Engineering topics

Frank, Stephen (ORCID:0000000343666163)

Publications and source records attributed to Frank, Stephen (ORCID:0000000343666163).

4 records

DOE OSTI2024

Maximizing Efficiency and Quality: Leveraging Automated Testing for Laboratory Commissioning

The traditional commissioning process uses sampling to select equipment for functional acceptance testing when large quantities of equipment are present. Although this approach is generally effective in identifying wide-spread issues, it has several shortcomings: it fails to evaluate equipment not included in the sample, provides only a one-time validation of equipment operation, and the standard documentation is a simple checklist of pass/fail questions. During the construction and commissioning process of the new Research and Innovation Laboratory (RAIL) in Golden, CO, the National Renewable Energy Laboratory team engaged Group14 Engineering to implement a Connected Commissioning process using fault detection and diagnostic software for automated functional acceptance testing. This presentation highlights the advantages offered by automated functional testing in this critical laboratory setting: (1) sampling 100% of BAS-connected equipment during functional testing, (2) testing results backed by data beyond the traditional pass/fail checklist, and (3) an automated test process that can be regularly executed by the building management team for ongoing commissioning throughout the life of the building. The presentation will also cover technical challenges associated with Connected Commissioning and the important conversations with key stakeholders that need to occur well before functional acceptance testing in order to successfully implement the automated testing processes.

automated testing↗

DOE OSTI2023

Maximizing Efficiency and Quality: Leveraging Automated Testing for Laboratory Commissioning

The traditional commissioning process for a building often implements sampling rates to select equipment for functional acceptance testing when large quantities of equipment are present. While this approach is generally effective in identifying wide-spread issues, it has several key shortfalls. (1) It provides a one-time validation of equipment operation in which the standard documentation is a simple checklist of pass/fail questions. (2) It fails to evaluate equipment not included in the sample rate selection. During the construction and commissioning process of the new Research and Innovation Laboratory (RAIL) in Golden, CO, the National Renewable Energy Laboratory team engaged Group14 Engineering to implement a Connected Commissioning process using fault detection and diagnostic software for automated functional acceptance testing. The goal of this presentation will be to highlight the advantages offered by automated functional testing in this critical laboratory setting. These are primarily (1) sampling 100% of BAS-connected equipment during functional testing, (2) testing results backed by data beyond the traditional pass/fail checklist, and (3) an automated test process that can be regularly executed by the building management team for ongoing commissioning throughout the life of the building. The presentation will also cover technical challenges associated with connected commissioning and the important conversations with the key stakeholders that need to occur well before functional acceptance testing in order to successfully implement the automated testing processes.

automated testing↗

DOE OSTI2023

Progress Update from the EV Charging Working Group (#982)

The EV charging working group (#982) was formed in summer 2022 to define the equip, points, relationships, and operational data related to Level 1-2 (AC) and Level 3 (DC) Electric Vehicle Supply Equipment (EVSE). Last year, the working group developed formal EVSE-related equipment definitions, where were merged into the Project Haystack documentation in November 2022. More recently the group has also been working to expand and improve Haystack definitions related to electrical equipment and circuits. This initiative is important both for EV charging related applications and to improve Project Haystack's appeal to other industry verticals such as data centers, commercial building electrification, and more. In this presentation we will share the working group's motivation, progress, and near-term objectives.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗

DOE OSTI2023

A Modeling Toolkit for Comparing AC and DC Electrical Distribution Efficiency in Buildings

Recently, there has been considerable research interest in the potential for DC distribution systems in buildings instead of the traditional AC distribution systems. Due to the need for performing power conversions between DC and AC electricity, DC distribution may provide electrical efficiency advantages in some systems. To support comparative evaluations of AC-only, DC-only, and hybrid AC/DC distribution systems in buildings, a new modeling toolkit called the Building Electrical Efficiency Analysis Model (BEEAM) was developed and is described in this paper. To account for harmonics in currents or voltages arising from nonlinear devices, the toolkit implements harmonic power flow, along with nonlinear device behavioral descriptions derived from empirical measurements. This paper describes the framework, network equations, device representations, and an implementation of the toolkit in an open source software package, including a component library and graphical interface for creating circuits. Simulations of electrical behavior and device and system efficiencies using the toolkit are compared with experimental measurements of a small office environment in a variety of operating and load configurations. A detailed analysis of uncertainty estimation is also provided. Key findings were that a comparison of predicted versus measured efficiencies and power losses in the validation testbed using the initial toolkit implementation predicted device- and system-level efficiencies with reasonably good accuracy under both balanced and unbalanced AC scenarios. An uncertainty analysis also revealed that the maximum estimated error for system efficiency across all scenarios was 3%, and measured and modeled system efficiency agreed within the experimental uncertainty in approximately half of the scenarios. Based on the correspondence between simulation and measurement, the toolkit is proposed by the authors as a potentially useful tool for comparing efficiency in AC, DC, and hybrid AC/DC distribution systems in buildings.

DC distribution↗