Data Management Q&A Series: Looking at the Business Benefits

What are the top high-value business problems that can benefit from Smart Grid analytics?

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Almost every aspect of a utility’s operation – planning, asset management distribution operations, customer care and billing can benefit greatly from the application of Smart Grid analytics to Smart Grid deployments. Some of this will be predicated on each utility and its business case priorities for Smart Grid deployments. Since a Smart Grid deployment necessitates a change in how a utility does its business, other areas may come up simply as a function of the deployment activity. Here are a few examples:

Asset management

·         Ability to match asset parameters with observed readings and expected load patterns for effective asset utilization (increased reliability), perform condition-based and predictive maintenance (postpones expensive replacements), plan future enhancements and grid optimizations (enables decision making).

·         Including real-time status/events and GIS data with this enables visualizations that provide additional insights for managing distributed grid assets.

Demand Response

·         Analyze customer load profiles, consumption patterns, demographic and weather information to match customers with the DR programs they are likely to participate in and to design new DR programs that are better aligned to customer demand patterns.

·         Also determine the success of DR programs in load shedding and load shifting by analyzing customer behaviors and patterns during DR events. Being able to do this can help utilities manage and predict load better and reduce the need to purchase/generate costly ‘peak’ power.

Billing analysis

Analyze profiles (e.g. restaurants, hospitals) and usage patterns of C&I customers and provide them information to help them decide the best rate for their type of load. Use this information to also create new value-added offerings (e.g. Energy Management services) for customer groups.

Customer related

·         Integrate deployment data (from device installers), provisioned devices in the backend systems (MDM, Headend, DRMS, etc.) with asset management data to create dashboards for tracking progress of device installations and provisioning.

·         Ability to correlate reads, alarms and customer profile information to detect theft and revenue leakage.  For example, use frequent power off/power on meter alarms with consecutive reading gaps and/or static reads from the premise to flag a potential theft event.

·         Use consumer usage patterns and post DR event analysis to provide targeted education to customers on energy efficiency and conservation goals.

Dynamic pricing

Use customer consumption patterns, weather and demographic data to segment customers and offer dynamic pricing programs and tailored energy efficiency initiatives.

Distributed generation management

Ability to analyze the combined effect of existing generation resources and distributed energy resources (such as intermittent renewable power sources) to determine when/if peaking generation plants or virtual power plants need to be brought online. Using this information in near real-time provides the ability to switch between distributed generation sources based on demand so that utilities achieve the lowest cost of power while reducing the carbon footprint.

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