{"id":32669,"date":"2026-01-23T17:59:16","date_gmt":"2026-01-23T17:59:16","guid":{"rendered":"https:\/\/www.trccompanies.com\/?post_type=news-and-insights&p=32669"},"modified":"2026-01-23T17:59:19","modified_gmt":"2026-01-23T17:59:19","slug":"five-strategies-to-improve-model-management-for-utilities-and-isos-rtos","status":"publish","type":"news-and-insights","link":"https:\/\/www.trccompanies.com\/fr\/insights\/five-strategies-to-improve-model-management-for-utilities-and-isos-rtos\/","title":{"rendered":"Five Strategies to Improve Model Management for Utilities and ISOs\/RTOs"},"content":{"rendered":"\n\n
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Achieve a timely, accurate and complete system of record that delivers flexibility and resilience<\/h2>\n

For modern utilities, as well as their ISO and RTO partners, the business of energy delivery has taken a digital turn. Data matters more than ever, with asset upgrades, smart meters, distributed energy resources (DERs) and omnichannel customer service all impacting how organizations operate. Indeed, the very nature of balancing supply and demand and collaborating with ISOs and RTOs to manage the bulk electric system requires the rapid exchange of timely, accurate and complete data across multiple organizations and stakeholder groups.<\/p>\n

Over the last decade, as companies have digitally transformed and modernized operations, the use of multiple software systems, manual data updates and disjointed business processes has become increasingly common. As a result, utilities and operators struggle to create, maintain and synchronize comprehensive, accurate digital network models of the electrical grid that serve as the single source of truth for planning, operations, and regulatory compliance. This impacts both the efficiency of utilities’ operations and the associated costs.<\/p>\n

Utilities, ISOs and RTOs alike require a better way to manage model data. They need a shared, harmonized system of record that supports all aspects of decision-making. This requires practical, proven strategies that connect and integrate enterprise information technology (IT) and operational technology (OT), along with the people and processes involved in day-to-day business operations.<\/p>\n

Siloed Systems, Disparate Data and Disconnected People<\/h2>\n

Organizations struggle with model management\u2014and achieving an accurate and complete system of record\u2014for many reasons.<\/p>\n

For starters, utilities must manage an onslaught of data across multiple platforms, including internal Energy Management Systems (EMS), Outage Management Systems (OMS), Geographic Information Systems (GIS) and ISO\/RTO model data management platforms. These maintain data formats, asset naming conventions and other identifiers that often diverge over time.<\/p>\n

For example, it is common for legacy data snapshots, taken decades ago when joining an ISO, to become misaligned with current asset naming conventions and industry standard asset identifiers. As a result, both internal and ISO-level models drift apart, causing operational inefficiencies and process-related risks.<\/p>\n

For ISOs and RTOs, receiving model data in inconsistent formats, such as PDFs, PS CAD files, spreadsheets and one-line diagrams, adds complexity. The lack of harmonization forces model data management teams to spend countless hours parsing, validating and reconciling disparate submissions.<\/p>\n

Antiquated data snapshots, frozen at the date of membership, can persist for decades. At the same time, member utilities evolve and upgrade their systems independently, further widening the gap between ISO and utility models.<\/p>\n

Moreover, manual data entry, legacy practices and disconnected operational systems introduce high risks of errors and version drift. Organizational silos among IT, engineering and operations departments make the problem worse through poor communication and governance gaps.<\/p>\n

The fragmented nature of current utility software exacerbates the issue. Siloed analytics tools and datasets lead to isolated views and offer limited insight into overall grid model data quality. In addition, employees waste valuable hours searching for and cleaning up data, with 12 hours per worker lost weekly and customer issue resolution 43% slower across fragmented environments.<\/p>\n

The consequences of poor model data management include manual processes prone to errors, inconsistent application of data validation rules and a loss of operational efficiency.<\/p>\n

Challenges to achieve an accurate system of record include:<\/p>\n