Advanced Grid Analytics: Making Smart Use of Smart Meter Data

Ifigeneia Stefanidou, Head of Product Management Grid Edge EMEA, Landis+Gyr

Forward-looking distributions system operators (DSOs) are already using grid analytics to optimize asset management, secure grid operation and achieve operational excellence. They are doing so by combining smart metering data (still mainly used for billing purposes) with information from their distribution system, opening up new possibilities for identifying voltage violations, predicting equipment failure and providing optimal strategies for grid development.

Advanced grid analytics solutions normally comprise a powerful enterprise platform and modular software packages designed to perform very specific analytical tasks. Such software modules typically provide user-friendly interfaces that offer graphical representations of live data trends.

Some examples of advanced grid analytics applications include those for asset management, distribution system monitoring, reliability planning and distributed energy resource optimization.

One of the key challenges for asset managers is to get a visualization of the assets distributed within their territory, and an accurate understanding of their condition. Geographical information systems (GIS) have traditionally been at the core of many utility asset registration and accounting processes, providing vital information about assets’ geo-spatial locations. However good these systems are, they often leave utilities with an incomplete view of their assets’ loading throughout the day, the week and year, and most utilities today cannot otherwise rely on conventional maintenance and replacement inspections for asset management. With advanced grid analytics utilities can be more proactive and efficient, particularly when the data they need are already at their disposal via smart meters and smart grid solutions.

Asset loading applications analyze real-load and production profiles from smart meters to calculate and visualize the power flow across an entire distribution system. This overview provides operators with insights on overloaded or over-dimensioned grid areas, enabling them to take decisions which have immediate positive impact on grid efficiency and reliability. This use of data analytics ultimately helps utilities to optimize their grid investment strategies.

Grid monitoring applications provide operators with system-wide voltage monitoring, based on dynamic data from smart meters, sensors, and geographical information systems. Meter events can be used as alarms for system outages and enable fast restoration and, therefore, increased customer satisfaction.

Reliability planning applications are designed to enable the analysis of network outages and enhance grid reliability. They draw upon information from historical outage events to provide the necessary data for optimizing grid investments and maintenance decisions under specific budget constraints.

Last but not least, distributed energy resources optimization applications are being used to determine the optimal amount of distributed generation that can be integrated into a given network without violating power quality standards.

In each of these cases, existing smart meter data are being captured and leveraged to support utilities’ investment strategies, while also introducing control schemes (flexibility management and new grid tariffs, for example). The use of this technology is an important step forward for the industry and a logical extension of smart grid solutions. Moreover, it supports the wisdom of the phrase coined by famous management consultant Peter Drucker: ‘You can’t manage what you can’t measure.”