-
- Arras Energy was developed with four main analysis use-cases in mind, hosting capacity,
-tariff design, electrification, and resilience. These use-cases were identified by the Technical
-Advisory Committee. In addition, these use-cases provided the basis for additional
-requirements, which themselves can be considered more general use-cases for HiPAS GridLABD, giving rise to the following important new features:
-
-
- - CYME model conversion and import.
- - Historical, real-time, and forecasted weather.
- - AMI and SCADA data import.
- - Distribution system assets, such as poles and duct banks.
- - Geographic data sets such as address resolution, ground elevation, vegetation, service
-territories, census tracts, geographic distances and powerline paths.
- - Subcommands to enable internal data and model modification and maintenance
-routines.
- - Tools to enable external data and model generation and creation capabilities.
- - Templates to enable open-source distribution of analysis methodologies.
- - Output to plotting modules and cloud-deployed/streaming data repositories.
- - Support for cloud-based deployment infrastructure, including Amazon AWS and Docker.
- - Support for automated continuous integration and continuous deployment (CI/CA)
- - Online documentation for each version deployed.
-
-
-These features result in important new algorithmic, modeling capabilities, and upgrades that
-are now a standard part of the GridLAB-D suite of tools distributed in the open-source by the
-Linux Foundation Energy (LF Energy) under the brand name “Arras Energy”.
-
-Hosting Capacity Analysis
-The goal of hosting capacity analysis is to quantify the maximum DER generation, EV charger,
-and demand response that can deployed at any location in a distribution network without
-violating distribution system voltage, current, or control limits. This process was also referred
-to as integration capacity analysis (ICA), particularly when focused only on solar resource
-integration.
-
-ICA is achieved using a system-wide iterative power flow solution that examines
-all the combinations of loading at every customer meter in a distribution system. Distributed
-generation and DER resources are varied at customer meter, independently, to verify whether
-a system violation occurs somewhere within a feeder.
-
-Grid Resilience Analysis
-The resilience use-case was supported by the US Department of Energy’s Grid Resilience
-Intelligence Project (GRIP) project, funded by the Solar Energy Technology Office. The goal of
-GRIP is to assist distribution utilities in responding to grid events by:
-
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- - Anticipating grid events using machine learning and artificial intelligence techniques
-with diverse data sources.
- - Absorbing grid events by employing validated control strategies for distributed energy
-resources; and
- - Reducing recovery time by managing distributed energy resources in the case of limited
-communications.
-
-
-GRIP builds on previous efforts to collect massive amounts of data that can be used to finetune grid operations, including the VADER project and other Grid Modernization Lab
-Consortium projects on distributed controls and cyber security.
-
-GRIP included innovative applications of artificial intelligence and machine learning for
-distribution grid resilience using predictive analytics, image recognition, increased “learning”
-and “problem solving” capabilities for anticipation of grid events. The GRIP project
-demonstrates distributed control theory with and without communications to absorb and
-recover from grid events.
-
-GRIP was deployed, tested and validated with utility partners in North America. Anticipation
-analytics were tested and validated with Southern California Edison; absorption algorithms
-were tested in Vermont; and extremum seeking controls developed by Lawrence Berkeley
-National Laboratory were tested with member utilities of the National Rural Electric
-Cooperative Association.
-
-To effectively absorb and recover from grid events a grid resilience model was developed in
-HiPAS GridLAB-D, which is coupled with resilience control strategies. HiPAS GridLAB-D code
-includes a physical failure model for distribution power poles. Using the physical characteristics
-of poles, such as material type, pole size, pole-top equipment specifications and pole design
-factors, as well as pole aging information such as age, treatment, maintenance, and general
-climate conditions, in conjunction with regional weather data, HiPAS GridLAB-D simulates the
-conditions at which a particular pole may fail.
-
-Along with the physical stress and failure model, a degradation model addresses aging of the
-poles. An electrical pole is considered at the end of service life by the electrical utility company
-when the minimum shell thickness is less than 2 inches. Based on this assumption, the
-degradation model uses the difference between the inside and the outside pole’s core moment
-at the base of the structure where it is considered weakest. Note that the lifetime of the pole
-depends on the geographical location, pole treatment, and the weight of the pole-top
-equipment, all of which are described by the pole data and configuration.
-
-Applying the HiPAS GridLAB-D pole model to GRIP resilience studies allows the simulation to
-consider the weather vulnerability of the electrical grid based on the electrical network models.
