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Demand charges and user flexibility – Exploring differences in electricity consumer types and load patterns within the Swedish commercial sector

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  • van Zoest, Vera
  • El Gohary, Fouad
  • Ngai, Edith C.H.
  • Bartusch, Cajsa

Abstract

Demand-based charges have been employed as a tool intended to reduce electricity users’ maximum demand but there is a lack of consensus regarding their efficacy. One reason for this may be the diversity in the flexibility potential of different types of users. This study explores the flexibility potential of different types of electricity consumers in the small to medium-sized commercial sector (35-63A) in response to a compulsory demand charge. The objective is to characterize varying levels of flexibility with respect to different types of commercial users with different load patterns. A multivariate clustering technique was used to group commercial users with comparable load patterns based on a year of hourly data before the tariff change was introduced. This method was used to: (1) match users from the intervention area and reference area with similar load patterns, without losing any user data, and (2) compare how users with different load patterns react differently to the tariff change. We found clear distinctions in the types of commercial users in each cluster and their response to the tariff, demonstrating the extent to which demand flexibility may be dependent on the nature of an organization’s activities and its respective load patterns. The highest demand flexibility was found in clusters which had a large share of users in the IT sector, commerce and public administration. The lowest demand flexibility was found in the real estate and education sectors. Future research should further investigate these variations and explore the possibilities of tailoring interventions to the specific types of users.

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  • van Zoest, Vera & El Gohary, Fouad & Ngai, Edith C.H. & Bartusch, Cajsa, 2021. "Demand charges and user flexibility – Exploring differences in electricity consumer types and load patterns within the Swedish commercial sector," Applied Energy, Elsevier, vol. 302(C).
    • Handle: RePEc:eee:appene:v:302:y:2021:i:c:s0306261921009211
    DOI: 10.1016/j.apenergy.2021.117543
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    References listed on IDEAS

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    1. Luis Hernández & Carlos Baladrón & Javier M. Aguiar & Belén Carro & Antonio Sánchez-Esguevillas, 2012. "Classification and Clustering of Electricity Demand Patterns in Industrial Parks," Energies, MDPI, vol. 5(12), pages 1-14, December.
    2. Hobman, Elizabeth V. & Frederiks, Elisha R. & Stenner, Karen & Meikle, Sarah, 2016. "Uptake and usage of cost-reflective electricity pricing: Insights from psychology and behavioural economics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 455-467.
    3. Jang, Dongsik & Eom, Jiyong & Jae Park, Min & Jeung Rho, Jae, 2016. "Variability of electricity load patterns and its effect on demand response: A critical peak pricing experiment on Korean commercial and industrial customers," Energy Policy, Elsevier, vol. 88(C), pages 11-26.
    4. Al-Wakeel, Ali & Wu, Jianzhong & Jenkins, Nick, 2017. "k-means based load estimation of domestic smart meter measurements," Applied Energy, Elsevier, vol. 194(C), pages 333-342.
    5. Yilmaz, S. & Rinaldi, A. & Patel, M.K., 2020. "DSM interactions: What is the impact of appliance energy efficiency measures on the demand response (peak load management)?," Energy Policy, Elsevier, vol. 139(C).
    6. Öhrlund, Isak & Schultzberg, Mårten & Bartusch, Cajsa, 2019. "Identifying and estimating the effects of a mandatory billing demand charge," Applied Energy, Elsevier, vol. 237(C), pages 885-895.
    7. Zigui Jiang & Rongheng Lin & Fangchun Yang, 2018. "A Hybrid Machine Learning Model for Electricity Consumer Categorization Using Smart Meter Data," Energies, MDPI, vol. 11(9), pages 1-19, August.
    8. Räsänen, Teemu & Voukantsis, Dimitrios & Niska, Harri & Karatzas, Kostas & Kolehmainen, Mikko, 2010. "Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data," Applied Energy, Elsevier, vol. 87(11), pages 3538-3545, November.
    9. Wohlfarth, Katharina & Klobasa, Marian & Gutknecht, Ralph, 2020. "Demand response in the service sector – Theoretical, technical and practical potentials," Applied Energy, Elsevier, vol. 258(C).
    10. Fateh Nassim Melzi & Allou Same & Mohamed Haykel Zayani & Latifa Oukhellou, 2017. "A Dedicated Mixture Model for Clustering Smart Meter Data: Identification and Analysis of Electricity Consumption Behaviors," Energies, MDPI, vol. 10(10), pages 1-21, September.
    11. Katrina Jessoe & David Rapson, 2015. "Commercial and Industrial Demand Response Under Mandatory Time-of-Use Electricity Pricing," Journal of Industrial Economics, Wiley Blackwell, vol. 63(3), pages 397-421, September.
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    Cited by:

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    2. Patankar, Neha & Fell, Harrison G. & Rodrigo de Queiroz, Anderson & Curtis, John & DeCarolis, Joseph F., 2022. "Improving the representation of energy efficiency in an energy system optimization model," Applied Energy, Elsevier, vol. 306(PB).

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