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| − | = Introduction =
| + | {{DISPLAYTITLE: Resource Adequacy Assessment Resource Center}} |
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| − | Traditional utility planning has always involved the production of various forecasts of future demand, with the goal of ensuring investment in new resources, including transmission, that would result in sufficient generation and transmission to serve demand over some defined future period. This planning process is composed of multiple processes and models, including power flow modeling and production simulation modeling (sometimes called power-cost modeling). A precursor in the planning process is a resource adequacy (RA) study that provides an assessment of whether future resources are sufficient to provide for demand at all times.
| + | <div style='text-align: center; text-size:18px;'> |
| | + | __NOTOC__ |
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| − | Prior to the widespread adoption of renewable energy sources (not counting hydro), a typical RA study would consist of performing a simple calculation—calculating the percentage by which installed capacity would exceed peak demand. Ratios (called “planning reserve margins,” or PRM) ranging from approximately 11% to 16% were generally regarded as achieving an acceptable level of RA.
| + | [https://msites.epri.com/resource-adequacy Click to access Resource Adequacy site ] |
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| − | However, as early as 1947, work had begun on probabilistic assessments of RA. This work recognized that, because different generating resources have different failure rates, that two otherwise identical systems, but with differing unit failure rates, would not achieve the same level of reliability. Moving to a reliability framework in which we can measure the degree to which a system can provide for customer demand—or conversely, the degree to which it fails—began to move the industry into more accurate RA assessments.
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| − | This guide focuses on RA assessments in systems with high/growing levels of variable generation (VG), storage, and demand response (DR). An essential component of this discussion centers around the contribution of VG to RA, which differs significantly than for conventional generation.
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| − | To illustrate important concepts in RA with renewables, this guide introduces some of the early studies in RA involving renewables, and then moves to more recent studies and inquiries. As renewable integration studies evolved, so did the methods and sophistication of the assessments.
| + | [[Category:Resource Adequacy]] |
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| − | The plan for this guide is to introduce RA concepts and metrics, followed by a discussion of other influences on RA. We then move to a discussion of RA with renewables, including assessment methods to calculate the capacity contribution of renewables. After a discussion of alternative metrics proposed for RA with renewables, we describe modeling styles that may be useful in incorporating increased levels of DR and storage.
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| − | == Recommendations ==
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| − | Models and methods for undertaking high-quality resource adequacy assessments are readily available and offer significant capabilities. When conducting a RA assessment, there are many options available, and these can be selected based on the study objectives.
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| − | As more VG is added to the bulk power system, there is increasing interest in LOLP metrics, and the relationship between some of these metrics is in the early stages of exploration. The traditional planning reserve margin is becoming less relevant as a result. RA metrics include
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| − | * LOLE, measured in days/year, hours/year
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| − | * LOLP, probability of insufficient resources; also used as a generic term for related metrics
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| − | * LOLH, number of hours with loss-of-load events
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| − | * EUE, expected unserved energy
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| − | The terminology “loss of load” that is part of the LOLP metric doesn’t necessarily mean that load is dropped. In large, interconnected systems, an LOLP event may instead consist of insufficient resources within the balancing region that may result in some combination of unintended flows, emergency operations, or emergency imports.
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| − | Calculation methods include convolution and various types of Monte Carlo. Convolution is generally faster computationally, and it is therefore an attractive choice for studies that evaluate a large number of scenarios. Monte Carlo simulations explicitly simulate outages, and because they are run for many iterations, can often provide deeper insights.
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| − | There are many assumptions and key factors that can have a significant impact on RA assessments. Some of these include:
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| − | * Long term weather patterns
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| − | * Imports and exports
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| − | * Interconnection with neighboring systems
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| − | * Planned maintenance, especially during relatively high-risk periods
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| − | * Forced outage rate of the generation fleet
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| − | * Pattern of VG generation, which is expected to vary from year to year, and the relationship to demand
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| − | * Target level of reliability
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| − | * Demand response and storage, and how they are represented in the model
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| − | Because of these factors, it may be difficult to find a simple method for calculating either RA or ELCC. Therefore, we recommend that any simple methods that are adopted should be regularly benchmarked against a reliability model, and it should be trued up as appropriate.
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| − | For systems experiencing high growth in VG, it is advisable to observe how the various LOLP metrics behave as more VG is added to the system so that additional insights can be obtained as the industry evaluates potentially alternative metrics and targets.
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| − | LOLE-FLEX can provide useful insights regarding system flexibility, especially with the increasing VG. We strongly encourage experimentation with this metric, and caution in applying targets that conflict with NERC BAL standards.
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