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Assessment of Commercial Building Lighting as a Frequency Regulation Resource

Author

Listed:
  • Alexandra Karpilow

    (Distributed Electrical Systems Laboratory, EPFL, Route Cantonale, 1015 Lausanne, Switzerland)

  • Gregor Henze

    (Department of Civil, Environmental and Architectural Engineering, University of Colorado, Boulder, CO 80309, USA
    National Renewable Energy Laboratory, Golden, CO 80401, USA
    Renewable and Sustainable Energy Institute, Boulder, CO 80309, USA)

  • Walter Beamer

    (Acuity Brands Lighting, Boulder, CO 80302, USA)

Abstract

This paper evaluates the potential for automated lighting control as a resource for frequency regulation of the electric grid system in the context of current energy policies, economic incentives, and technological trends. The growing prevalence of renewable energy has increased the need for ancillary services to maintain grid frequency and stability. While demand side resources like heating, ventilating, and air-conditioning systems, as well as water treatment plants are already evaluated as regulation service providers, the potential application to electrical lighting systems has largely been ignored. Yet, aggregations of lighting systems that are retrofitted with intelligent controls could conceivably contribute to frequency regulation services with little impact on user comfort. To further explore the feasibility of lighting potential, this paper explores (1) how lighting control systems are limited by visual comfort perception and acceptability, (2) how such limitations impact the performance of the lighting system as an frequency regulation resource, and (3) how the market potential of lighting systems as demand side resources compares in different regional transmission organizations. Finally, the impact of developing technologies on the application of lighting systems for frequency regulation is discussed.

Suggested Citation

  • Alexandra Karpilow & Gregor Henze & Walter Beamer, 2020. "Assessment of Commercial Building Lighting as a Frequency Regulation Resource," Energies, MDPI, vol. 13(3), pages 1-14, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:3:p:613-:d:315090
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    Cited by:

    1. 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).

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