enabling demand response, in which utilities or even individual customers adjust their electricity usage in real time to keep the broader system in balance—a crucial, ongoing challenge to maintaining reliability. This balance is achieved through careful scheduling and management of power plants—the supply side of the equation—with substantial reserves in place in case of unanticipated changes.
Greater control of the demand side of the equation gives electricity system operators more flexibility to handle the unexpected, and to integrate more renewable energy from wind and solar plants, whose output can change suddenly with a turn in the weather (though forecasting has come a long way, making this variability less severe than it once was.)
As part of the smart grid project, a computer simulation took data from independent power producers, the Bonneville Power Administration (BPA), and other sources to create a signal that reflected different scenarios on the Northwest grid. These simulations included a rapid increase in wind power generation from an approaching storm front, or an unexpected loss of power from the Columbia Generating Station, the region’s sole remaining nuclear plant.
The goal was to see whether the incentive signal behaved appropriately, going down in value as power becomes abundant, and going up when it grows scarce. Utilities could respond to the signal by increasing electricity demand—charging up batteries with the low-cost wind power, say—or reducing electricity demand temporarily by turning down customer thermostats, for example—to help maintain that balance.
“We were able to validate that the transactive control technique basically works,” Melton said.
The signal provided the appropriate information for several scenarios. In others, such as anticipated hot or cold days, when energy use spikes for air conditioning or heating, the signal didn’t change. That’s also expected behavior because the BPA, which operates the regional transmission grid, anticipates those conditions and plans accordingly. “There was no real need to take short-term actions,” he said.
For the most part, demand response technologies focus on quickly reducing demand when power prices go high, indicating a shortage of energy. There are fewer opportunities to quickly increase demand to soak up excess, low-cost power on the system. “This is a challenge for the industry going forward to rethink some of the relationships and business models to be able to be flexible in either direction,” Melton said.
As part of the project, IBM did a simulation in which 30 percent of demand on the electricity system could respond to the incentive signals. In that scenario, the Pacific Northwest could expect a 4 percent reduction of peak power costs.
But reaching 30 percent penetration of demand-response technologies in actuality would require lots more investment in smart transformers, controllable thermostats, and other equipment. Another challenge will be articulating what that 4 percent savings means in dollar terms to entities like BPA and its utility customers, and agreeing on a financial arrangement to recognize the benefit and make the technologies a viable investment for the utilities and their customers.
“This is true anywhere you want to deploy these new technologies,” Melton said, referencing similar efforts in California and New York aimed at creating markets to integrate renewable energy and provide electricity customers more choice. “At the end of the day, in all of these discussions, we have to be able to clearly identify the value streams and what they’re worth to all parties so we can figure out the finance side of this. We have not done that yet.”
While the smart grid demonstration stopped collecting data last August, Melton said there are efforts afoot to keep some parts of the transactive control signals alive in the region for further research locally.
