Aggregate Modeling of Air-Conditioner Loads Under Packet-based Control with Both On and Off Grid Access Requests

TL;DR

This work develops an enhanced aggregate (macro) model for Packetized Energy Management (PEM) loads that simultaneously accounts for turn-ON and turn-OFF requests and allows for variable packet lengths. The model discretizes device temperatures into bin states, uses a controlled Markov chain to capture PEM actions, and includes opt-out dynamics and timer-bin progression to reflect energy packet lifecycles. Validation against agent-based simulations demonstrates strong agreement in fleet temperature distributions and aggregate power tracking under Reg-D signals, while robustness analyses quantify effects of parameter heterogeneity. The study also shows how variable packet lengths, informed by the macro-model distributions, can improve tracking performance relative to fixed-length PEM, offering practical guidance for PEM design and operation.

Abstract

Coordination of distributed energy resources (DERs) can engender flexibility necessary to improve grid reliability. Packetized Energy Management (PEM) is a method for coordinating DERs, such as thermostatically controlled loads (TCLs) and electric vehicles, within customer quality-of-service (QoS) limits. In PEM, a DER uses local information to offer flexibility by sending a request to the DER coordinator to turn-ON or turn-OFF. Much work has focused on modeling and analyzing aggregations of DERs under PEM with fixed packet durations and only turn-ON requests. Different recent efforts to enable variable packet lengths have shown an increase in available flexibility and ramping capability, but have not been modeled in aggregate, which limits systematic analyses. To address this issue, this paper presents a new aggregate bin-based (macro) model of PEM loads that incorporates both turn-ON and turn-OFF request features, enabling the model to accurately characterize the capability of the fleet of DERs to track a power reference signal, population temperature dynamics, aggregate request rates, and variable packet lengths. Simulation-based validation is performed against an agent-based (micro) model to evaluate robustness and quantify model accuracy. Finally, the distribution of variable packet lengths from macro-model simulations are applied to inform past work on PEM with randomized packet lengths

Figures (8)

• Figure 1: Typical architectural structure and components involved in PEM-based coordination of DERs
• Figure 2: Exponential CDFs used for probabilistic ON and OFF- request decisions. (a) Temperature-based exponential CDF for turn-ON requests. (b) Time-based exponential CDF for turn-OFF requests.
• Figure 3: Model validation showing power trajectory, temperature distribution, request patterns, and opt-out dynamics over one hour for a fleet of 2000 AC units.
• Figure 4: Temporal evolution of macro model temperature distributions and their mean trajectory
• Figure 5: Performance of the enhanced macro model while tracking an AGC Reg-D signal. Top: power tracking comparison. Bottom: the average population temperatures during Reg-D tracking.