This manuscript presents design and analysis of a set of reference-tracking control policies for large-scale coordination of distributed energy resources (DERs) and quantifies tracking errors that arise due to heterogeneity in the power ratings for a fleet of DERs. In particular, the relay-based, reference-tracking control strategy that underpins much of packetized energy management (PEM) is augmented to uniquely leverage PEM’s energy packet request mechanism to optimize the number of accepted requests and to explicitly consider the quality of service (QoS). In addition, tracking errors from modeling a heterogeneous fleet of packetized DERs with a group of homogeneous macromodels are analytically derived for relevant PEM information scenarios. Finally, simulation-based analysis validates the results and shows that PEM is suitable for providing load balancing and ramping services for the grid.

Public safety power shutoffs (PSPS) are a common pre-emptive measure to reduce wildfire risk due to power system equipment failure. System operators use PSPS to de-energize electric grid elements that are either prone to failure or located in regions at a high risk of experiencing a wildfire. Successful power system operation during PSPS involves coordination across different time scales. Adjustments to generator commitments and transmission line de-energizations occur at day-ahead intervals while adjustments to load servicing occur at hourly intervals. Generator commitments and operational decisions have to be made under uncertainty in electric grid demand and wildfire potential forecasts. This paper presents deterministic and two-stage mean-CVaR stochastic frameworks to show how the likelihood of large wildfires near transmission lines affects generator commitment and transmission line de-energization strategies. The optimal costs of commitment, operation, and lost load on the IEEE 14-bus and 24-bus test systems are compared to the costs generated from prior optimal power shut-off (OPS) formulations. The proposed mean-CVaR stochastic program generates less total expected costs evaluated with respect to higher demand scenarios than costs generated by risk-neutral and deterministic methods.

Packetized Energy Management (PEM) is a demand dispatch scheme that can be used to provide ancillary services such as frequency regulation. In PEM, distributed energy resources (DERs) are granted uninterruptible access to the grid for a pre-specified time interval called the packet length. This results in a down ramp-limited response in PEM for DERs that can only consume power from the grid. In this work, a linearized virtual battery model of PEM is provided that is capable of predicting the down ramp-limited output of PEM and is used in a model predictive control (MPC) framework to improve the performance of PEM in tracking an automatic generation control (AGC) signal. By performing statistical analysis on the AGC regulation signal, PJM Reg-D, an autoregressive moving average (ARMA) model is derived as a predictor for the MPC-based precompensator. Finally, as an alternative to MPC, it is shown that by varying the packet length as a function of time, for example through packet randomization, frequency regulation can be improved under PEM.

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.

Electric vehicle (EV) penetration has been increasing in the modern electricity grid and has been complemented by the growth of EV charging infrastructure. This paper addresses the gap in the literature on the EV effects of total electricity costs on commercial buildings by incorporating V0G, V1G, and V2B charging. The electricity costs are minimized in 14 commercial buildings with real load profiles, demand, and energy charges. The scientific contributions of this study are the incorporation of demand charges, quantification of EV, and smart charging electricity costs and benefits using several representative long-term datasets, and the derivation of approximate equations that simplify the estimation of EV economic impacts. Our analysis is primarily based on an idealized uniform EV commuter fleet case study. The V1G and V2B charging electricity costs as a function of the number of EVs initially diverge with increasing charging demand and then become parallel to one another with the V2B electricity costs being lower than V1G costs. A longer EV layover time leads to higher numbers of V2B charging stations that can be installed such that original (pre-EV) electricity costs are not exceeded as compared to a shorter layover time. Sensitivity analyses based on changing the final state of charge (SOC) of EVs between 90% and 80% and initial SOC between 50% and 40% (thereby keeping charging energy demand constant) show that the total electricity costs are the same for V0G and V1G charging, while for V2B charging, the total electricity costs decrease as final SOC decreases.