software analytics for complex electrical power systems began about six years ago, CEO Mark Ascolese told me. After developing a computer-aided design program (CAD) needed to develop fail-safe power systems for FAA flight control facilities, Ascolese said EDSA realized its software also could be used to model the optimal operating status for every component in a complex electric power system. The software can be used by grid operators to predict outages and other problems by pinpointing devices that show deteriorating performance. Last month, EDSA said it is collaborating with Viridity Energy of Conshohocken, PA, to develop real-time “master controller” software to operate a campus-wide electric power “microgrid” at the University of California, San Diego. UCSD’s microgrid includes two 13.5 megawatt gas turbines, a 3 megawatt steam turbine and a 1.2 megawatt solar-cell installation that together supply 82 percent of the annual power needs for the 1,200-acre, 450-building campus.
—Zementis: The San Diego-based software analytics startup—which will be presenting at tomorrow’s Xconomy forum on smart energy—announced last October that it was working with Virginia-based defense contractor SAIC to develop real-time predictive analytics technologies that utilities can use to identify power grid components at a high risk of failure. By predicting the likelihood of failure, grid operators can prevent power outages by dispatching maintenance crews to replace components before they fail. Zementis CEO Michael Zeller told me the six-year-old company has turned its attention to potential applications in the energy sector after developing its core technology for use in analyzing and predicting outcomes in a host of financial and online merchant applications. “What we do is take models developed in any open source or commercial data mining tool, and integrate them into commercial production systems,” Zeller said.
—Detectent: Based in Escondido, CA, privately Detectent initially began six years ago as a research project when ConEdison asked founder Mike Madrazzo to analyze the utility’s customer billing data for signs of energy theft, i.e. identify customers who had illegally tapped into the utility’s power grid. Since then, spokesman Wayne Willis tells me the self-funded company has expanded its capabilities to help analyze the enormous amount of data being generated by smart meters and a utility’s advanced metering infrastructure (AMI). Detectent’s technology can be used to help a utility analyze how customers using their energy—whether a customer is running a high-energy pool pump or air conditioner in mid-day, for example, and to formulate recommendations to shift the time of use or adopt more energy-efficient alternatives. “We just don’t think people will integrate the information they get from their smart meters,” Willis says. “But we do think they will integrate the information and advice they get from their utility.”