applied Energy, Elsevier, vol. 339(C).
    2. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2018. "Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †," Energies, MDPI, vol. 11(7), pages 1-20, June.
    3. Li, Kai & Ma, Minda & Xiang, Xiwang & Feng, Wei & Ma, Zhili & Cai, Weiguang & Ma, Xin, 2022. "Carbon reduction in commercial building operations: A provincial retrospection in China," Applied Energy, Elsevier, vol. 306(PB).
    4. Bedi, Jatin & Toshniwal, Durga, 2019. "Deep learning framework to forecast electricity demand," Applied Energy, Elsevier, vol. 238(C), pages 1312-1326.
    5. Siano, Pierluigi & Sarno, Debora, 2016. "Assessing the benefits of residential demand response in a real time distribution energy market," Applied Energy, Elsevier, vol. 161(C), pages 533-551.
    6. Wang, Huaiyu & Ji, Changwei & Shi, Cheng & Yang, Jinxin & Wang, Shuofeng & Ge, Yunshan & Chang, Ke & Meng, Hao & Wang, Xin, 2023. "Multi-objective optimization of a hydrogen-fueled Wankel rotary engine based on machine learning and genetic algorithm," Energy, Elsevier, vol. 263(PD).
    7. Vanhoudt, D. & Geysen, D. & Claessens, B. & Leemans, F. & Jespers, L. & Van Bael, J., 2014. "An actively controlled residential heat pump: Potential on peak shaving and maximization of self-consumption of renewable energy," Renewable Energy, Elsevier, vol. 63(C), pages 531-543.
    8. Hwang, Hyunkyeong & Yoon, Ahyun & Yoon, Yongtae & Moon, Seungil, 2023. "Demand response of HVAC systems for hosting capacity improvement in distribution networks: A comprehensive review and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
    9. Wang, Huilong & Wang, Shengwei, 2021. "A disturbance compensation enhanced control strategy of HVAC systems for improved building indoor environment control when providing power grid frequency regulation," Renewable Energy, Elsevier, vol. 169(C), pages 1330-1342.
    10. Rama Curiel, José Adrián & Thakur, Jagruti, 2022. "A novel approach for Direct Load Control of residential air conditioners for Demand Side Management in developing regions," Energy, Elsevier, vol. 258(C).
    11. Hu, Maomao & Xiao, Fu & Wang, Lingshi, 2017. "Investigation of demand response potentials of residential air conditioners in smart grids using grey-box room thermal model," Applied Energy, Elsevier, vol. 207(C), pages 324-335.
    12. Gupta, Rajat & Morey, Johanna, 2022. "Empirical evaluation of demand side response trials in UK dwellings with smart low carbon technologies," Renewable Energy, Elsevier, vol. 199(C), pages 993-1004.
    13. Petrucci, Andrea & Ayevide, Follivi Kloutse & Buonomano, Annamaria & Athienitis, Andreas, 2023. "Development of energy aggregators for virtual communities: The energy efficiency-flexibility nexus for demand response," Renewable Energy, Elsevier, vol. 215(C).
    14. Wang, Jian Qi & Du, Yu & Wang, Jing, 2020. "LSTM based long-term energy consumption prediction with periodicity," Energy, Elsevier, vol. 197(C).
    15. Rinaldi, Arthur & Yilmaz, Selin & Patel, Martin K. & Parra, David, 2022. "What adds more flexibility? An energy system analysis of storage, demand-side response, heating electrification, and distribution reinforcement," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    16. Haider, Haider Tarish & See, Ong Hang & Elmenreich, Wilfried, 2016. "A review of residential demand response of smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 166-178.
    17. Guelpa, Elisa & Verda, Vittorio, 2021. "Demand response and other demand side management techniques for district heating: A review," Energy, Elsevier, vol. 219(C).
    Full references (including those not matched with items on IDEAS)

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