Bonneville Power Uses Analytics to Manage Demand

Among the challenges faced by electric utilities, especially during the summer cooling season, is managing peak demand while figuring out how to integrate intermittent power sources into the grid. Increasingly, big data analytics tools are being applied to the emerging smart grid as a way to gauge demand while improving grid reliability and lowering costs.

The latest example comes from the Bonneville Power Administration, the U.S. Energy Department agency that oversees power distribution in the Pacific Northwest. AutoGrid Systems, a data analytics vendor to the energy sector, said this week that Bonneville engineers are using its demand response management tool to find new ways to anticipate peak demand and capacity.

AutoGrid (Redwood City, Calif.) said the power agency has been using the tool since February to schedule and conduct more than 20 demand response events ranging from 18 to 28 megawatts of power. The result was a savings of more than 500 megawatt-hours, the analytics company claimed. (A megawatt-hour is equivalent to 1,000 kilowatts used continuously for one hour.)

Bonneville also uses the analytics tool for a power aggregation demonstration designed to identify ways to manage and balance sudden swings in capacity and demand such as increased power from wind generation. Utilities have long been looking for ways to incorporate new energy sources into the emerging smart grid, especially solar, wind and other alternative sources.

The Bonneville Power Administration was formed in 1937 to manage and market wholesale electricity generated the hydroelectric dams in the Columbia River basin. It has since added electricity from nuclear power plants. Its 142 utility and 490 transmission customers provide power to Idaho, western Montana, Oregon, Washington and parts of California, Nevada, Utah and Wyoming.

The agency is now looking for ways to manage new capacity from intermittent renewable energy sources ranging from wind, solar and thermal. Emerging analytics tools could also help energy administrators like Bonneville defer expensive investments in new power plants.

Hence, AutoGrid cited estimates from market researcher Navigant Research forecasting revenue from demand response tools like its Energy Data Platform could grow from $1.6 billion in 2014 to $9.7 billion by 2023.

As the smart grid emerges, analytics tools promise to play a greater role in managing peak demand while boosting “load-shedding capacity” during peak energy generation periods.

The Bonneville demonstration project authorizes energy aggregators to identify demand response tools. Under the program, municipal utilities, power cooperatives and other private, commercial and industry energy generators would use the tools to manage loads across their service territories.

In one such demonstration, demand response events were scheduled and calibrated to shed an energy load in 10 minutes or less, AutoGrid said.

Along with planning and executing the grid management events, Bonneville managers said its demand response team uses the demonstrations to gather real-time and historical performance data used to tweak future demonstrations of smart demand response and capacity shedding.

The demonstration also assessed the ability of AutoGrid’s demand management system to handle multiple demand response events, including schedule events with as little as 10 minutes of advance notice to participants. It also gauged the tool’s ability to monitor demand response exercises in real time while visualizing load shedding on a minute-by-minute capacity, the company said.

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