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Transforming Electrical Load from an Operational Constraint to a Controllable Resource

Author

Listed:
  • Rajesh Tyagi

    (GE Global Research, Niskayuna, New York 12309)

  • Weiwei Chen

    (Rutgers Business School–Newark and New Brunswick, Newark, New Jersey 07901)

  • Jason Black

    (Huntington National Bank, Columbus, Ohio 43215)

  • Prasoon Tiwari

    (Columbus Labs, New Delhi 110 017, India)

  • Bernard Lecours

    (GE Energy Connection, Atlanta, Georgia 30339)

  • Jamison Shaver

    (MARSEC Inc., Palo Alto, California 94301)

Abstract

Electric utilities have historically treated power demand as an uncontrollable input, requiring generation and transmission resources to maintain the supply-demand balance. In recent years, demand response (DR) has emerged as a means to manage customer loads to balance the grid. This paper presents analytic solutions to enable utilities to optimize DR programs to serve as operational resources for the grid. We developed two sets of analytics. First, we developed a clustering-based method to accurately estimate the load curtailments expected from customers during DR events. Then, we used an option value-based optimal DR event scheduling method to compute a dynamic threshold value that the utility can use to make daily decisions for triggering DR events. In extensive tests, the proposed methods show superior performance over existing approaches. We implemented these analytics in the General Electric (GE) PowerOn™ Precision Demand Response Management System, which GE offered from 2011 to 2015.

Suggested Citation

  • Rajesh Tyagi & Weiwei Chen & Jason Black & Prasoon Tiwari & Bernard Lecours & Jamison Shaver, 2017. "Transforming Electrical Load from an Operational Constraint to a Controllable Resource," Interfaces, INFORMS, vol. 47(4), pages 292-304, August.
    • Handle: RePEc:inm:orinte:v:47:y:2017:i:4:p:292-304
    DOI: 10.1287/inte.2017.0894
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    References listed on IDEAS

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    1. Herter, Karen, 2007. "Residential implementation of critical-peak pricing of electricity," Energy Policy, Elsevier, vol. 35(4), pages 2121-2130, April.
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