A Near-Optimal Maintenance Policy for Automated DR Devices

8 Pages Posted: 22 Oct 2014 Last revised: 25 Mar 2015

See all articles by Carlos Abad

Carlos Abad

Columbia University - Department of Industrial Engineering and Operations Research (IEOR)

Garud Iyengar

Columbia University - Department of Industrial Engineering and Operations Research (IEOR)

Date Written: February 28, 2015

Abstract

Demand side participation is now widely recognized as being extremely critical for satisfying the growing electricity demand in the US. The primary mechanism for demand management in the US is demand response (DR) programs that attempt to reduce or shift demand by giving incentives to participating customers via price discounts or rebate payments. Utilities that offer DR programs rely on automated DR devices (ADRs) to automate the response to DR signals. The ADRs are faulty; but the working state of the ADR is not directly observable -- one can, however, attempt to infer it from the power consumption during DR events. The utility loses revenue when a malfunctioning ADR does not respond to a DR signal; however, sending a maintenance crew to check and reset the ADR also incurs costs. In this paper, we show that the problem of maintaining a pool of ADRs using a limited number of maintenance crews can be formulated as a restless bandit problem, and that one can compute a near-optimal policy for this problem using Whittle indices. We show that the Whittle indices can be efficiently computed using a variational Bayes procedure even when the load-shed magnitude is noisy and when there is a random mismatch between the clocks at the utility and at the meter. The results of our numerical experiments suggest that the Whittle-index based approximate policy is within 3.95% of the optimal solution for all reasonably low values of the signal-to-noise ratio in the meter readings.

Keywords: Automated Demand Response, Dynamic programming, Bayes procedures

JEL Classification: C61, D81, C11

Suggested Citation

Abad, Carlos and Iyengar, Garud, A Near-Optimal Maintenance Policy for Automated DR Devices (February 28, 2015). Available at SSRN: https://ssrn.com/abstract=2512427 or http://dx.doi.org/10.2139/ssrn.2512427

Carlos Abad (Contact Author)

Columbia University - Department of Industrial Engineering and Operations Research (IEOR) ( email )

321 S.W. Mudd Building
500 West 120th Street
New York, NY 10027
United States

Garud Iyengar

Columbia University - Department of Industrial Engineering and Operations Research (IEOR) ( email )

331 S.W. Mudd Building
500 West 120th Street
New York, NY 10027
United States
+1 212-854-4594 (Phone)
+1 212-854-8103 (Fax)

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