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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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| − | = Resource Adequacy Measurement=
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| − | == Evolution of Resource Adequacy ==
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| − | Planning for resource adequacy has been driven by operational requirements to ensure that the power system could evolve and adapt so that demand could be met continuously. Future assessments have often included seasonal forecasts, to ensure that the fleet of available generation would be adequate to meet the upcoming season. Longer term assessments could cover a few, or many, future years. Because plant construction has historically taken many years, RA assessments would often cover a 10-year horizon, and in some cases, longer. More recently, construction times have been in the region of months, presenting new challenges for RA.
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| − | === Basic Indicators ===
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| − | Prior to the adoption of significant renewable energy sources, relatively simple indicators could provide general information regarding the level of adequacy a given system could achieve. This primarily involved the widely-used planning reserve margin (PRM), calculated as the percentage by which installed capacity exceeds peak demand. Common accepted levels for the PRM ranged (and still range) from approximately 11% to 16%. Levels below this range were generally thought to indicate potential reliability problems relative to RA, whereas levels above this range generally indicated sufficiency, if not an oversupply.
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| − | For many years the power system industry has tracked various metrics associated with plant outages. From this evolved a series of reliability metrics, and many of these are collected and archived by the North American Electric Reliability Corporation (NERC) in the Generating Availability Data Set [1]. From the various data inputs, NERC has defined a family of metrics that account for generators’ forced outage rates. Of the several forced outage rates (FOR), the effective forced outage rate-demand (EFORd) is commonly used in the power system industry because it accounts for plant failures during times of need.
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| − | The use of EFORd (and related metrics) made it possible to introduce a basic notion of reliability into resource adequacy. Interpreting EFORd as the rate at which a unit could be expected to fail, the plants’ capability during peak periods could now contain a reliability element. The easiest way to do this is to calculate “unforced capacity” (UCAP). For a plant with capacity C<sub>p</sub> and EFORd F<sub>p</sub> we can calculate
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| − | <blockquote>UCAP = C<sub>p</sub> (1 - F<sub>p</sub>)
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| − | </blockquote>
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| − | Although UCAP isn’t itself a reliability metric, it is a much better indicator than PRM, especially if the PRM is based on installed capacity (ICAP). Table 2‑1 illustrates this for two simple, identical systems. Each system has several identical 200 MW units, but the EFORd’s differ between the two systems. They both have the same PRM, slightly less than 16%. But the differences in the two systems’ UCAPs are a good indication that they will not have the same ability to serve peak demand reliably.
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| − | Table ‑ UCAP and PRM for Two Hypothetical Systems
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| − | {|
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| − | !
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| − | ! '''System A'''
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| − | ! '''System B'''
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| − | |-
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| − | | '''Number of 200 MW Units'''
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| − | | 11
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| − | | 11
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| − | |-
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| − | | '''System ICAP'''
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| − | | 2200
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| − | | 2200
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| − | |-
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| − | | '''EFORd on all units'''
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| − | | 0.1
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| − | | 0.2
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| − | |-
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| − | | '''System UCAP'''
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| − | | 1980
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| − | | 1760
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| − | |-
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| − | | '''Peak Demand'''
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| − | | 1900
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| − | | 1900
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| − | |-
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| − | | '''PRM'''
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| − | | 15.8%
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| − | | 15.8%
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| − | |}
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