Reliability Planning: Weather-Correlated Models Ensure Resource Adequacy & Maximize Value

January 15, 2024



Resource planners must ensure resource adequacy and maximize economic value with the energy transition. Because a greater portion of energy supply flows from renewables and battery storage, maintaining reliability requires more advanced reliability planning that captures the impact of weather on unit outages, energy production, and transmission lines. Using traditional reliability models that miss the linkages of weather on unit outages and rely solely on predefined distributions or historical data can lead to biased and inconsistent results that present an inaccurate picture of system risks. Dr. Brandon Mauch, Director of Resource Planning Analytics for Ascend Analytics, lead a webinar that discussed how weather-correlated reliability models improve resource adequacy outcomes while maximizing value. Dr. Mauch was joined by Dr. Gary Dorris, CEO and Co-Founder of Ascend Analytics, and Mr. Zachary Brode, Manager of Analytics for Ascend.

Key Takeaways

  • Using weather-correlated models for reliability planning yields a clearer view of the relative contributions of different resource types towards system reliability, providing insights that have traditionally not been captured due to model-limited choice.
  • Using resource adequacy models that sample from predefined distributions or resample solely from historical data underestimates system risks and fails to capture value opportunities associated with different resource types.
  • Capturing meaningful uncertainty related to forced outages, long term load forecasts, renewable generation, and load profiles requires the use of simulations that probabilistically envelop model assumptions, rather than being led by them.  

Weather as the Fundamental Driver

Historically, reliability planners forecasted potential thermal outages using uniform distribution models. This simple, deterministic approach to resource adequacy modelling proved feasible in an era where all resources were dispatchable, and energy storage options limited. Today, weather drives load and renewable generation, leading to volatile supply and load conditions; extreme weather also plays a significant role in forced outages. Thus, today's reliability planners must account for the impacts of – and correlations between – variable generation, transmission constraints, forced outages, and extreme weather events.

Dr. Mauch provided multiple examples to demonstrate the importance of accounting for weather as the fundamental driver in reliability planning models. One example highlighted differences between ERCOT wind sources, as shown in Figure 1, where Panhandle wind is negatively correlated with load, while Coastal wind is positively correlated with load. Incorporating these observations into models produces very different impacts in terms of reliability planning, Dr. Mauch noted. Another example involved a 2023 PJM ISO analysis of Winter Storm Elliot, which demonstrated relationships between extreme weather and forced outages for natural gas and coal. During Elliot, PJM systemwide outages totalled 24% of capacity, driven primarily by natural gas and coal (representing 70% and 15%, respectively, of load loss). Similar outcomes occurred in ERCOT during Winter Storm Uri in 2021, further illuminating the need to account for weather-correlated outages that align with high load.  

A diagram of a wind systemDescription automatically generated with medium confidence
Figure 1: Correlations between resource type/location and load, ERCOT

Better Models for Navigating the Energy Transition

Dr. Mauch identified best practices, derived from years of experience working with Ascend's PowerSIMM™ reliability modelling platform. He noted three areas of high importance:

  • Using simulations that are not restricted to solely historical observations.
  • Accounting for evolving load patterns.
  • Proper modelling of energy storage in resource adequacy models.  

Weather-driven simulations need to provide accurate and realistic representations of renewables, load, and forced outages, and correlations between variables must be maintained. System planners should scale loads to match future expectations for demand growth and load shapes driven by increasing electrification of buildings and vehicles. Load must also be simulated under a wide range of conditions to account for changing patterns and evolving peak demand levels. Finally, energy storage should be modelled using state-of-charge tracking with realistic discharge assumptions that account for uncertainty in storage management. Dr. Mauch added that analysis related to storage, and especially long-duration storage, remains an emerging challenge for resource adequacy models, given the relative newness of storage in resource portfolios.

Implications for Using Weather-Correlated Factors in Reliability Models

To clearly capture correlations between load, resource types, and forced outages, Mr. Brode provided a reliability risk comparison in which the same system was analysed with and without weather-driven, correlated unit outages, as shown in Figure 2. An approach that uniformly distributes outages throughout the year will concentrate risk during the summer months. A weather-correlated approach, however, provides a more accurate lens through which to consider risk, revealing potential for forced outages during winter months.

A graph showing the weather forecastDescription automatically generated
Figure 2: Reliability risk comparison - uniform distribution vs. weaterh correlated model

Correctly assessing outage risk requires understanding the factors creating the shortage. As an example, Mr. Brode pointed to renewable-heavy systems where the majority of risk and load shed occurs in the late afternoon and early evening as solar ramps down and wind may drop of as well. Including weather-correlated outages produces scenarios where risk shifts away from purely summer afternoons and into winter mornings, during the coldest part of the day where unit outages might be higher. The ability of a resource to add reliability to the system is measured through their electric load carrying capacity (ELCC) accreditation and resource management within ISOs.

Including weather-correlated outages in resource adequacy models can also produce profound impacts in terms of maximizing value and driving additional resource choice, as shown in Figure 3. A weather-driven correlated outage lens makes it clear that not all resources add equal value and reveals latent value that could be extracted and monetized. For instance, choosing to add four-hour duration storage of equivalent capacity rather of thermal generation produces a 15% positive change in capacity value, which translates directly to the resource's economic value.

A graph showing the solar energyDescription automatically generated
Figure 3: ELCC with weather-correlated outages

In renewable-heavy systems, correctly assessing ELCC becomes even more dependent on correlations between load, resource type, and weather. Failing to correctly model positive or negative correlations of unit outages to weather can lead to major resource adequacy risk and inaccurate conclusions related to system capacity. Navigating these dynamics requires updated modeling that uses independent simulations to account for interconnectedness in key variables used for resource adequacy planning, as well as for characterizing uncertainty related to load, demand, population growth, and generation. Mr. Brode noted that weather-driven simulations provide realistic samples that maintain structural relationships between variables while incorporating future expectations related to load growth and daily demand patterns.

Evolving to Meet Long-Term Reliability Planning Needs

Dr. Mauch concluded by pointing out that long-term system reliability planning models must be updated to account for a changing climate. Temperatures across the U.S. have steadily increased during the past century. Heat waves occur more often and last longer. As extreme weather causes more loss-of-load hours, the need for more precise models becomes even more apparent. Going forward, energy reliability planners must also ensure that their models are not underestimating the capacity needed to serve load in a warming world with more frequent extreme weather events.

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