Smart Grid: How Theft Analytics Helps Utilities Protect Assets

Electricity theft includes diverting power currents and tampering with the meters that measure customer electricity consumption.  While energy diversion and tampering have been increasing along with the world’s economic challenges, the evolution of smart grid technologies fortunately has enabled better ways to proactively identify potential diversion problems. In addition, smart meters and grid devices provide the type of data that can be leveraged by back-office analytics and software techniques to detect theft and support the next steps of revenue protection— prosecution and payment collection.

This Accenture point of view introduces a capability framework for utilities to consider in the pursuit of achieving high performance. We review smart grid and back-office analytics maturity against the types of diversions that can be identified and benefits captured based on various smart grid deployment levels.

The revenue diversion analysis capability model discussed in this point of view starts with simple analytics on customer, account and billing data and progresses through analytics based on data from smart grid feeder and transformer meters. Finally, there is a way to use geographic information systems (GIS) and network visualization to apply geospatial analytics to the problem of energy diversion.

The five levels of the capability model presented in this point of view correlate the required level of maturity in grid infrastructure, smart metering, modeling of distribution network connectivity and back-office capabilities to levels of maturity in energy diversion identification and analysis. The framework can also be used to develop a road map for a revenue diversion analytics solution aligned with smart meter and smart grid deployment activities, as the model demonstrates what kind of analytics can be achieved based on the technology a utility has deployed.

Each utility must calculate its own return on investment for smart grid investments, such as feeder or transformer meters or a mix thereof to augment advanced metering infrastructure (AMI) data for advanced theft analytics. Looking at the five-level continuum of analytic capabilities detailed in this point of view, utilities can extract the most benefit from the diversion solutions described at level 4 and level 5, which go beyond AMI data to include grid device information in the analytics mix. These solutions truly harness the power of grid equipment to make theft analytics smart.

Many of the theft mitigation opportunities presented in this paper require investment in distribution network model management, smart grid infrastructure and data management capabilities that many utilities have not yet made—but which are very relevant for a distribution smart grid. Adding theft analytics benefits to the business case would only help utilities further cost justifies these expenditures.