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Data Produced by Power Distribution Networks is Critical, but so is Perfecting Data Quality - Part 1

Data Produced by Power Distribution Networks is Critical, but so is Perfecting Data Quality - Part 1

The electric utility industry is undergoing a major transformation driven by new sources of energy generation (solar and wind power), consumer demand for faster and more affordable services, cybersecurity, and big data. Gathering data to harvest insights and forecast more accurately offers a significant potential to optimize the way utilities operate. Emerging modern grids demand accurate data and as-operated network information to function optimally. Given these business imperatives, utilities must overcome current constraints and limitations to enable essential operations data quality.

Good quality data enables the utility to understand network and asset behaviour, operating conditions, and their impact on customer service. Electric networks change routinely, and operations reflect a dynamic condition. Therefore, quality data must be regularly assessed based on its context of use. The utility network must enable accurate measurements of network behaviour to assure accurate observations and the ability to optimize measures in response to current and accurate data. The ability to assure correct inputs from system and operations data enables the utility to substantially improve the quality and cost efficiency of its operations.

The key question is: how can electric utility network operators validate and use data they collect?

To begin with, many utilities agree that they are still missing key information about their assets. This is because the underlying infrastructure for utility networks used today was deployed decades ago when recording data was not critical to business. To make up for this limitation, operators can leverage newly gathered records that come from smart meters and other sources.

Extracting real value from utility data, however, requires the development of a data-driven operation and a data ecosystem that can underpin processes, systems, and people; and create an as-operated paradigm as opposed to an as-designed model.

Utilities generate substantial volumes of data, and while the Internet of Things (IoT) proliferates across networks, thanks to smart devices, it creates multiple new data points that can put pressure on infrastructure. BI Intelligence estimates that the global installed base of smart meters will increase from 450 million in 2015 to 930 million in 2020. On top of this, distributed energy resources (DER) and legacy IT systems bring fresh challenges to utilities having to manage and interpret greater volumes of information. For example, thousands of mini-generation plants can sit all over the network, bringing in new data points every minute. A system is therefore required to gather and maintain multiple sources of data.

Energy network operators also believe that a principal challenge they face lies in ascertaining a single source of dependable information from the data gathered. Most of these records remain siloed in multiple files and IT systems and therefore need to be unified.

To consolidate this data, it must be segregated from the set-ups where it gets stored. This is also necessary because of the fast pace of development in the power sector implies that the lifespan of discrete IT systems may get shorter over time. Data should be able to move seamlessly between traditional and modern systems.

The modern grid enables the utility to react quickly and effectively in a complex and demanding environment. To enable this intelligence, it is imperative to harmonize data with actual operating conditions. Creating this harmony between data and as-is or as-switched conditions requires an Intelligent Data Management Solution to align utility process and system data. Finding the right model and system to align this data is the first step to obtaining high quality, actionable data and improving modern grid services quality.

Stay tuned for part two of this blog that will talk about the necessity of not just data quality but also governance in operating modern grids.  Read part 2 here


By Sunil Kotagiri | September 17th, 2018

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