The LIINES is happy to announce that ISA Transactions has accepted our recent paper entitled: Event Triggered State Estimation Techniques for Power Systems with Integrated Variable Energy Resources.  The paper is authored by Reshma C. Francy, Prof. Amro M. Farid and Prof. Kamal Youcef-Toumi.

In recent years, we have had the opportunity to contribute to two large studies that present visions of the future smart grid:  The MIT Future of the Electric Grid Study, and the IEEE Vision for Smart Grid Controls: 2030 and Beyond.  Both of these works emphasized that in order for the future grid to be truly smart, it has to be responsive, dynamic, adaptive and flexible.  This is the case even when highly variable renewable energy sources sources are plugged in.   The first step in achieving this vision is having greater “situational awareness” — knowing what is going on when and where in the grid.

For decades, state estimation has been a critical technology in achieving such situational awareness for power system operators.   Over time, it has become quite the mature technology. But, the integration of renewable energy changes all that.  Not only does it introduce rapidly changing behavior into the grid; but it also does so in the low voltage distribution system where state estimation is not usually applied.   The conventional solution is to not just monitor the grid faster but also for the entire power grid all the way down to the low voltages.  That means that not only do all the power grid’s measurements have to be gathered from across power grid’s geography but they also have to computed at an ever faster rate.   This is an exponentially growing problem  — hardly a solution befitting a future “smart” grid.

This paper seeks to address these two requirements in a practical way.   The idea is to use a concept called “event-triggering”.  It takes advantage of the fact that the wind doesn’t always blow and the sun doesn’t always shine.  When local power grid conditions are highly variable, say at a wind turbine or solar panel, a “trigger” will kick in telling the state estimator to run.  But when the power grid is relatively stable, the new state estimator will use a simplified linear approach based upon the last time the full state estimator was run.  Relative to traditional state estimation, this simple solution has been shown to reduce computational time by 90% in numerical case studies.

While ultimately, in the long term, the smart grid will require a fundamental “rethink” in how to approach state estimation, monitoring, and situational awareness, this solution demonstrates how traditional state estimation techniques can be enhanced for future smart grid applications.

A full reference list of smart grid research at LIINES can be found on the LIINES publication page: http://amfarid.scripts.mit.edu