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By Jesse Berst

That's why it has been gratifying to see Silicon Valley jump so successfully into the smart grid fray. As part of our regular "Discovery Showcase" series, I bring you

If you would like to get in touch with the firm, ping me for an introduction at jesse.berst@smartgridnews.com.

A Silicon Valley smart grid startup is gunning to lower the cost of demand response by 90% while increasing efficiency 30%.

Palo Alto, CA-based AutoGrid was founded by Stanford University professor Amit Narayan. Its most recent hire is smart grid pioneer Chris Knudsen. Knudsen, who formerly ran PG&E's Technology Innovation Center, joins as chief technology officer.  AutoGrid has already attracted marquee investors including Foundation Capital, Voyager Capital and Stanford University. What's more, it is leading a $4-million grant project funded by DOE and the California Energy Commission to investigate "highly dispatchable and distributed demand response for the integration of distributed generation."

The core mission

Amit, Chris and company are tackling what may be the smart grid's hardest problem – how to manage it as an integrated system, not just a series of adjacent, siloed apps that occasionally swap data.

Analytics is the firm's core differentiator – ultra-fast analysis of ultra-large data sets. In fact, its stated mission is to "provide a new generation of software analytics  to enable a sustainable energy infrastructure of the future."

Other companies have also jumped into the grid analytics fray, but AutoGrid is also grappling with the physics of the grid.  In this sense, the company is part of the move to blend IT and OT (to blend data from information technology and operations technology). As a first step, the company began by building a grid simulation system. It built that simulator on top of best practices pulled from places such as Pacific Northwest National Laboratory (its free GridLAB-D tool), Lawrence Berkeley National Laboratory, and Columbia University. And from the financial industry, which has decades of experience grappling with Big Data.