Impacts of Using Distributed Energy Resources to Reduce Peak Loads in Vermont

To help the United States develop a modern electricity grid that provides reliable power from multiple resources as well as resiliency under extreme conditions, the U.S. Department of Energy (DOE) is leading the Grid Modernization Initiative (GMI) to help shape the future of the nation's grid. Under the GMI, DOE funded the Vermont Regional Initiative project to provide the technical support and analysis to utilities that need to mitigate possible impacts of increasing renewable generation required by statewide goals. Advanced control of distributed energy resources (DER) can both support higher penetrations of renewable energy by balancing controllable loads to wind and photovoltaic (PV) solar generation and reduce peak demand by shedding noncritical loads. This work focuses on the latter. This document reports on an experiment that evaluated and quantified the potential benefits and impacts of reducing the peak load through demand response (DR) using centrally controllable electric water heaters (EWHs) and batteries on two Green Mountain Power (GMP) feeders. The experiment simulated various hypothetical scenarios that varied the number of controllable EWHs, the amount of distributed PV systems, and the number of distributed residential batteries. The control schemes were designed with several objectives. For the first objective, the primary simulations focused on reducing the load during the independent system operator (ISO) peak when capacity charges were the primary concern. The second objective was to mitigate DR rebound to avoid new peak loads and high ramp rates. The final objective was to minimize customers' discomfort, which is defined by the lack of hot water when it is needed. We performed the simulations using the National Renewable Energy Laboratory's (NREL's) Integrated Energy System Model (IESM) because it can simulate both electric power distribution feeder and appliance end use performance and it includes the ability to simulate multiple control strategies.