Can Weather APIs Meet FERC’s Standards?

In the rapidly evolving energy sector, maximizing the capacity of power transmission is becoming increasingly vital. One of the key factors in determining the capacity of overhead power lines are real-time environmental conditions, particularly wind speed and wind direction. Traditional methods, like Static Line Ratings (SLR), rely on conservative estimates, often limiting the true potential of transmission lines. Ambient-Adjusted Ratings (AAR) have been introduced to address this limitation by factoring in real-time temperature data. However, the next significant leap in transmission optimization comes through Dynamic Line Ratings (DLR), which adjusts power line capacity in real time, based not only on temperature but also on critical factors like wind speed and direction. This article delves into the role of accurate wind data, the benefits and limitations of weather APIs, and how advanced sensors are essential for fully realizing the benefits of DLR systems.

Ambient Adjusted Rating with FERC 881

In December 2021, the U.S. Federal Energy Regulatory Commission (FERC) issued Order 881, requiring U.S. transmission operators to implement Ambient-Adjusted Ratings (AAR) by July 2025. The goal is to increase grid capacity by using more accurate, real-time temperature data. Instead of relying on static, conservative weather assumptions. AARs adjust dynamically based on the actual temperature at the power line, while factors like wind speed remain static. Lower ambient temperatures allow transmission lines to carry more electricity, while higher temperatures reduce their capacity. In many cases, AAR can increase transmission capacity by 5-10%.

To implement AAR, transmission operators can leverage weather APIs that provide real-time and forecasted temperatures, as temperature is easier to model due to its gradual changes over short distances compared to factors like wind. The temperature data from a weather API is often closely aligned with observations from weather stations. This consistency allows weather APIs to reliably capture or estimate temperatures, enabling transmission operators to make accurate, real-time AAR without the need for localized sensors along the grid.

To emphasize the similarity between weather API and weather station data, we performed a linear regression analysis comparing 1-hour AAR values, calculated using temperature data from January to July 2024. The data comes from a weather station near Hólmsheiði, Iceland, and a weather API for the same location.

The scatter plot illustrates a strong correlation between the two datasets, with a linear regression line showing an excellent fit. The R-squared value of 0.98 indicates that 98% of the variance in AAR values calculated using weather station temperature data can be explained by AAR values derived from the API temperature data. This high correlation demonstrates that the API data closely matches actual temperature readings, with minimal error.

Figure 1. Comparison of AAR calculated using temperature measurements from a weather station compared to AAR calculated using temperature estimates from a weather API.

While direct sensor measurements are typically more precise, these results show that temperature values from weather APIs offer a high level of accuracy, making them a viable solution for applications like AAR. This aligns with the guidelines of FERC Order 881. Although minor discrepancies exist, the relatively stable nature of temperature variations ensures that APIs can be relied upon with minimal risk of significant error.

Dynamic Line Rating

Building on FERC Order 881 and exploring potential reforms to further enhance transmission line ratings, FERC issued an Advance Notice of Proposed Rulemaking (ANOPR), seeking input on reforms to implement Dynamic Line Ratings (DLR). DLRs adjust transmission line ratings in real time based on actual environmental conditions such as wind speed, wind direction, temperature and solar radiation. By adapting to real-time weather conditions, DLR can increase transmission capacity by up to 40%, offering a significant improvement in grid capacity beyond what is achievable with AAR.

While weather APIs are effective at estimating temperature, they often fall short in capturing precise wind conditions, an essential factor in determining transmission line capacity with DLR. Wind speed, which can vary significantly over short distances due to geographic features like terrain and vegetation, is less accurately estimated by weather models compared to temperature. As wind is the largest contributor to cooling effects on conductors, inaccuracies in wind data from APIs can lead to substantial errors in real-time and forecasted ampacity calculations.

Without accurate, localized data, fully optimizing the use of transmission lines becomes challenging. By deploying on-site sensors to measure critical variables such as wind speed, wind direction, and temperature in real time, transmission providers can maximize both the safety and efficiency of their infrastructure. This ensures that lines are fully utilized while adhering to safety standards.

To highlight the discrepancies between weather API and weather station wind data, we conducted a linear regression analysis comparing 1-hour DLR values, calculated using temperature, wind speed, and wind direction, from January to July 2024. The data comes from the same weather sources used for the AAR calculation above.

In contrast to the AAR data, the DLR data reveals a significant gap between weather API estimates and direct measurements. The scatter plot highlights these discrepancies, with a linear regression line showing a relatively weak fit between DLR values from the weather API and those from direct weather measurements. The R-squared value of 0.63 indicates that only 63% of the variance in DLR observed by the weather station can be explained by the API data. This lower R-squared, compared to the much higher value of 0.98 for AAR data, suggests that the weather API’s wind speed estimates are considerably less reliable.

Figure 2. Comparison of DLR calculated using temperature, wind speed and wind direction measurements from a weather station compared to DLR calculated using temperature, wind speed and wind direction estimates from a weather API.

This discrepancy underscores why FERC's ANOPR emphasizes the need for direct wind speed measurements through on-site sensors. Without these, any DLR system risks inaccurately estimating power line capacity, which could compromise grid reliability and efficiency. Direct DLR systems equipped with wind sensor provide the most accurate wind speed data, improving both real-time and forecasted ratings, essential for effective grid management. This approach helps prevent the risks of overloading or underutilizing transmission infrastructure.

The time series graph illustrates the ampacity of a transmission line over time, comparing several methods of determining line ratings between April 14, 2024, and April 17, 2024. The graph demonstrates the effectiveness of DLR in maximizing transmission capacity compared to AAR and the more conservative SLR, particularly when incorporating actual weather measurements.

Interestingly, for one hour, the DLR from the weather station measurements dips below the SLR, indicating a potential risk of operating the line above its thermal rating, which could pose safety hazards. During this period, AARs also fail to capture the decreased capacity, underscoring the limitations of AAR in scenarios with rapidly changing environmental conditions.

The difference between the rating calculations methodologies for this period is also summarized in table 1.

Table 1. Comparison of ratings calculation methodologies from April 14 to April 17, 2024.

Accurate data is also crucial during extreme weather conditions. As these events become more frequent and severe due to global warming, real-time data from on-site sensors plays a key role in preventing system failures and ensuring grid stability. Having the ability to respond quickly to rapidly changing environmental conditions is vital to maintaining a safe and reliable transmission network.

Key Takeaways

The shift toward DLR represents a significant opportunity to optimize transmission capacity in real-time, far beyond the limitations of SLR and AAR. As demonstrated, real-time weather measurements, particularly wind, are essential for maximizing grid efficiency and ensuring safety. By deploying sensors to capture these conditions, transmission operators can harness the full potential of their infrastructure, aligning with FERC's vision for a more reliable and resilient grid. Moving forward, integrating accurate, localized weather data will be crucial to unlocking the true capacity of the grid.