Enter the 2019 R&D 100 Awards!
The Dynamic Contingency Analysis Tool is a 2018 R&D 100 Award winner. All of the R&D 100 Awardees were announced at the R&D 100 Awards Gala held in Orlando, Florida on Nov. 16, 2018.
The R&D 100 Awards have served as the most prestigious innovation awards program for the past 57 years, honoring R&D pioneers and their revolutionary ideas in science and technology.
Submissions for the 2019 R&D 100 Awards are now being accepted. The deadline to submit an entry for the 2019 R&D 100 Awards is June 17, 2019. Any new technical product or process that was first available for purchase or licensing between January 1, 2018 and March 31, 2019, is eligible for entry in the 2019 awards.
Start or complete your entry now: visit: https://rd1002019.secure-platform.com/a For more info: www.rd100conference.com/awards
A natural or malicious event that causes an outage to one part of a power grid is harmful enough, but when failure in one spot affects other parts of the grid in a domino effect, the resulting blackout can be disastrous both financially and with regard to public safety. Simulations are needed to understand how power grids will react to disruptions, and operators can benefit from testing out methods for stopping or minimizing the harm of cascading failures in a safe virtual environment.
Researchers at Pacific Northwest National Laboratory noticed a lack of tools for simulating dynamic operations during cascading failures, with existing tools only analyzing steady-state operations. As cascading outages take place, failure events can occur at varying fast and slow speeds, affecting grid operations differently at different times. Newer developments in energy technology, such as the increased use of renewable energy sources, and higher transmission speeds, also make power grids more dynamic.
Taking recommendations from various stakeholders, including the IEEE Working Group of Understanding, Prediction, Mitigation and Restoration of Cascading Failures (CFWG), and the Electric Power Research Institute (EPRI), PNNL researchers developed a tool that combines both dynamic and steady-state analysis to more accurately predict the progression of cascading events, and better recommend fixes for stopping or mitigating the outage. The Dynamic Contingency Analysis Tool (DCAT) is the first tool of its kind with this hybrid analysis capability, and was selected for an R&D 100 Award last fall.
“Cascading events should be simulated as a combination of steady-state and dynamic processes to accurately depict slower and faster phases of blackout development and their sequence,” said Nader Samaan, PNNL power systems research engineer and principal investigator for the DCAT project in an interview with R&D Magazine. “In the current industry practice, dynamic and steady-state simulations are usually conducted in sequence, where the dynamic simulations follow the steady-state runs. This is not an adequate approach to simulate cascading outage sequences.”
DCAT is designed so that it can be used on a laptop or desktop, or with a high-performance computer system. In an HPC environment, DCAT runs up to 100 times faster than traditional analysis tools, according to its developers.
The tool screens power grids for weak spots, and operators can run simulations to see how the grid would respond to an extreme event such as a hurricane, tornado, wildfire or malicious tampering. DCAT checks projected grid activity in five-second iterations, determines when a cascading failure could be halted and makes suggestions for stopping or minimizing the outage.
“DCAT then provides detailed information on protection actions and other potential corrective actions. Then, the user can use this information to test different preventative and corrective actions and choose the most effective one,” Samaan said.
Developing this first-of-its-kind tool was not without challenges, Samaan explained.
“The transition between steady-state and dynamic analysis required the development of several new modules to be added to commercial planning tools,” he said. “Modeling of corrective actions as a large optimization problem was a challenge in terms of adjusting objective functions, constraints, and using the right solver.”
DCAT uses the Python computer code and can be run on industry planning tools like Siemens’ PSS®E and General Electric’s PSLF. Integrated in the tool is a General Algebraic Modeling System (GAMS) based corrective action module developed by PNNL, which optimizes the recommended fixes for weaknesses and disruptions.