Digitalization efforts are maturing quickly in the power and utilities industry and are already delivering value to grid operations around the world through efficiency gains, lower costs, reduced risk, and improved HSE. With our focus on rapid data access and contextualization across source silos, Cognite has identified the following key areas within transmission and distribution where improved data management practices can deliver outsized value to grid operators.
1. Fault Analysis
In an effort to improve both the reliability and resilience of the grid, advanced analytics can be applied to fault analysis to uncover root causes and safely automate switching and efficient reconnection. Examination and characterization of previous faults help operators learn from operational history in order to reduce the risk of future outages and minimize the cost of energy not supplied (CENS). This directly improves the end-user experience and serves as a competitive advantage.
The Data Challenge: Failure patterns may be difficult to extract from existing data, either due to the significant quantity of data to be analyzed or the lack of data science experience within the organization.
2. Digital Twins
Going a step further, digital twins offer a visual and predictive representation of the conditions in a physical system or environment. While most operators are familiar with digital twins of assets, a good data model can also be used to represent operations across an electrical grid. By looking at the system holistically, operators gain additional insight about potential faults and how to reroute power as needed with minimal impact. As the digital twin trains on data and becomes operationalized, it can also be used to create data and simulate a variety of operating scenarios to analyze what-if outcomes.
The Data Challenge: The largest obstacle is finding, interpreting, and organizing the most relevant data among the big data being collected from across the electrical grid.
3. Smart Maintenance
Next, smart maintenance practices improve the predictability in grid operations and offer a path toward dynamic maintenance planning and away from an entirely calendar-based approach. Beginning with remote condition monitoring and improved visibility across a distributed network of transformers and substations, teams can more readily analyze the health of each asset, predict unexpected issues, and improve decision-making. This leads directly to more accurate maintenance work and repairs, and can result in better management of maintenance deferrals.
The Data Challenge: Advanced modeling requires reliable data pipelines, quality, and operationalization of the resulting predictive models, in order to drive adoption and value.
4. Yield Optimization
Once maintenance processes become more predictable, the operational focus can shift to reworking operational strategy, processes, and planning in order to optimize yield across the existing grid infrastructure. Here, improved analytics on grid data can help operators reduce power loss and enable dynamic capacity allocation without the need for significant capital reinvestments. Better access to grid data enables dynamic line rating systems with increased capacity without compromising safety.
The Data Challenge: There are a significant number of variables in play that exist in various systems and silos and may not be readily available for use.
In pursuit of these applications, some operators may choose to approach these challenges with point solutions from a portfolio of vendors. Unfortunately, this does not address the fundamental challenges with data access and contextualization and increases complexity due to vendor lock-in.
Grid operators can capture significant value by embracing the right digital strategy. For digital applications to truly become adopted across an organization, the data architecture needs to be able to support the needs of an emerging class of new data consumers, integration partners, and developers. This is where Cognite is well-positioned to enable digital initiatives with a proven DataOps solution that unlocks siloed data, contextualizes both IT and OT sources, and accelerates time to value.