DRP-FLR: Data-Driven Assessment of Demand Response Potential for Flexible Load Regulation in Smart Grids

• URL: http://arxiv.org/abs/2607.22590v1
• Date: Wed, 10 Jun 2026 08:16:39 GMT
• Title: DRP-FLR: Data-Driven Assessment of Demand Response Potential for Flexible Load Regulation in Smart Grids
• Abstract summary: rapid growth of AI workloads and renewable energy resources exacerbates supply-demand imbalance in power systems.<n>Traditional load regulation designed for efficient allocation inadequate and motivating demand response mechanisms to enable load controllability in smart grids.<n>We propose -FLR to jointly optimize DR utilization, participant economic benefit, and renewable accommodation, while enforcing supply-demand balance and economic feasibility.
• Score: 11.669256895040531
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid growth of AI workloads and renewable energy resources exacerbates supply-demand imbalance in power systems, making traditional load regulation designed for efficient allocation inadequate and motivating demand response (DR) mechanisms to enable load controllability in smart grids. However, existing DR-oriented approaches either focus on optimizing electricity cost or occupant comfort with limited benefit to system-level balance. Others overlook the diverse and dynamic consumption patterns of heterogeneous energy entities, leading to significant over- or under-regulation. Therefore, we propose DRP-FLR. First, DRP-FLR achieves accurate short-term load forecasting by embedding exogenous knowledge (e.g., entity information, prediction time) into historical load representations. Next, it constructs entity-specific load-pattern profiles by clustering historical load curves, and estimates DR potential by matching forecasted loads with pattern profiles. Finally, DRP-FLR formulates flexible load regulation as a mixed-integer optimization problem and solves it with an MILP solver to jointly optimize DR utilization, participant economic benefit, and renewable accommodation, while enforcing supply-demand balance and economic feasibility. Experiments on a regional grid and a campus microgrid show that DRP-FLR reduces regulation deviation by 36.63%-91.87% and improves participant benefit by 44.66% on average.

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