January 31, 2018

Leveraging the potential of AMI data in Asset Management

ThinkstockPhotos-522152858_Highlight.jpgManaging distribution assets is a key consideration for utilities, as they look to maximize the life of existing and new asset investments. With Advanced Grid Analytics, the AMI data can be harnessed to help utilities make sound strategic investment decisions.

In addition to managing aging assets, utilities are exploring ways to reliably integrate large quantities of distributed energy resources (DER) into the grid. The level of complexity in the energy grid increases also as a variety of residential loads such as residential photovoltaics (PVs), electric vehicles (EVs), air conditioners, water heaters, washers and dryers are increasingly being connected to the network. In order to efficiently manage their assets in the distribution network, utilities need new tools and processes in place.

Traditionally, distribution asset management has consisted of placing grid components in the field and replacing them after they have failed. With the deployment of AMI, utilities have started to evaluate the value of smart metering data beyond the primary business case of gathering consumption data and automate billing. With also asset management business cases in mind, utilities have found the potential of AMI data in monitoring the condition of assets in near-real time, in visualizing actual loading conditions, and in applying advanced analytics to optimize asset lifecycles and increase gird efficiency.

By utilizing grid analytics, utilities can leverage asset management benefits from their AMI network by gathering data from grid-edge sensors and various source systems, and analyze the data to get a better understanding of the asset operational behavior. They can use analytics applications to detect outages faster, predict outages, plan optimal reliability investment projects, and better manage the impact of renewables assets.

Landis+Gyr Advanced Grid Analytics – enabling data driven asset management 

The core of the data driven asset management strategy is the utility’s ability to both visualize their distribution system and run asset management scenarios with high-level granular data derived from actual system conditions. Utilities can then use the results of this analysis to develop, test, and justify asset management projects. With Landis+Gyr Advanced Grid Analytics, the data from Geographical Information Systems (GIS) can be merged with operational data from smart meters and sensors to improve a utility’s ability to geo-spatially visualize and analyze asset performance in the field. 

The Advanced Grid Analytics applications provided by Landis+Gyr visualize voltages in the entire network, helping to identify problem areas. With the power flow calculations, the utility can have an accurate picture of the currents and voltages at all points of their network and thus identify overloaded and underloaded assets to take corrective actions and to avoid outages and losses. Furthermore, they can simulate the effect of adding new elements, for example solar panels, in the network and understand the impact on the neighboring assets. Other use cases focus on detecting potential fraud, identifying customers who contribute the most to the peaks of consumption and determining plan of action to improve the network reliability.

AGA user-friendly interface__Multiple level visualisation of analytics and power flow results.jpg

AGA user-friendly interface: Multiple level visualisation of analytics and power flow results

Proving the power in Liechtenstein

In its project with Liechtensteinische Kraftwerke (LKW), Landis+Gyr works together with the customer to pilot  the potential of grid analytics in voltage visualization, integration of distributed energy resources and optimizing asset loading and, thus, maintenance investments in LKW’s network. The Advanced Grid Analytics software applications allow the utility to visualize and analyze grid loading, including load flow on distribution substations, power lines, fuses and other grid components based on real load profiles of consumers or prosumers. This enables a well targeted investment planning in the distribution system.