Energy, Power

Perfecting power distribution data quality

Data produced by power distribution networks is critical, but so is perfecting data quality and governance.
By Sunil Kotagiri June 28, 2019
Courtesy: Cyient

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 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.

Quality data enables the utility to understand network and asset behavior, 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 behavior 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 improve the quality and cost efficiency of its operations.

Many utilities, however, believe they are still missing key information about their assets. This is because the underlying infrastructure utility networks use today was deployed decades ago when recording data was not critical to business success (see Figure 1). To make up for this limitation, operators can leverage newly gathered records from smart meters and other sources. However, extracting real value from utility data 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.

Figure 1: Utilities are missing key information about their assets because the underlying infrastructure for utility networks used today was deployed decades ago when recording data was not critical to business. Courtesy: Cyient

Figure 1: Utilities are missing key information about their assets because the underlying infrastructure for utility networks used today was deployed decades ago when recording data was not critical to business. Courtesy: Cyient

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 (see Figure 2). BI Intelligence estimates 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 and legacy information technology 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 a principal challenge they face lies in ascertaining a single source of dependable information from the data gathered (see Figure 3). Most of these records remain siloed in multiple files and information technology (IT) systems and need to be unified.

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

Figure 2: Smart devices are enablers that generate substantial volumes of data from utilities, which can put pressure on infrastructure. Courtesy: Cyient

Figure 2: Smart devices are enablers that generate substantial volumes of data from utilities, which can put pressure on infrastructure. Courtesy: Cyient

The modern grid enables the utility to react 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.

Geospatial information systems (GIS) have made it critical to use data for networks. And these are not just digitalized maps that can offer information to third parties. Today, GIS has transformed into data centers that can be customized in several ways based on their needed purpose. They can also be used to prioritize power projects and bundle different projects together for more cost-effective work.

The outlook of network operators to data sharing also must shift from a need-to-know basis to a presumption of disclosure. In particular, there should be more of data sharing between gas and electricity networks.

While the value of data for network operators is understandable, it is also essential to collect data in the right ways. If things move in the right direction from the earliest stage, problems that emerge later can be prevented. There have been cases where network operators amassed volumes of data that was never actually used – such attempts only result in waste of time and resources. Therefore, data must always be gathered for a specific purpose and not just for the sake of record keeping.

On the other hand, some operators feel if they get too selective in the data gathering process, it can throttle innovation because there are several upcoming uses of data. By also consulting their stakeholders, operators can accommodate the data needs of others instead of just acting by their objectives.

Mastering a data governance model

Electric utilities are large organizations with many discrete suborganizations with each managing various programs, processes and systems. Typically, these organizations work separately in silos, often duplicating and not sharing data. As a result, harmonizing data systems and processes with increasing volume of data aggravates problems associated with a lack of data governance. Today’s mandate is a need to engage a governance model assuring process, system and data alignment to meet modern grid demands.

Figure 3: Energy network operators 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. Courtesy: Cyient

Figure 3: Energy network operators 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. Courtesy: Cyient

Data governance enables the utility to aggregate data across multiple processes and systems and requires blending accountability, agreed service levels and measurement. Adopting a strong governance model will improve the company’s data lifecycle approach.

As departments are getting reshuffled to facilitate more collaboration between asset managers and data organizers, data-driven transformation for networks needs to be intensified with the judicious deployment of data validation tools.

Sunil Kotagiri is deputy general manager at Cyient. Edited by Jack Smith, content manager, Control Engineering, CFE Media, jsmith@cfemedia.com.

MORE ANSWERS

KEYWORDS: power distribution networks, data acquisition

Emerging modern grids demand accurate data and as-operated network information to function optimally.

Data should be able to move between traditional and modern systems.

Today’s mandate is a need to engage a governance model assuring process, system and data alignment to meet modern grid demands.

CONSIDER THIS

How important do you feel data governance is to the grid?


Sunil Kotagiri
Author Bio: Sunil Kotagiri is deputy general manager at Cyient.