Search

Distributed energy resources (DERs), such as responsive loads and energy storage systems, can help grid operators to more effectively balance supply and demand and manage network constraints. However, consumer acceptance of load coordination schemes depends on ensuring customer quality of service (QoS), thus requiring the careful management of device-level constraints. Simultaneously managing both device-level and system-level objectives is a difficult problem. Most potential solutions to the DER coordination problem are in some way distributed, with decision logic running both at the device (such as a thermostat) and the coordinator, with potentially complex communications between devices and one or more coordinators. It is difficult to effectively test distributed DER control schemes, particularly when there are thousands of devices with both device-level and grid-level constraints and asynchronous communications. This paper addresses this challenge by presenting a novel real-time grid-and-DER cyber-physical co-simulation platform for testing advanced DER coordination schemes and characterizing the behaviors of a DER fleet. The co-simulation platform is particularly suitable for: $i$) testing the real-time performance of a large fleet of DERs delivering advanced grid services; $ii$) characterizing the distribution of states for a large fleet of managed DERs; and $iii$) incorporating practical limitations of DERs and communications and analyzing the effects on fleet-wide performance. To illustrate these benefits, we demonstrate how the platform can be used to test a fleet with thousands of DERs coordinated using Packetized Energy Management (PEM) and interacting with a realistic transmission system model. [ABSTRACT FROM AUTHOR]

Using distributed energy resources (DERs), such as thermostatically controlled loads (TCLs), electric vehicles (EVs), and energy storage systems (ESSs) as a way to manage demand has been known for decades. A demand management scheme that explicitly considers the individual DER's local quality of service (QoS) is known as demand dispatch. Packetized energy management (PEM) is a demand dispatch paradigm that borrows packet-based concepts from wireless communications to dynamically manage fleets of DER at-scale and in realtime via small, discrete fixed-duration/fixed-power energy packets. PEM addresses QoS in a bottom-up fashion by having a coordinator authorize/deny incoming requests from DERs to consume energy packets. This manuscript extends prior work on modeling a large-scale population (i.e., macro-model) of homogeneous TCLs and ESSs operating under the PEM paradigm. In particular, we extend the macro-model methodology to include deferrable loads (DLs), such as EVs, together with analysis of QoS guarantees. Comparisons between an agent-based (micro-model) simulation and the proposed macro-model are presented to validate modeling accuracy and QoS guarantees. [ABSTRACT FROM AUTHOR]