Hub Sessions

The importance of cable diagnostic testing to verify the true condition (Health Index) of cable assets and the many benefits of using a non-destructive technique to protect cable assets.  How Hydro Ottawa has incorporated this testing method into their asset management plan and the financial benefits as well as improvements with reliability they have seen for the past 6 years.

Because they are critical infrastructures it is essential that we operate and maintain them in the best possible way. Based on increased possibilities of continuous monitoring of assets (and therefor an increasing amount of assetdata), reports on asset performance and an increasing amount of operational data, predictive analytics and remaining lifetime calculations will gain importance. In other words, when we combine asset data, performance data, operational (harmonics) data and disturbance analysis data predictive analytics is possible. But there is no one truth, meaning Assetmanagers need a simulation environment in which they can investigate and use scenario’s (for instance grid extensions but also flooding scenario’s in which network operability can be checked).

The use of intelligent edge devices to acquire asset sensory data, along with machine learning algorithms to predict when an asset will fail, is becoming more attractive to maintenance managers as they seek new methods to get maintenance costs under control. The use of this technology can augment or even supplement human subject matter experts while providing significant advanced notice of asset health issues by analyzing and learning from past asset health data. In this presentation, we will discuss practical ways in which utilities can get started today and see how others are implementing this technology. - Speaker to be announced

Organisations today need to be able to map, monitor and manage the whole enterprise. We will discuss how functional and hierarchical barriers are overcome to create efficient processes and provide a management structure around topics such as financial objectives, asset investment planning customer and stakeholder satisfaction and risk and compliance.

A robust and agile platform for its increasingly data-driven business. 

Getting state of the art networks means an investment in digitalisation of the grid. Enexis started the digitalisation with distribution automation (DA)in 2011 and followed with the roll-out of the smart meter in the years after. In 2017 a roll-out  of Da-light is started together with the development of an IT architecture and a data science community. Digitalisation helps with the challenges we face due to the energy transition and the more complex energy system we face. The data that is generated is already used in several use cases as will internally as externally and is generating value from the moment that the first components where live.

Key dynamics required to highlight the importance of managing the impact of low-voltage data growth for utilities today. Best practices for incorporating customer technologies such as DERs and home energy management into the grid planning and operations and what could a reference framework of future grid management might look like?

Grid operators push digitalization programs in order to support their asset management processes and procedures. We describe the building blocks, opportunities, challenges, key requirements and limitations. We present an entire ecosystem from data capture, storage, analysis, interpretation, recommendations and operational execution of respective work orders. 

Utility analytics has exploded in the last few years primarily driven by the hype around big data, data science and advanced analytics technologies, and is now seen as a strategic lever to improve assets, operations and customer satisfaction. Despite this drive many utilities are still struggling to implement analytics, either not knowing where to start, not knowing how to scale from pilot developments or attempting technology/vendor driven approaches. This presentation makes the argument for a business led and driven analytics capability deployment approach, enabled by advanced technologies and data science, based on experience from successful projects in the US and Sweden.