Advanced Computing in the Age of AI|Wednesday, January 29, 2020
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Data Platforms Supplying ‘Smarts’ to the Power Grid 

Software is defining and redefining everything from datacenters to the energy infrastructure delivering the power to run IT infrastructure.

Software-defined power distribution that also incorporates predictive analytics to forecast future demand debuted late last year when the Dutch company Eneco Group rolled out what it claims is the first software-driven power plant. Teamed with AutoGrid Systems, an analytics provider for the energy sector based in Redwood City, Calif., Eneco deployed a platform that allows it to manage and trade energy assets on the wholesale market.

Meanwhile, mainstream analytics vendors are partnering with other energy companies on other distribution models, including virtual power plants.

Eneco's software-driven power grid uses AutoGrid's "predictive controls" technology to integrate what the industry calls "distributed energy resources," then trades those energy assets on fluctuating wholesale energy markets. Power resources include customer-owned combined heat and power units, energy storage systems, solar inverters, HVAC systems, load control switches and electric vehicle chargers (when plugged into smart grid, electric car batteries also can be used as a energy storage resource).

The partners said the smart power grid serves as a platform for a 100-megawatt power plant able to react in real-time to power demand on Dutch wholesale electricity markets. By combining power from a variety of distributed power generation assets, many of them from renewable sources, the software-defined power plant can trade on energy markets around the clock. That means, for instance, that the stored energy from renewables can be sold on the wholesale market to help meet peak demand.

AutoGrid Systems said it is also providing software services to existing customers such as Florida Power & Light, Southern California Edison, Bonneville Power Administration, local utilities and Germany's E.ON.

The software-defined power plant represents perhaps the most ambitious deployment of AutoGrid's predictive controls technology. The platform is designed to process petabytes of data streaming from millions of connected energy assets. The data is used to predict and optimize supply and demand patterns across smart energy grids, the company said this week.

As the "smart grid" and the Internet of Things (IoT) emerge, analytics vendors are starting to adjust their data platforms to help utilities and energy companies manage power issues like integrating renewables into the power grid. Those distributed energy resources can be tapped on wholesale markets during periods of peak demand.

For example, enterprise Hadoop leader Hortonworks (HDP: NASDAQ) announced a deal last fall with Open Energi to develop a "virtual power station" based on the energy firm's power demand response technology. London-based Open Energi said it would use Hortonworks' data platform to manage smart grid operations that are being accelerated by IoT sensor network deployments.

The energy company's dynamic power demand technology aggregates power consumption data from across the grid to create the equivalent of a power station. Combined with Hortonwworks' Hadoop distribution, the virtual power station can adjust demand to meet supply rather than simply adjusting supply up or down, the partners said.

"Data is the driving force behind a smarter energy grid and with access to more accurate technology, it is now possible to build a far more detailed picture of energy usage," Andy Leaver, Hortonworks' vice president of international operations, noted in a statement announcing the partnership.

About the author: George Leopold

George Leopold has written about science and technology for more than 30 years, focusing on electronics and aerospace technology. He previously served as executive editor of Electronic Engineering Times. Leopold is the author of "Calculated Risk: The Supersonic Life and Times of Gus Grissom" (Purdue University Press, 2016).

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