Internet of Things: Situational awareness with predictive grid analytics

Pacific Gas & Electric has demonstrated a visualisation platform providing grid situational awareness.

The growing Internet of Things (IoT) represents a new reality. Smart meters, sensors embedded in utility field equipment and across the grid, and personal devices such as smartphones and computers, all connected to the enterprise environment, are already impacting how we manage our power distribution and consumption.

Engerati has identified three key impact areas of the IoT in energy.

One is the flexibility and resilience necessary to integrate the growing levels of distributed generation and storage and the emergence of prosumers. Another is the ability to support intelligent asset management at both the economic and technical levels.

Data integration and visualisation

The third is situational awareness with predictive grid analytics, which is an emerging topic but with two major challenges to overcome.

First, many disparate sources of complex and plentiful data must be integrated and managed.

Such data includes not only that from the IoT devices but also for example environmental information such as weather measurements for renewable forecasting and equipment data on status for condition monitoring.

Second, the outputs of the data integration and analytics require tools such as visualisation to provide the necessary insights and awareness into the state of the grid.

As an example of how utilities are tackling these challenges, we turn to Pacific Gas & Electric (PG&E), which has just completed a demonstration of grid operations' situational intelligence.

PG&E demonstration

The aim of PG&E’s pilot was to develop a project version of a real-time data visualisation software platform in order to demonstrate the integration and visualisation of data streams from multiple sources on one system, as opposed to a variety of tools operating across multiple different screens and platforms.

“Because these tools are not integrated with each other, this forces the distribution operations personnel to use ‘swivel chair integration’ where the people search between multiple data streams in an ad-hoc fashion,” states the project report.

“The ability to integrate and visualise a wide range of data sources on a single platform in near real-time would allow end users to make improved operational decisions, with the benefits of allocating resources more efficiently, improving reliability and increasing safety.”

In the project, PG&E integrated data streams from over 20 sources. In addition, the US utility maintained a system to provide updated data extracts from each source.

The visualisation tool that was developed enabled users to customise a map with various data layers depending on their specific use case.

For example, in the ‘outage anticipation’ use case, weather, wildfire, lightning and earthquake feeds were combined onto a single map view along with GIS information including Google Streetview.

Previously, users had to spend significant time and energy guiding field personnel to equipment of interest. By providing access to street-level views integrated with PG&E’s internal asset data, personnel in operations centres were enabled to provide improved support to field personnel.

With asset maps overlaid with external weather maps, planning could be undertaken for potential outages.

Other use cases that were demonstrated were construction planning, circuit loading research, daily operations, emergency operations and distributed generation planning.

Data visualisation challenges

PG&E notes that there were some unique challenges that needed addressing in the project.

One of these was that nearly all of the data sources needed to be updated with some frequency in order for the visualisation tool to provide near real-time information.

Another was that sometimes a feature would appear to be providing the desired functionality and would pass tests designed to verify that it was behaving as designed, but the output of the feature would show incorrect data.

Automated testing suites and sophisticated visualisation capabilities might help mitigate this issue, and are important elements for utilities to consider when choosing an integrated data platform.

Situational grid intelligence - next steps

PG&E regarded the project as successful, saying it has allowed the company “to validate the potential to better identify and leverage insights from an increasingly complex ecosystem of operational data.”

While the prototype version of the platform has reached end of life, PG&E intends to continue to explore future situational intelligence platforms using the lessons learned.

Recommendations for expanding a future version of the platform include developing a data export functionality to allow users to explore deeper into the data to perform specific analyses.

Another is applying an added layer of business logic and other ‘rules’ to the raw data to provide the user with actionable insights.

Additional use cases also should be tested, the report concludes.

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