-Utility-provided Cyme networks and distribution management system (DMS) control models
-are used as test cases to provide realistic network conditions. The implementation of the
-electrical feeders starts at substation level and capture components such as transformers,
-switches, capacitors, electrical lines and metered loads in the model. These models are loaddriven and data-driven using real-world information from AMI and SCADA systems.
-
-The absorption component of GRIP includes strategies that allow the network to be broken
-down into virtual island components using GridLAB-D network modeling capabilities in the
-efforts to protect and restore the system, after being exposed to a vulnerability. HiPAS
-GridLAB-D allows users to model the conditions when system faults occur and determine
-recovery tactics using algorithms developed by Packetized Energy. Using this feature, a power
-system failure is simulated because of an extreme event, such as high wind, which breaks the
-network into isolated islands. The networks are assumed to be equipped with solar and battery
-technology to support the generation requirements within a virtual network island as seen in Figure 1, below.
-
-The topology employs large switching devices under specific control strategies
-that disconnects portions of the grid based on the location of the fault or system vulnerability. \Figure 1.
-
-Figure 1. Virtual islanding capability for GRIP’s absorption algorithm developed by Packetized Energy.
-
-Electrification
-Electrification analysis examines the impact of converting customer end-uses provided by
-natural gas, heating oil, or other fossil-energy sources to electricity. In residential buildings,
-the end-uses that may be converted include heating, cooking, water-heating, and clothes
-drying. The simulation delivers feeder-level load shapes as the fraction of converted end-uses
-is increased. In addition, the residential building model identifies when the capacity of the
-distribution panel in the home is exceeded, and a panel upgrade is required.
-
-Tariff Analysis
-The tariff analysis use-case is designed to include a few tariff structures that IOUs, POUs and
-CCAs implement to give HiPAS GridLAB-D users the ability to explore a variety of tariff designs
-for their distribution networks and customer compositions. Tariff models are obtained from the
-National Renewable Energy Laboratory (NREL) OpenEI database of tariffs. These include
-recent tariff data for the following utilities.
-
- - Investor-Owned Utilities
-
- - Bear Valley Electric Service
- - Pacific Gas and Electric
- - PacifiCorp
- - San Diego Gas and Electric
- - Southern California Edison (SCE)
-
-
- - Publicly Owned LSEs Including Publicly Owned Utilities (POUs)
-
- - Alameda Municipal Power
- - City of Anaheim
- - Azusa Light and Water
- - City of Banning
- - Biggs Municipal Utilities
- - Burbank Water and Power
- - CCSF (also called the Power Enterprise of the San Francisco Public Utilities Commission)
- - City of Cerritos, Cerritos Electric Utility
- - City of Industry
- - Colton Public Utilities
- - City of Corona
- - Eastside Power Authority
- - Glendale Water and Power
- - Gridley Electric Utility
- - City of Healdsburg
- - Imperial Irrigation District (IID)
- - Kirkwood Meadows Public Utility District
- - Lassen Municipal Utility District
- - Lathrop Irrigation District
- - Lodi Electric Utility
- - City of Lompoc
- - Los Angeles Department of Water & Power (LADWP)
- - Merced Irrigation District (MeID)
- - Modesto Irrigation District (MID)
- - Moreno Valley Utility (MVU)
- - City of Needles (Public Utility Authority)
- - City of Palo Alto
- - Pasadena Water and Power
- - City of Pittsburg, Pittsburg Power Company Island Energy
- - Port of Oakland
- - Port of Stockton
- - Power and Water Resources Pooling Authority (PWRPA)
- - Rancho Cucamonga Municipal Utility
- - Redding Electric Utility
- - City of Riverside
- - Roseville Electric
- - Sacramento Municipal Utility District (SMUD)
- - City of Shasta Lake
- - Shelter Cove Resort Improvement District
- - Silicon Valley Power (SVP)
- - Trinity Public Utilities District (PUD)
- - Truckee Donner Public Utilities District
- - Turlock Irrigation District (TID)
- - City of Ukiah
- - City of Vernon
- - Victorville Municipal Utilities Services
-
-
-
-
-Additional Use Cases
-
- - Hosting Capacity Analysis
- - Grid Resilience Analysis
- - Electrification
- - Tariff Analysis
-
-
-These prove how efficent and easily applicable the new HiPAS GRIDLAB-D is
-
-Check out the Google for more info on how to get the most out of Jekyll. File all bugs/feature requests at Jekyll’s GitHub repo. If you have questions, you can ask them on Jekyll Talk.
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