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The Value of Resource Adequacy
Why reserve margins aren’t just about keeping the lights on.
recent outages in Texas and Arizona are examples of such reliability events that would only be captured by modeling a full ( e.g., 30-year) distribution of weather and its impact on load, resources, and fuel availability. To capture these costs, a model with an hourly economic dispatch is needed, with the ability to run a sufficiently wide range of scenarios—including extreme combinations that create physical reliability problems.
Economic reliability analysis combines production cost and reliability simulation techniques. In particular, they capture all production and scarcity costs of power above the variable cost of the marginal capacity resource, which is typically a new combustion turbine. In addition, these analyses require a realistic distribution and sufficiently large number of scenarios of weather, unit performance, and economic growth to capture the extreme conditions during which reliability is a concern. Finally, transmission capabilities and neighboring systems must also be analyzed in detail because reliability support from neighboring systems might be limited by both transmission and resource availability constraints.
To illustrate the application of economic considerations to reliability analysis, Astrape Consulting performed a case study using an actual (herein generalized) power system that includes approximately 40,000 MW of capacity with a weather normalized peak load of approximately 35,000 MW and 10,000 MW of inter-ties with multiple neighboring systems.
The Strategic Energy and Risk Valuation Model (SERVM) was used in this case study to perform economic reliability modeling. 7
SERVM commits and dispatches generation economically to meet load plus operating reserves during all 8,760 hours of a year and then calculates reliability costs and other reliability metrics such as LOLE and LOLH. SERVM is a multi-area model that models directly-interconnected neighboring regions to simulate out-of-region purchases over tie lines when necessary for reliability. To gain an accurate picture of the system’s physical and economic reliability-related costs, the analysis involved 112,000 full-year simulations 8 (each for 8,760 hours) for each reserve margin level analyzed. The simulations included 40 historical weather years in which load, resources, and fuel availability were dependent on historic hourly weather data. The results from these simulations were then used to determine the average and distribution of reliability-related costs for different reserve margin levels. Simulating a sufficiently large range of reserve margins thus allows for both the identification of 1) the reserve margins that yield the lowest average costs and 2) an assessment of the cost uncertainty, including the risk (probability) that actual outcomes significantly exceed these average costs.
Defining Reliability-Related Costs
Setting target reserve margins based on economic reliability simulations requires balancing the costs of adding new capacity against the benefit of adding that capacity. For the case study, new capacity was assumed to be a combustion turbine. In other regions, that might not be the appropriate marginal new resource. It’s also possible to evaluate a supply curve of new capacity that stretches from lower-cost demand response resources to higher-cost additions of new physical generation.
As the level of installed capacity resources changes, the total benefit of the additional capacity must be captured, as well as the costs of that capacity. This means the analysis must