Improving Grid Reliability in the Face of Extreme Events
Heterogeneous Architectures
Heterogeneous architecture refers to hardware that, in addition to traditional processing units, also has hardware accelerators such as graphics processing units (GPUs). This architecture provides extra computational power for the computing-intensive task of modeling “stochastic” grid dynamics, which have random probability distributions or patterns that need to be analyzed statistically. ExaGO consists of applications designed to solve large-scale stochastic optimization (nonlinear problems), security-constrained optimization (resource scheduling), and multi-period optimization problems (grid infrastructure interdependencies).
Modeling the impact of variable generation energy resources on grid reliability would be an example of stochastic grid dynamics. This GPU architecture has the advantage of being able to process many pieces of data simultaneously, significantly increasing computing performance for modeling the behavior of complex systems. The platform has already demonstrated unprecedented levels of performance and scalability.
In testing, ExaGO simultaneously solved more than 3,000 instances of alternate current optimal power flow (ACOPF)—a critical system-level grid management calculation to balance real and reactive power—for a simulated 2,000-bus Texas grid in less than 10 minutes. This performance significantly exceeds that of current generation planning tools and enables grid operators to identify optimal responses to multiple simultaneous failures of grid components (known as N-k contingencies), such as those occurring during extreme weather conditions.
Putting the Technology to Work
So, what can grid operators do with a modeling platform like ExaGO? Much more than they could do with current generation tools, said Abhyankar.
ExaGO can be used to help manage the operational uncertainties from intermittent, distributed energy resources. The software can also be used to accurately assess a multitude of grid operating conditions for maintaining security and reliability or to mitigate frequency deviation during blackouts and other disruptive events. ExaGO can also be applied to optimize day-ahead and real-time power market operations.
Because ExaGO provides a complete and portable transmission grid modeling solution, transmission system operators can optimize their planning using more accurate life cycle estimates for grid assets, which represent billions of dollars in annual investment. Grid operators can also better prepare for extreme weather events, natural disasters, and potential cyberattacks by more accurately forecasting the impacts of those events on grid reliability in advance. These steps also include formulating the most effective emergency response options and identifying the best assets for frequency control to avoid broader, cascading failures of the power system.
“With a platform with these computational capabilities and modeling features in place, the potential applications and new use cases are extensive,” said Abhyankar. “Most importantly, with the ability to execute high-fidelity grid modeling and power flow analysis—quickly and at scale—grid operators are better able to keep the lights on when power grid reliability is threatened.”
ExaGO is available for general use through open-source licensing on GitLab.
