Designing effective demand response: A review of behavioral insights, consumer engagement, and operational strategies in energy systems

Demand response (DR) programs help electricity systems balance supply and demand by encouraging consumers to adjust their usage in response to price signals or incentives. As population growth, urbanization, and the electrification of transportation and industry continue to increase energy needs, flexible and effective DR programs have become increasingly important. However, consumer participation remains difficult to secure because individuals are influenced by a range of cognitive factors that shape how they perceive and respond to DR incentives. In this paper, we identify five broad categories of cognitive influences that systematically affect consumer decision-making in DR settings. For each category, we provide representative examples from DR programs and review the relevant literature. We then develop a conceptual framework linking consumer engagement, cognitive influences, and socioeconomic factors, and propose unified strategies for integrating behavioral interventions at each stage of the DR engagement process. These strategies aim not only to strengthen consumer participation but also to enhance the overall effectiveness of DR programs. By deepening our understanding of how behavioral factors shape consumer responses, this review offers actionable insights for practitioners, policymakers, and researchers seeking to design DR interventions that improve energy system performance while promoting efficiency and sustainable consumption habits.

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Publication Details

Journal
Energy Research & Social Science
Published
2025-12-03
DOI
https://doi.org/10.1016/j.erss.2025.104474
Citations
1
Primary Topic
Smart Grid Energy Management
Type
article
Field-Weighted Citation Impact
0.73
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article

Designing effective demand response: A review of behavioral insights, consumer engagement, and operational strategies in energy systems

Van‐Hai Bui, Duc Vu, Akhtar Hussain, Duy Linh Vu et al.
1 citations
Energy Research & Social Science
Smart Grid Energy Management
0.73
article

Designing effective demand response: A review of behavioral insights, consumer engagement, and operational strategies in energy systems

Van‐Hai Bui, Duc Vu, Akhtar Hussain, Duy Linh Vu, Xiao Zhang
article en
1 citations

Abstract

Demand response (DR) programs help electricity systems balance supply and demand by encouraging consumers to adjust their usage in response to price signals or incentives. As population growth, urbanization, and the electrification of transportation and industry continue to increase energy needs, flexible and effective DR programs have become increasingly important. However, consumer participation remains difficult to secure because individuals are influenced by a range of cognitive factors that shape how they perceive and respond to DR incentives. In this paper, we identify five broad categories of cognitive influences that systematically affect consumer decision-making in DR settings. For each category, we provide representative examples from DR programs and review the relevant literature. We then develop a conceptual framework linking consumer engagement, cognitive influences, and socioeconomic factors, and propose unified strategies for integrating behavioral interventions at each stage of the DR engagement process. These strategies aim not only to strengthen consumer participation but also to enhance the overall effectiveness of DR programs. By deepening our understanding of how behavioral factors shape consumer responses, this review offers actionable insights for practitioners, policymakers, and researchers seeking to design DR interventions that improve energy system performance while promoting efficiency and sustainable consumption habits.

Energy Research & Social ScienceVol. 131
National Economics University (VN), University of Michigan–Flint (US), Richard Wolf (Germany) (DE), University of Michigan–Dearborn (US), Université Laval (CA)
Openalex Percentile: Top 21%
Smart Grid Energy Management
0.73
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