skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Software-Based Challenges of Developing the Future Distribution Grid

Abstract

The software that the utility industry currently uses may be insufficient to analyze the distribution grid as it rapidly modernizes to include active resources such as distributed generation, switch and voltage control, automation, and increasingly complex loads. Although planners and operators have traditionally viewed the distribution grid as a passive load, utilities and consultants increasingly need enhanced analysis that incorporates active distribution grid loads in order to ensure grid reliability. Numerous commercial and open-source tools are available for analyzing distribution grid systems. These tools vary in complexity from providing basic load-flow and capacity analysis under steady-state conditions to time-series analysis and even geographical representations of dynamic and transient events. The need for each type of analysis is not well understood in the industry, nor are the reasons that distribution analysis requires different techniques and tools both from those now available and from those used for transmission analysis. In addition, there is limited understanding of basic capability of the tools and how they should be practically applied to the evolving distribution system. The study reviews the features and state of the art capability of current tools, including usability and visualization, basic analysis functionality, advanced analysis including inverters, and renewable generation andmore » load modeling. We also discuss the need for each type of distribution grid system analysis. In addition to reviewing basic functionality current models, we discuss dynamics and transient simulation in detail and draw conclusions about existing software?s ability to address the needs of the future distribution grid as well as the barriers to modernization of the distribution grid that are posed by the current state of software and model development. Among our conclusions are that accuracy, data transfer, and data processing abilities are key to future distribution grid modeling, and measured data sources are a key missing element . Modeling tools need to be calibrated based on measured grid data to validate their output in varied conditions such as high renewables penetration and rapidly changing topology. In addition, establishing a standardized data modeling format would enable users to transfer data among tools to take advantage of different analysis features. ?« less

Authors:
; ;
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1164206
Report Number(s):
LBNL-6708E
DOE Contract Number:  
DE-AC02-05CH11231
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
99 GENERAL AND MISCELLANEOUS

Citation Formats

Stewart, Emma, Kiliccote, Sila, and McParland, Charles. Software-Based Challenges of Developing the Future Distribution Grid. United States: N. p., 2014. Web. doi:10.2172/1164206.
Stewart, Emma, Kiliccote, Sila, & McParland, Charles. Software-Based Challenges of Developing the Future Distribution Grid. United States. https://doi.org/10.2172/1164206
Stewart, Emma, Kiliccote, Sila, and McParland, Charles. 2014. "Software-Based Challenges of Developing the Future Distribution Grid". United States. https://doi.org/10.2172/1164206. https://www.osti.gov/servlets/purl/1164206.
@article{osti_1164206,
title = {Software-Based Challenges of Developing the Future Distribution Grid},
author = {Stewart, Emma and Kiliccote, Sila and McParland, Charles},
abstractNote = {The software that the utility industry currently uses may be insufficient to analyze the distribution grid as it rapidly modernizes to include active resources such as distributed generation, switch and voltage control, automation, and increasingly complex loads. Although planners and operators have traditionally viewed the distribution grid as a passive load, utilities and consultants increasingly need enhanced analysis that incorporates active distribution grid loads in order to ensure grid reliability. Numerous commercial and open-source tools are available for analyzing distribution grid systems. These tools vary in complexity from providing basic load-flow and capacity analysis under steady-state conditions to time-series analysis and even geographical representations of dynamic and transient events. The need for each type of analysis is not well understood in the industry, nor are the reasons that distribution analysis requires different techniques and tools both from those now available and from those used for transmission analysis. In addition, there is limited understanding of basic capability of the tools and how they should be practically applied to the evolving distribution system. The study reviews the features and state of the art capability of current tools, including usability and visualization, basic analysis functionality, advanced analysis including inverters, and renewable generation and load modeling. We also discuss the need for each type of distribution grid system analysis. In addition to reviewing basic functionality current models, we discuss dynamics and transient simulation in detail and draw conclusions about existing software?s ability to address the needs of the future distribution grid as well as the barriers to modernization of the distribution grid that are posed by the current state of software and model development. Among our conclusions are that accuracy, data transfer, and data processing abilities are key to future distribution grid modeling, and measured data sources are a key missing element . Modeling tools need to be calibrated based on measured grid data to validate their output in varied conditions such as high renewables penetration and rapidly changing topology. In addition, establishing a standardized data modeling format would enable users to transfer data among tools to take advantage of different analysis features. ?},
doi = {10.2172/1164206},
url = {https://www.osti.gov/biblio/1164206}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Sun Jun 01 00:00:00 EDT 2014},
month = {Sun Jun 01 00:00:00 EDT 2014}
}