Abstract
The penetration of distributed energy resources (DERs) on the electric power system is changing traditional power flow and analysis studies. DERs may cause the systems’ protection and control equipment to operate outside their intended parameters, due to DERs’ variability and dispatchability. As this penetration grows, hosting capacity studies as well as protection and control impact mitigation become critical components to advance this penetration. In order to conduct such studies accurately, the electric power system’s distribution components should be modeled correctly, and will require realistic time series loads at varying temporal and spatial conditions. The load component consists of the built environment and its load profiles. However, large-scale building load profiles are scarce, expensive, and hard to obtain. This article proposes a framework to fill this gap by developing detailed and scalable synthesized building load profile data sets. Specifically, a framework to extract load variability characteristics from a subset of buildings’ empirical load profiles is presented. Thirty-four discrete wavelet transform functions with three levels of decomposition are used to extract a taxonomy of load variability profiles. The profiles are then applied to modeled building load profiles, developed using the energy simulation program EnergyPlus®, to generate synthetic load profiles. The synthesized load profiles are variations of realistic representations of measured load profiles, containing load variabilities observed in actual buildings served by the electric power system. The paper focuses on the framework development with emphasis on variability extraction and application to develop 750 synthesized load profiles at a 15-minute time resolution.
Highlights
The penetration of distributed energy resources (DERs) on the electric power system, especially the distribution system segment, is changing the energy landscape as well as traditional power flow and analysis studies
The lack of large-scale measured data sets for buildings, the availability of EnergyPlus, the availability of commercial reference building model (CRBM)’s benchmark building stock, and the proposed generation of taxonomy of load variability in this paper present a compelling case for large-scale data set generation of synthetic yet accurate representations of building energy profiles for benchmark buildings that can be used for a variety of applications, including hosting capacity studies
This paper provides a framework for generating such data sets by combining the low-frequency information in modeled load profiles from EnergyPlus with high-frequency variability extracted from measured data to create synthetic load profiles
Summary
The penetration of distributed energy resources (DERs) on the electric power system, especially the distribution system segment, is changing the energy landscape (see Fig. 1 [1]) as well as traditional power flow and analysis studies. This is because the time series contain trends (daily, weekly, and seasonally) and changes in level and slope [3] These varying temporal and spatial time series load profiles and variability characteristics provide critical information for use with demand-side management, quasi-static simulation, DER planning, tools for short-term load forecasting, and synthetic load profile development, among others. EnergyPlus can provide yearly whole-building and building end-use categories (subsystems) time series load profile data at various timescales, ranging from 1-minute to 60-minute time resolutions, for the U.S building stock These load profiles represent building and occupant energy behaviors based on set schedules and parameters, yielding load profiles that contain less variability than what is encountered in actual buildings.
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