Data Analytics for Smart Grid Development and Deployment

The smart grid is leading the power industry into a data and analytics boom as its implementation phase gives way to its value phase, according to Christine Richards, senior analyst with Energy Central's Utility Analytics Institute.

Richards defined the decade from 2000 to 2010 as the smart grid's development phase, the five-year period from 2007 to 2012 as the infrastructure, or implementation phase and the decade from 2010 to 2020 as the value phase.

In the value phase, grid optimization, intelligent asset management and the integration of renewable resources and electric vehicles will be achieved by transforming raw data into actionable intelligence for all business and operational units of a utility.

In a recent webcast on the topic, participants clearly connected the traditional and new sources of data, how that data is processed for integrity ("data you can trust" ), how it is mashed-up with other data to create myriad insights and how those insights can be presented to enable evidence-based decisions, often in real time.

The traditional basis for data creation and interpretation for energy companies typically begins with a geographic information system (GIS), an outage management system, SCADA (supervisory control and data acquisition) substations and field devices. With the advent of advanced metering infrastructure (AMI) and other grid modernization steps, data sources will multiply, become richer and the value of analytics increased accordingly.

Raw data generation is transformed by analytics into useful guidance for decision-making related to multiple areas "Smart Grid Deployment " which major energy companies across the country plan to use to provide: customer empowerment, outage/distribution management, condition-based asset maintenance and the integration of distributed renewable energy sources.

The proper analysis of data will transform the utility's role from reacting to historical events and outcomes to predictive, proactive stances that avoid outages or power quality issues.

One difficulty in conveying new, data-enabled capabilities, of course, is describing how such things get done in terms of systems. Leveraging Data from the "Plant Floor" to decision maker via discrete data with limited value and culled to reached the status of actionable information with enterprise value in formats useful, visual representations (visualization) such that the data generation to outputs as integrated reports, dashboards, analytics and answers to self-service queries.

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Tags: ., Analytics, Data Sourcing, Predictive Analytics, Visualization, smart grid


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