Our six-part series delves into the importance of high-quality data in the power markets. Read the introductory post.
Power market participants rely on lots of data for their market decisions. Knowing where the data comes from, what changed, and when is critical for trusting these decisions and avoiding costly mistakes. The slightest misaligned time series data or information gaps can result in catastrophic errors, signifying the importance of having trustworthy data in highly dynamic market situations. Healthy data lineage practices are a component of trusting and feeling confident in the data.
In this blog, we’ll explore the importance of data lineage and its impact on market participants, discuss Yes Energy’s data lineage practices, and detail the data lineage observability we provide our customers so they can feel confident in our data. We’ll also provide key questions to ask information providers to ensure you’re accessing the best data for decision-making.
In the power market industry, data comes in various granularities across different markets, from real-time market data to hourly day-ahead data, daily profit and loss reporting, grid condition forecasts, and everything in between.
Adding to this complexity, data sources exhibit a multitude of reporting behaviors, including varying time zone designations, inconsistent handling across daylight savings time, unique naming conventions, and diverse data formats – not to mention occasional reporting errors and revisions. Understanding raw data changes, especially those by vendor solutions, is critical for trusting your market decisions, especially given the volume and complexity of data in power markets.
At Yes Energy, our data operations team is dedicated to cleaning, standardizing, and monitoring incoming data in real-time to ensure accuracy and consistency so market participants don’t have to worry about this themselves and can instead focus on making informed decisions with confidence (Figure 1).
Visual summary of Yes Energy’s data lifecycle
Whether you’re a financial or physical market participant, data transparency is key to optimizing operations and maximizing opportunities. Robust data lineage tracking gives you confidence in the accuracy of the numbers you rely on for the analytics, debugging, and compliance efforts associated with financial trading, risk analysis, asset development, or utility operations.
Real-time traders rely on timely, accurate data to make frequent trading decisions. Data delays or discrepancies can impact profitability, which emphasizes the importance of knowing when and how data refreshes.
Day-ahead traders tend to analyze large volumes of historical and forecasted price, generation, load (demand), and congestion data for shaping their bidding strategies. Inaccurate and inconsistent handling of data revisions can lead to unreliable analytics and poor market positions.
Risk managers rely on a clear audit trail of revised price data and settlement corrections for financial reporting and risk modeling. Lacking a clear audit trail can introduce significant errors into this reporting.
Utilities must provide reliable power supply to meet demand while optimizing costs – therefore, inaccurate price, demand, and generation data could negatively impact the reliable delivery of power and cost optimizations.
Asset managers and developers need accurate real-time and historical data to optimize existing assets or to develop new sites. Inaccurate or outdated information can lead to suboptimal planning and dispatch decisions.
A foundational component of data lineage is understanding the reporting nature and of the data. Forecast data sets such as load and generation are revised as the forecast gets closer to the time it is predicting. Power market data sources also revise some of their non-forecast data sets as part of their normal data formation process.
For example, locational marginal pricing (LMP) is a non-forecast, real-time data set essential for understanding and modeling future power grid behavior, but it’s one of the most frequently revised data sets published by Independent System Operators (ISOs). Having visibility and observability into what changed and when is key to making sure the right data is being used to make decisions, test strategies, and calculate profit and loss.
When an ISO issues an update, it’s critical to track and store the full update history of the data. For example, common analytical practices such as creating hyper-accurate backcasting models require nodal pricing data for a specific point in time, not the most recently published data. In other words, energy traders need to understand the revisions, or “vintages,” of the data they’re using in their model to better evaluate decisions made given the information available at the time.
Yes Energy’s data catalog includes the record history for all existing non-forecast LMP, ancillary services, generation, load, flow, and additional forecast data tables. These tables help with data lineage because they provide the full stream of what data changes occurred and when they were ingested into our DataSignals Cloud product using the metadata operation and timestamp fields (Figure 3). This observability into the data provides explicit transparency, helping you feel confident in your market decisions.
An example of Yes Energy’s Data Signals Cloud hourly LMP table showing the original data and two updates
In addition to cleaning and standardizing time series data, Yes Energy specializes in creating calculated, or enriched, data sets out of other base time series data so that customers don’t have to perform these data transformations themselves.
For example, ERCOT doesn’t directly publish nodal congestion data, but this information is key to market participants looking to analyze historical nodal congestion patterns. You can find examples of calculated data types in all views named like “_CALC” in our DataSignals Cloud database product (Figure 4).
Sample calculated day ahead estimated congestion for ERCOT, showing the deep history and data structure. All of the calculated data types are in views named like “_CALC”, so you know they are calculated by Yes Energy.
Calculated data points are tagged for transparency in the Yes Energy DataSignals Cloud, and our in-house team uses automated and manual tests to ensure data quality, including being alerted by any points of failure along the transformation process and by the underlying data dependencies.
Here are four questions to ask potential providers to understand their data lineage processes and ensure you’re receiving the highest-quality data:
At Yes Energy, we believe proper data lineage practices are essential for establishing trust with our customers, and we’re committed to being as open and transparent about our data as possible. That’s why we have data lineage tracking tools across the Yes Energy ecosystem for both ourselves and our customers.
Two features that make data lineage tracking more visible to customers are our vintage non-forecast time series data and calculated data types in DataSignals® Cloud. We continue to research and implement data lineage capabilities, for ourselves and our customers, into our products. We also have robust internal data quality tracking in DataSignals Cloud – reach out if you want to learn more about our internal data monitoring practices.
This is the third blog of a six-part series on data quality. Subscribe to our blog so you’re notified first when the next blog in our data observability series comes out.
To learn how Yes Energy can support your power market needs with accurate, high-quality data, request a demo or check out our free sample data on the Snowflake marketplace.
If you’re a current customer, you can check out more in-depth examples such as code snippets and workflows in our code repository.