Unlocking Deep Demand Flexibility via Dynamic Signals

TL;DR

This paper introduces a privacy-preserving dynamic signaling framework that uses feedback-based learning to enhance demand flexibility, reduce peaks, and improve grid stability amid increasing distributed energy resources.

Contribution

It presents a novel, scalable, and privacy-preserving dynamic pricing mechanism that effectively aligns HEMS behaviors with grid stability goals using aggregate demand feedback.

Findings

Significant peak demand reduction achieved in simulations.

Framework robust across diverse climates and scalable deployments.

Dynamic pricing enhances DERs' contribution to grid stability.

Abstract

The rapid proliferation of distributed energy resources (DERs) and the electrification of residential loads offer significant potential for grid flexibility but pose stability challenges under static pricing regimes. Specifically, high levels of automation under static Time-of-Use (TOU) tariffs often induce ``device synchronization,'' where simultaneous responses from home energy management systems (HEMS) create artificial demand peaks that threaten grid stability. This paper proposes a privacy-preserving, one-way dynamic signaling framework to unlock deep demand flexibility from HEMS. We utilize a feedback-based learning algorithm that updates day-ahead price profiles based on aggregate substation demand and environmental contexts, effectively closing the loop between utility objectives and aggregated edge behaviors. The framework is rigorously validated using high-fidelity simulations on an 84-bus distribution network populated with hundreds of HEMS controlling diverse devices, including HVAC, PV, batteries, and flexible loads. Results demonstrate that the proposed mechanism achieves substantial reductions in both peak demand and total load variation. Extensive analyses across diverse climates and scalable deployments confirm the framework's robustness, indicating that dynamic pricing acts as a force multiplier for DERs, with peak shaving potential increasing significantly under high renewable penetration scenarios.