Resource Adequacy Planning
Summary Description
Resource adequacy planning is a structured analytical process used to assess whether an electricity system has sufficient capacity and energy supply to reliably meet forecasted electricity demand over defined planning horizons, while explicitly accounting for uncertainty in demand, generator availability, and system conditions. Resource adequacy assessments compare projected demand with available supply, incorporating generator outages, deratings, transmission constraints, and reserve requirements to determine the risk of supply shortfalls and the need for corrective actions (IESO (2025)).
Unlike capacity expansion modelling, which focuses on identifying least-cost long-term investment pathways, resource adequacy planning evaluates the reliability sufficiency of a given resource portfolio under expected and stressed conditions. These assessments are performed across multiple time horizons, including long-term, seasonal, near-term outlooks, with increasing levels of operational specificity as the assessment horizon shortens (IESO (2024a), IESO (2024b)).
A defining characteristic of resource adequacy planning is the use of explicit reliability criteria to determine acceptable levels of risk, such as probabilistic loss-of-load expectation criterion, which provides a quantitative benchmark against which supply sufficiency and reserve requirements are assessed. Resource adequacy assessment incorporates both deterministic and probabilistic elements. Deterministic inputs include projected demand, committed supply resources, planned outages, and transmission transfer capabilities, while probabilistic components represent forced generation and transmission outages, and load forecast uncertainty.
Weather and climate conditions play an important role in resource adequacy planning because they affect both electricity demand levels and resource availability. Adequacy methodologies therefore explicitly incorporate weather-sensitive demand forecasts and uncertainty distributions from historical weather variability, to present the impact of extreme temperatures and other weather conditions on reliability risk (IESO (2025)).
Overall, resource adequacy planning serves as a reliability assurance function within the electricity system, translating forecasts of demand, supply availability, and uncertainty into quantitative indicators of system sufficiency. The results inform decisions related to reserve requirements, outage coordination, market signals, and operational readiness.
Role in the Electricity System
Resource adequacy planning functions as a key part of reliability assurance within the electricity system. Its role is to determine whether the electricity system, given a defined set of resources and operating assumptions, meets established reliability standards over specified time horizons, and to identify when additional actions or resources may be required (IESO (2024a)).
In practice, resource adequacy planning provides a link between long-term planning and system operation. Long-term and seasonal adequacy assessments inform system planners about potential future capacity shortfalls and guide decisions related to reserve requirements, outage coordination, and resource procurement timing. Near-term adequacy assessments support operational readiness by identifying periods of elevated reliability risk based on updated demand forecasts, outage schedules, and system conditions (IESO (2024b), IESO (2025)).
Resource adequacy planning also supports coordination across system functions by providing a common analytical basis for transmission planning, outage management, and market signaling. By quantifying the timing, location, and duration of potential reliability risks, adequacy assessments inform decisions on outage rescheduling, emergency preparedness measures. They also provide forward-looking signals regarding emerging capacity needs, indicating when additional resources may be required to maintain reliability and helping guide procurement and investment decision, while remaining analytically distinct from real-time dispatch and market operations (IESO (2024a), IESO (2025)).
:::
Models
RAPs typically rely on probabilistic simulation models that evaluate system performance under uncertainty. These models simulate many realizations of demand, generator availability, and weather-dependent resources to assess whether a given resource portfolio can reliably meet electricity demand. A key feature of adequacy models is their chronological and weather-driven structure, which evaluates whether available resources can reliably meet demand across a wide range of possible system conditions. Climate data play a central role in this framework by shaping both electricity demand and weather-dependent resource availability, enabling the models to capture variability, extreme events, and correlated risks under current and future climate conditions.
To address non-stationary climate conditions, recent approaches extend historical weather-based planning by incorporating climate-projected datasets or climate-informed scenarios that reflect future changes in temperature, hydrology, and renewable resource patterns (EPRI (2024), Carvallo et al. (2023)). In practice, this may involve using ensembles of downscaled climate projections or adjusting historical weather datasets to represent future conditions, enabling adequacy models to evaluate reliability under a range of plausible climate futures rather than relying solely on historical variability.
