Abstract: Transmitting a large file across the internet requires breaking up the file into smaller packets of data. Packetized energy management (PEM) leverages similar concepts from communication theory to coordinate distributed energy resources by breaking up deferrable residential consumer demands into smaller fixed-duration/fixed-power packets of energy. Each individual load is managed by a probabilistic automaton that stochastically requests energy packets as a function of its local dynamic state (e.g., temperature or state-of-charge). Based on the aggregate request rate from packetized loads and grid conditions, the PEM coordinator will modulate the rate of accepting requests, which permits tight tracking of a reference (load-shaping or market) signal. This paper presents a state bin transition (macro) model suitable for characterizing a diverse population of electric water heaters (EWHs) and energy storage systems (ESSs) under a single PEM coordinator that is validated against an agent-based simulation of the diverse loads. The resulting model illustrates how diversity of packetized load types enhances the level of flexibility offered by the coordinator.
Abstract: This paper presents a state bin transition (macro)model for a large homogeneous population of thermostatically controlled loads (TCLs). The energy use of these TCLs is coordinated with a novel bottom-up asynchronous, anonymous, and randomizing control paradigm called Packetized Energy Management (PEM). A macro-model for a population of TCLs is developed and then augmented with a timer to capture the duration and consumption of energy packets and with exit-ON/OFF dynamics to ensure consumer quality of service. PEM permits a virtual power plant (VPP) operator to interact with TCLs through a packet request mechanism. The VPP regulates the proportion of accepted packet requests to allow tight tracking of balancing signals. The developed macro-model compares well with (agent-based) micro-simulations of TCLs under PEM and can be represented by a controlled Markov chain.