To fully embrace wind and solar power, grid operators need to be able to predict and manage the variability that comes from changes in the wind or clouds dimming sunlight.
One solution may come from a $2-million project backed by the U.S. Department of Energy that aims to develop a risk dashboard for handling more complex power grid scenarios.
Grid operators now use dashboards that report the current status of the power grid and show the impacts of large disturbances—such as storms and other weather contingencies—along with regional constraints in flow and generation. The new dashboard being developed by Columbia University researchers and funded by the Advanced Research Projects Agency–Energy (ARPA-E) would improve upon existing dashboards by modeling more complex factors. This could help the grid better incorporate both renewable power sources and demand response programs that encourage consumers to use less electricity during peak periods.
“[Y]ou have to operate the grid in a way that is looking forward in time and that accepts that there will be variability—you have to start talking about what people in finance would call risk,” says Daniel Bienstock, professor of industrial engineering and operations research, and professor of applied physics and applied mathematics at Columbia University.
The new dashboard would not necessarily help grid operators prepare for catastrophic black swan events that might happen only once in 100 years. Instead, Bienstock and his colleagues hope to apply some lessons from financial modeling to measure and manage risk associated with more common events that could strain the capabilities of the U.S. regional power grids managed by independent system operators (ISOs). The team plans to build and test an alpha version of the dashboard within two years, before demonstrating the dashboard for ISOs and electric utilities in the third year of the project.
Variability already poses a challenge to modern power grids that were designed to handle steady power output from conventional power plants to meet an anticipated level of demand from consumers. Power grids usually rely on gas turbine generators to kick in during peak periods of power usage or to provide backup to intermittent wind and solar power.
But such generators may not provide a fast enough response to compensate for the expected variability in power grids that include more renewable power sources and demand response programs driven by fickle human behavior. In the worst cases, grid operators may shut down power to consumers and create deliberate blackouts in order to protect the grid’s physical equipment.
One of the dashboard project’s main goals involves developing mathematical and statistical models that can quantify the risk from having greater uncertainty in the power grid. Such models would aim to simulate different scenarios based on conditions—such as changes in weather or power demand—that could stress the power grid. Repeatedly playing out such scenarios would force grid operators to fine-tune and adapt their operational plans to handle such surprises in real life.
For example, one scenario might involve a solar farm generating 10 percent less power and a wind farm generating 30 percent more power within a short amount of time, Bienstock explains. The combination of those factors might mean too much power begins flowing on a particular power line and the line subsequently starts running hot at the risk of damage.
Such models would only be as good as the data that trains them. Some ISOs and electric utilities have already been gathering useful data from the power grid for years. Those that already have more experience dealing with the variability of renewable power have been the most proactive. But many of the ISOs are reluctant to share such data with outsiders.
“One of the ISOs has told us that they will let us run our code on their data provided that we actually physically go to their office, but they will not give us the data to play with,” Bienstock says.
For this project, ARPA-E has been working with one ISO to produce synthetic data covering many different scenarios based on historical data. The team is also using publicly available data on factors such as solar irradiation, cloud cover, wind strength, and the power generation capabilities of solar panels and wind turbines.
“You can look at historical events and then you can design stress scenarios that are somehow compatible with what we observe in the past,” says Agostino Capponi, associate professor of industrial engineering and operations research at Columbia University and external consultant for the U.S. Commodity Futures Trading Commission.
A second big part of the dashboard project involves developing tools that grid operators could use to help manage the risks that come from dealing with greater uncertainty. Capponi is leading the team’s effort to design customized energy volatility contracts that could allow grid operators to buy such contracts for a fixed amount and receive compensation for all the variance that occurs over a historical period of time.
But he acknowledged that financial contracts designed to help offset risk in the financial market won’t apply in a straightforward manner to the realities of the power grid that include delays in power transmission, physical constraints, and weather events.
“You cannot really directly use existing financial contracts because in finance you don't have to take into account the physics of the power grid,” Capponi says.
The team’s expertise spans multiple disciplines. Bienstock, Capponi, and their colleague Garud Iyengar, professor of industrial engineering and operations research, are all members of Columbia’s Data Science Institute. The project’s principal investigators also include Michael Chertkov, professor of mathematics at the University of Arizona, and Yury Dvorkin, assistant professor of electrical and computer engineering at New York University.
Once the new dashboard is up and running, it could begin to help grid operators deal with both near-term and long-term challenges for the U.S. power grid. One recent example comes from the current COVID-19 pandemic and associated human behavioral changes—such as more people working from home—having already increased variability in energy consumption across New York City and other parts of the United States. In the future, the risk dashboard might help grid operators quickly identify areas at higher risk of suffering from imbalances between supply and demand and act quickly to avoid straining the grid or having blackouts.
Knowing the long-term risks in specific regions might also drive more investment in additional energy storage technologies and improved transmission lines to help offset such risks. The situation is different for every grid operator’s particular region, but the researchers hope that their dashboard can eventually help level the speed bumps as the U.S. power grid moves toward using more renewable power.
“The ISOs have different levels of renewable penetration, and so they have different exposures and visibility to risk,” Bienstock says. “But this is just the right time to be doing this sort of thing.”
Jeremy Hsu has been working as a science and technology journalist in New York City since 2008. He has written on subjects as diverse as supercomputing and wearable electronics for IEEE Spectrum. When he’s not trying to wrap his head around the latest quantum computing news for Spectrum, he also contributes to a variety of publications such as Scientific American, Discover, Popular Science, and others. He is a graduate of New York University’s Science, Health & Environmental Reporting Program.