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

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.

Optimization and Control (math.OC); Machine Learning (cs.LG)

I.2.1; I.5.3; G.1.6

arXiv:2607.22590 [math.OC]

(or arXiv:2607.22590v1 [math.OC] for this version)

https://doi.org/10.48550/arXiv.2607.22590 Focus to learn more arXiv-issued DOI via DataCite

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