Imagine you’re driving your car at 70 mph on a newly paved, flat, straight freeway. Dawn is breaking and there are very few other vehicles on the road. You have a ten-mile stretch of road just like this ahead of you and you’re confident you’ll arrive at your destination on time. It feels safe and predictable.
Now imagine you’re driving that freeway under the same conditions with your eyes closed. To stay on-track, you open your eyes every two seconds and then close them again. During that quick blink, you check your speed, your location in your lane, and scan for obstructions, adjusting your accelerator and steering accordingly.
Scary, right?
Luckily, this is an imaginary scenario, because getting feedback on your driving once every two seconds is woefully inadequate when driving at 70 mph. It’s unlikely you’d maintain the same confidence around safety and timing compared to driving with your eyes open full-time. Plus, what happens when you introduce variability like random potholes, reckless racing drivers or slick patches?
One could draw a parallel to this scenario with the way the electrical power grid is being operated, where feedback on the grid’s condition is provided once every two seconds. On the old grid of yesteryear, this would have been adequate, since grid disruptions were minor and power generation was easily controlled at a singular power generation facility. Today’s grid is significantly different. It is dynamic, supported by distributed and intermittent energy resources such as rooftop solar and has unpredictable and mobile loads due to elements such as electric vehicle charging.
Today’s grid operators are challenged with a dynamic grid with rapidly changing conditions. To maintain its stability, operators require both real-time visibility into what’s happening on the grid, and equally rapid control over the inverter-based assets that can respond quickly to address power and frequency fluctuations.
The tools to manage today’s dynamic grid exist today. Among the most critical components are high-speed sensors, such as phasor measurement units (PMUs), coupled with a high-speed controller that can process the data as rapidly and frequently as it is receiving it.
PMUs can be installed and are often already in place on transmission lines, where they collect and transmit data based on the magnitude and phase angle of the sine waves found in electricity, synchronized with a GPS time stamp. PMUs have a high sampling rate and can provide estimates of the magnitude and angle of a 60Hz AC signal every 16.6 milliseconds.
Because a PMU can generate a vast quantity of data, its benefits are best realized when it is paired with a sophisticated controller. A controller that uses artificial intelligence to analyze and interpret the data leverages algorithms that efficiently filter the data, parsing data into usable insights that enable the controller to assess grid health and dispatch the assets under its control as needed to respond to grid fluctuations.
Grid operators are at the mercy of the data they are provided, and if that data is the equivalent of only opening one’s eyes every two seconds, the stability of the grid is in constant jeopardy.
For a technical dive into PMUs and how they work, download our white paper.