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Sizing of district heating systems based on smart meter data: Quantifying the aggregated domestic energy demand and demand diversity in the UK

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  • Wang, Zhikun
  • Crawley, Jenny
  • Li, Francis G.N.
  • Lowe, Robert

Abstract

The sizing of district energy systems involves a trade-off between reliability and continuity of service, and avoidance of capital and running costs associated with oversizing. Finding the most appropriate sizing requires a thorough understanding of energy demand. However, empirical data necessary to support such an understanding is scarce, and district energy systems are typically oversized. This study uses smart meter data from the largest field trial to analyse residential energy consumption in the UK. It presents graphically the seasonal and daily variations in energy consumption patterns, the weather dependence of energy loads, and peak hourly demand during particularly cold weather conditions. It also explores the diversity effect in residential energy consumption and computes the after diversity maximum demand at different levels of aggregations. Results show that, peak hourly gas consumption was nearly seven times higher than electricity consumption during the cold spells, while diversity reduced gas and electricity maximum demand per dwelling up to 33% and 47%, respectively. This empirical quantitative analysis of energy demand and diversity can support improved design and operation of district energy, and in particular, enable reduced capital and running costs, and an improved understanding of economies of scale for district heating networks.

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  • Wang, Zhikun & Crawley, Jenny & Li, Francis G.N. & Lowe, Robert, 2020. "Sizing of district heating systems based on smart meter data: Quantifying the aggregated domestic energy demand and demand diversity in the UK," Energy, Elsevier, vol. 193(C).
    • Handle: RePEc:eee:energy:v:193:y:2020:i:c:s0360544219324752
    DOI: 10.1016/j.energy.2019.116780
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    4. Tang, Wenjun & Wang, Hao & Lee, Xian-Long & Yang, Hong-Tzer, 2022. "Machine learning approach to uncovering residential energy consumption patterns based on socioeconomic and smart meter data," Energy, Elsevier, vol. 240(C).
    5. Zheng, Ling & Zhou, Bin & Cao, Yijia & Wing Or, Siu & Li, Yong & Wing Chan, Ka, 2022. "Hierarchical distributed multi-energy demand response for coordinated operation of building clusters," Applied Energy, Elsevier, vol. 308(C).
    6. Nielsen, Tore Bach & Lund, Henrik & Østergaard, Poul Alberg & Duic, Neven & Mathiesen, Brian Vad, 2021. "Perspectives on energy efficiency and smart energy systems from the 5th SESAAU2019 conference," Energy, Elsevier, vol. 216(C).
    7. Meng, Guanfei & Liu, Hongxun & Li, Jianglong & Sun, Chuanwang, 2022. "Determination of driving forces for China's energy consumption and regional disparities using a hybrid structural decomposition analysis," Energy, Elsevier, vol. 239(PC).
    8. Yang, Ting & Zhao, Liyuan & Li, Wei & Zomaya, Albert Y., 2021. "Dynamic energy dispatch strategy for integrated energy system based on improved deep reinforcement learning," Energy, Elsevier, vol. 235(C).
    9. Salman Siddiqui & Mark Barrett & John Macadam, 2021. "A High Resolution Spatiotemporal Urban Heat Load Model for GB," Energies, MDPI, vol. 14(14), pages 1-28, July.
    10. Wang, Fei & Lu, Xiaoxing & Chang, Xiqiang & Cao, Xin & Yan, Siqing & Li, Kangping & Duić, Neven & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "Household profile identification for behavioral demand response: A semi-supervised learning approach using smart meter data," Energy, Elsevier, vol. 238(PB).
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