Design of Electricity Demand-Response Programs

Vishal V. Agrawal () and Şafak Yücel ()
Additional contact information
Vishal V. Agrawal: McDonough School of Business, Georgetown University, Washington, District of Columbia 20057
Şafak Yücel: McDonough School of Business, Georgetown University, Washington, District of Columbia 20057

Management Science, 2022, vol. 68, issue 10, 7441-7456

Abstract: During a heat wave on a summer afternoon, a utility firm may face unusually high demand and procurement cost for electricity. Under such conditions, a demand-response event occurs, and the firm asks residential customers to reduce their demand. Such a demand-response program not only reduces the firm’s procurement cost, but it can also be environmentally beneficial by reducing generation from emissions-intensive power plants. In a demand-response program, a utility firm pays a rebate to customers for each unit of their demand reduction—the difference between a customer’s demand and consumption. However, the demand reduction cannot be directly measured because the firm cannot observe the customer’s demand, but only its consumption. Accordingly, a utility firm estimates the demand reduction by subtracting the consumption from a baseline, which is typically set as a customer’s average historical demand. In this paper, we first investigate how the existence of a baseline influences a customer’s demand reduction decision and when it leads to under- or overestimation of the actual demand reduction. We then analyze how a utility firm should adjust the baseline from the customer’s average demand. We find that it should inflate (deflate) the baseline if the cost difference between event and non-event periods is large (small), even though this may lead to greater overestimation (underestimation) of the customer’s demand reduction. Interestingly, we show that inflation of the baseline, which one would expect to make reducing demand more attractive for a customer, can actually lead to a smaller demand reduction, resulting in higher emissions.

Keywords: sustainable operations; energy-related operations; business model innovation; demand response (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://dx.doi.org/10.1287/mnsc.2021.4278 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormnsc:v:68:y:2022:i:10:p:7441-7456

Access Statistics for this article

More articles in Management Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().