Data is driving change – adapt or fall off the grid

More information than ever before is flowing from smart meters and other sensors, along with traditional sources; and just as other industries are now fuelled and driven forward by data, the modern electric grid is no different.

If utilities are to improve both their operations and customer service, they will need to overcome current restrictions and limitations to ensure they have access to the very highest quality data.

 What’s holding the network back?

Many utility companies use the Geographic Information System (GIS) to model and forecast network behaviour within operational systems. To date, GIS has undoubtedly offered organisations the best instruments to manage electrical connectivity.

However, technological advances are changing the game and GIS no longer provides all the tools needed to navigate and manage the modern grid – a grid that requires operational data to be more accurate.

For example, GIS does not provide field personnel with the precise and timely data they need to perform their role. While GIS generates a great deal of data and information, it typically reflects the design and planning (as-built) perspective rather than an as-operated perspective.

The modern grid is also calling for Advanced Distribution Management Systems (ADMS), which look to make operational decisions without human interaction. This requires as-operated content along with extensive correlative asset data.

The need for optimisation and modification

To ensure that GIS can support electric operations, several modifications will need to be made. These include:

  • Improving the speed at which the network can be updated
  • Offering accurate views of the network to allow operations to be carried out in a timely manner
  • Deploying machine learning (ML) algorithms to harmonise phase and transformer connectivity with actual network conditions

ML is a form of Artificial Intelligence (AI) and quite possibly one of the most exciting emerging technologies of its time as it enables utilities to leverage different types of data and make better informed decisions within the ADMS platform.

For example, ML algorithms will enable field operations personnel to analyse data during a natural disaster to distribute alternative sources of energy during downtime and ensure an improved customer experience.

Utility companies must be able to react in real-time to complex and demanding scenarios. By harmonising data with actual operating conditions, they will improve their services significantly.

Creating harmony between data and as-is conditions requires an intelligent Data Management Solution (iDMS) to align process and system data. By using ML in day-to-day operations, utilities can take advantage of additional intelligence and create a virtual circle of data quality.

However, legacy firms and stand-alone data repositories make consolidation and aggregation difficult to achieve in reality. Identifying the right model to align all of the information will be the first step to achieving high quality data and improving modern grid service operations.

 Mastering data governance

The constraints do not end there. Within the industry there are many different organisations and departments, all of which have their own processes and systems. These organisations have traditionally worked separately, often duplicating data in siloes rather than sharing it.

If organisations want to make the most of the modern grid however, this must change. With data volumes increasing exponentially, so too are the issues associated with a lack of data governance.

Data governance gathers information from multiple sources and ensures a blend of accountability, agreed service levels and measurement. Businesses benefit immeasurably from data that is consistent and trustworthy.

For utilities that are using data to make business decisions, optimise operations and create new services, data governance can prove invaluable.

An iDMS gives operators a view into service levels, thus allowing utilities to enforce the agreed service levels at key points and manage restrictions. A durable governance model will ultimately go on to ensure high quality throughout the complete lifecycle of the data.

Unlocking the potential of analytics

The modern grid is offering utility companies the chance to improve their operations and provide a better, more streamlined service to their customers. And, like all modern industries, high quality data can help drive this forward.

Utilities will need to look at their long-term objectives and ensure they are creating a safety culture that will inform the models they introduce. From nurturing and building accountable teams, to education and knowledge sharing, organisations need to have a clear idea about what quality data is and where to start with it.

 With energy saving initiatives, new renewable energy sources and developing technologies, a smarter grid is needed to cope with the complex demands of the modern population. To take advantage and make the most of these dynamic changes, utilities needs to empower their operations with a level of efficient data quality that has previously not been possible in the network.

By optimising GIS, mastering data governance and establishing a culture of data quality, utilities will be able to ensure they’re moving in the right direction. By aligning these components, they can overcome current constraints and unlock the power of analytics that is vital to securing quality operations data.

Dan Beasley serves as Director of Utilities and Geospatial (U&G) within Cyient, Inc. of North America. Mr. Beasley has served in this capacity for the previous eight years. In total, he has over 25 years of experience in utility operation practices, including planning, development and implementation of geographic, outage, scheduling, work and mobile systems for utility organizations throughout North America. He specializes in assessing these systems and optimizing the validation and flow of data within business process and between these system interfaces.  

The views and opinions expressed in this article are the author’s own, and do not necessarily reflect those held by pv magazine.