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 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 substantially improve the quality and cost efficiency of its operations.
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.
Pinpointing Dependable 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.
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. GIS has today transformed into data centers that can be customized in several ways based on the purpose for which they are needed. They can also be used to prioritize power projects and bundle different projects together for more cost-effective work.
Furthermore, the outlook of network operators to data sharing must shift from “need to know” basis to a presumption of disclosure. 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 utilized—such attempts only result in wastage 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 that 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 sub-organizations, 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, therefore, a need to engage a governance model assuring process, system, and data alignment to meet modern grid demands.
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 approach to the data lifecycle.
Cyient’s Intelligent Data Management Solution (iDMS) has been customized to seamlessly monitor, identify, and rectify input data quality through configurable machine learning validation routines with no impact on CAPEX. For further information, visit Cyient.
About the author
Sunil Kotagiri is the Deputy General Manager, U&G BU at Cyient. Sunil has 20+ years of experience across various functions - consultancy, program management and business development in the areas of IT and operational technologies of utilities domain. He currently leads ‘Grid Solutions’ portfolio at Cyient. He has a strong knowledge of business processes and deep understanding of how different systems can be utilized effectively for resolving various business challenges. He has successfully led many projects in the areas of Smart Grid, AMI, ADMS, GIS and Cyber Security assessment. He has presented on variety of topics in leading utility events across the globe. He holds PMP and SGMM candidate navigator certificates.