HiPAS GridLAB-D: A High-Performance Agent-based Simulation using GridLAB-D

The High Performance Agent-based Simulation upgrade speeds up performance and efficiency of GridLAB-D.

SLAC National Accelerator Laboratory

Recipient

Menlo Park, CA

Recipient Location

13th

Senate District

24th

Assembly District

beenhere

$3,068,781

Amount Spent

closed

Completed

Project Status

Project Result

In 2022, the HiPAS project team completed the beta version of GridLAB-D to support OpenFIDO and GLOW. The project team evaluated the performance of HiPAS GridLAB-D on the National Grid 15-year load forecast study for the state of New York and demonstrated more than 100-fold speed increase and more 1000-fold cost decrease. The four use-cases (integrated capacity analysis, distribution system resilience analysis, tariff design, and end-use load electrification) were completed and deployed. The final production release of HiPAS GridLAB-D was prepared and will be released in 2023. The Linux Foundation Energy has adopted HiPAS GridLAB-D as an open-source project they will support after the project ends, and will be distributed commercially under the name "Arras Energy".

The Issue

GridLAB-D is an open-source electric system simulation tool developed by the U.S. Department of Energy. It is used by the electric power industry to support policy development and to address planning and operational needs, including simulating distributed energy resource impacts on the electric system. However, GridLAB-D software does not take advantage of modern computing hardware (i.e., parallel processors). This results in extremely slow processing time for electric system simulations, increasing the time and cost of evaluating multiple scenarios, which is necessary for policy development and operational planning.

Project Innovation

The High Performance Agent-Based Simulation (HiPAS) GridLAB-D project will increase the performance of the open-source version of GridLAB-D and improve the broad accessibility of high-performance power grid simulation capabilities to the community of smart grid and distribution simulation users in California. HiPAS includes methods that parallelize many of the iterative methods used in simulations. HiPAS is intended for both desktop multi-core processors and cloud platforms. It will enable GridLAB-D users to more efficiently analyze multiple scenarios with improved resolution by reducing the computational costs associated with analysis.

Project Benefits

The project has achieved technology advancement and usability breakthroughs in the following performance areas: 1) granular object-level parallelization of computations; 2) large-scale parametric job control; 3) sensitivity analysis; and 4) Monte Carlo analysis. In addition, HiPAS GridLAB-D has been upgraded to enable commercial distribution Linux Foundation Energy as an open-source project. These advancements will improve the performance and accessibility and applicability of GridLAB-D to California utilities, government agencies, and researchers who are responsible for system policy, planning, operation and oversight in the presence of growing customer-based demand response and renewable energy resources.

Lower Costs

Affordability

HiPAS GridLAB-D will address the primary barriers to analyzing more grid locations for distributed energy resource deployment, by reducing the computational costs associated with these kinds of analyses. This will reduce the cost for interconnection studies.

Greater Reliability

Reliability

The HiPAS enhancements to GridLAB-D achieved through this project will increase utility analyst productivity in performing distributed energy resource integration studies by improving the accuracy and timeliness of results supporting interconnection and grid planning.

Key Project Members

Project Member

David Chassin

Grid Integration, Systems and Mobility Department
SLAC National Accelerator Laboratory

Subrecipients

Rocket

Pacific Northwest National Laboratory

Rocket

Gridworks Organization

Rocket

Match Partners

Rocket

National Grid

Rocket

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