An assessment and ranking method on demand response potential for urban-scale buildings based on the energy usage portrait

Ying Zhang, Wenjie Gang, Zaixun Ling, Lihong Su and Honglang Dai

Energy, 2025, vol. 335, issue C

Abstract: Identifying buildings with a high potential for load shifting and peak shaving is crucial for implementing effective demand response programs in urban energy systems. Existing methods are either too complex for large-scale applications or lack well-characterized indicators to identify potential participants. This study proposes an assessment method based on historical load data to identify buildings with strong demand response potential at the urban level. Two subsystems are developed to generate buildings' energy usage portraits, incorporating 16 indicators for load shifting and 9 for monthly peak shaving. Indicators are weighted using the entropy weight method in order to determine buildings’ ranking of demand response potential and identify potential participants. The method is validated using open data and applied to identify potential participants from 362 buildings in a city. Nineteen high-potential users, mainly institutional and commercial buildings, are identified as suitable for both load shifting and peak shaving. High-weighted indicators for load shifting include the load correlation coefficient and coefficient of variation of load rate during peak/flat periods, while those for peak shaving include the peak hour consistency, potential reduction rate, and weather sensitivity. This method provides utilities with an efficient tool to identify potential participants for demand response, enhancing grid security.

Keywords: Demand response; Load shifting; Peak shaving; Entropy weight; Energy data; Building (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:335:y:2025:i:c:s0360544225038381

DOI: 10.1016/j.energy.2025.138196

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