Key Inputs
-Electricity demand and weather-dependent load profiles A core input to resource adequacy assessment is an electricity demand profile. These profiles are explicitly weather-sensitive, reflecting the dependence of electricity demand on temperature and other meteorological variables. Rather than relying on a single forecast, adequacy studies evaluate demand across many years of historical or synthetic weather, generating probabilistic load distributions that capture extreme conditions and tail risks (IESO (2024a), Carvallo et al. (2023)). However, reliance on historical weather implicitly assumes stationarity, which may not hold under climate change. Recent guidance therefore recommends assessing adequacy using climate-projected weather years to capture future variability and to update load and resource availability assumptions accordingly (EPRI (2024)). -Resource portfolio Resource adequacy models require a detailed representation of the existing and committed resource portfolio, including generation, storage, demand-side resources, and imports. For each resource, inputs include installed capacity, availability status, location, and operational characteristics (IESO (2024a)). -Generator availability, forced outages, and deratings Probabilistic representation of resource availability is a defining input to resource adequacy planning. Thermal generators are characterized using forced outage and derating parameters, commonly expressed through metrics such as the equivalent forced outage rate on demand, derived from historical performance data (IESO (2024a), NERC (2025)). Adequacy models assume that outage events occur stochastically and generate multiple outage patterns for each resource across simulations. Recent guidance highlights the need to better capture correlated outages and weather-related availability risks, particularly during extreme events (Carvallo et al. (2023)). -Hydro and weather-driven resources For hydroelectric and weather-driven resources, adequacy assessments require inputs describing water availability, inflows, and operational constraints, typically derived from long-term historical hydrological records. In hydro-dominated systems, interannual variability in water supply is a primary driver of adequacy risks and must be explicitly represented through chronological simulations (NPCC (2024)). - Energy storage resources Energy storage resources introduce additional modelling complexity because their availability depends on their state-of-charge and charging opportunities. Climate and weather conditions influence their adequacy contribution by affecting electricity demand and the availability of weather-dependent generation used for charging (NERC (2025); Carvallo et al. (2023)).- -Transmission deliverability and interregional exchanges Resource adequacy planning incorporates transmission transfer capabilities that constrain the deliverability of capacity within and between regions. These limits are typically provided exogenously from transmission planning studies and treated as fixed constraints in adequacy models (IESO (2024a)). Imports and exports are represented with assumptions about availability during system stress conditions. Best practice emphasizes caution in assuming firm imports during extreme weather conditions, particularly when neighbouring regions may be experiencing correlated demand or resource shortages (Carvallo et al. (2023), NESO (2025)).
Model Outputs
-Reliability metrics and adequacy indicators The primary outputs of resource adequacy assessments are reliability metrics that measure the frequency, duration, and magnitude of potential shortfall events. Traditionally, these metrics include loss of load expectations (LOLE), which represents the expected number of days or hours per year in which firm load cannot be served due to insufficient resources (IESO (2024a), NERC (2025)). Event-based or severity-based indicators provide additional information on the magnitude and characteristics of potential shortfall events. These metrics can be evaluated across all adequacy simulations, not only during extreme tail-risk conditions. For example, output variables include the expected number of shortfall events, maximum duration of shortfalls, peak capacity shortfalls, and total unserved energy over a year (NPCC (2024)). -Capacity surplus and deficiency signals Resource adequacy models also produce outputs that indicate capacity surplus or deficiency relative to reliability metrics. These outputs may be expressed as the amount of additional capacity required to meet adequacy criteria, or conversely, the margin by which the existing system exceeds minimum reliability requirements. Such results provide planners with actionable signals regarding the timing and magnitude of potential reliability concerns (IESO (2024a), NARUC-NASEO (2021)). -Temporal and conditional characterization of risk Chronological adequacy models identify the seasons, hours of day, or system conditions under which shortfalls are most likely, such as periods of extreme temperature, low renewable output, or constrained transmission. These outputs support diagnostics analysis by linking reliability risk to underlying drivers, including weather conditions, outage patterns, or energy limitations of storage and hydro resources (IESO (2024b), NPCC (2024)).
:::
Detailed Discussion
RAPs focus on whether the existing or planned power system can reliably meet demand under a wide range of operating conditions. As a result, weather variability and extreme events play a more important role in adequacy assessments, particularly in systems with increasing shares of weather-dependent generation. Extreme weather events and rare system stress conditions are therefore typically evaluated more directly within adequacy assessments than in capacity expansion modelling.
In practice, adequacy studies rely on probabilistic modelling frameworks that simulate system performance under many different weather conditions. Unlike deterministic expansion models, adequacy assessments typically evaluate multiple simulations using different weather profiles to capture variability in electricity demand, renewable generation, and hydrologic conditions. This probabilistic approach allows planners to estimate reliability metrics such as loss-of-load probability or expected unserved energy.
Historical weather datasets are commonly used to generate multiple demand and renewable generation profiles. However, the reliance on historical weather data presents challenges when considering future climate change. While probabilistic modelling frameworks are well suited to representing weather variability, historical datasets may not fully capture future climate conditions, including shifts in temperature patterns, changes in renewable resource availability, or increased in the frequency and intensity of extreme weather events. As a result, adequacy assessments may underestimate future reliability risks if climate changes are not adequately represented.
To address this challenge, climate projections could be used to develop climate-conditioned weather scenarios that reflect possible future weather patterns. Climate model projections may provide insight into potential changes in temperature distributions, the frequency and duration of extreme heat events, or shifts in renewable resource patterns. These projected changes can be translated into inputs for RAP, informing the development of extended weather datasets or targeted stress-testing scenarios. Such approaches allow planners to evaluate system performance under plausible future climate conditions while maintaining the probabilistic structure of existing adequacy modelling frameworks.