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Calculating capacity margins is a fundamental practice in power system planning and operations that ensures electric grids maintain adequate resources to meet demand reliably under all conditions. The electricity utility industry employs a simple strategy for maintaining reliability: always have more supply available than may be required, yet it can be difficult to forecast future electricity demand, and building new generating capacity can take years, so the industry regularly monitors the supply situation using a measure called reserve margin. This comprehensive approach to capacity planning protects consumers from blackouts while balancing the economic costs of maintaining excess generation capacity.
Understanding Capacity Margins and Reserve Margins
Capacity margin, often referred to as reserve margin in industry practice, represents the cushion of generating capacity available beyond what is needed to meet peak electricity demand. Reserve margin is calculated as capacity minus demand divided by demand, where “capacity” is the expected maximum available supply and “demand” is expected peak demand, and it is calculated for electric systems or regions made up of a number of electric systems. This metric serves as a critical indicator of power system health and reliability.
For instance, a reserve margin of 15% means that an electric system has excess capacity in the amount of 15% of expected peak demand. The formula can be expressed mathematically as:
Reserve Margin (%) = [(Total Available Capacity – Peak Demand) / Peak Demand] × 100
This straightforward calculation provides power system planners with a quick assessment of whether sufficient generation resources exist to maintain reliability. However, the simplicity of the formula belies the complexity of determining appropriate target levels and accounting for various uncertainties in modern power systems.
The Difference Between Planning and Operating Reserve Margins
It’s important to distinguish between different types of reserve margins used in power system planning and operations. Installed reserve margin (IRM) is the amount of the generating capacity in excess of the expected load, calculated to satisfy the loss of load expectation, typically 1 day in 10 years, and the IRM is different from the operating reserve margin (ORM). The ORM calculations account for the current generation and transmission outages and assume that all the demand response and interruptible power loads are connected, and ORM is thus lower than IRM.
Planning reserve margin is the difference between available capacity and peak load, normalized by peak load, in units of percent; for example, a 20 percent planning reserve margin would imply that planned single-hour capacity should exceed expected load by 20 percent. Planning reserve margins focus on long-term resource adequacy, while operating reserves address real-time grid balancing needs.
The Role of Capacity Margins in Power System Reliability
Capacity margins serve multiple critical functions in maintaining electric grid reliability. They provide a buffer against various uncertainties and contingencies that can threaten the balance between electricity supply and demand. Understanding why these margins are necessary helps explain the complexity of modern power system planning.
Protection Against Demand Uncertainty
The need for generation resources above peak load is driven by several factors, with the target planning reserve margin most commonly defined by using median annual peak load; thus additional generating capacity is needed to cover years in which demand eclipses this level such as during an extremely hot summer. Weather-driven demand variations represent one of the most significant sources of uncertainty in power system operations.
A dramatic example of this occurred during the Texas heat wave in August 2011. The Texas heat wave in August 2011 led to a supply emergency that illustrates the importance of reserve capacity, as the 2011 Summer Short-term Reliability Assessment projected ERCOT total internal demand would be equally likely to be above or below 64,964 megawatts (MW), but an unprecedented heat wave drove demand to record levels: 68,294 MW, or over 5% above the level quoted in the Summer Assessment. Reserve margins are maintained, at significant cost, for just such unanticipated emergencies, and operators were able to avoid loss of customer load (a blackout), but the ERCOT system used nearly all of its reserve generating capacity.
Accounting for Generation Outages
Generation resources are subject to forced and planned outages and may be unavailable during some hours of the year when needed. Power plants require regular maintenance, and equipment failures can occur unexpectedly. Capacity margins ensure that when individual generating units are offline, whether for scheduled maintenance or unplanned repairs, sufficient capacity remains available to meet demand.
Reserve capacity is necessary to cater for any loss of generating capacity due to faults or planned maintenance and refurbishment. The probability and duration of these outages must be carefully analyzed when determining appropriate reserve margin levels. Larger generating units typically require higher reserve margins because their sudden loss represents a more significant impact on system capacity.
Meeting Regulatory Requirements
The North American Electric Reliability Council (NERC) mandates that utilities hold operating reserves for interconnection reliability purposes which must be accounted for. These regulatory requirements ensure that power systems maintain minimum reliability standards across interconnected regions. Each assessment area’s anticipated reserve margin (ARM) is compared against its Reference Margin Level (RML)—the threshold margin established by the state, provincial authority, ISO/regional transmission organization (RTO), or other regulatory body to provide the level of resources needed to meet reliability criteria.
Methods for Calculating Capacity Margins
Power system planners employ various methodologies to calculate appropriate capacity margins, ranging from simple deterministic approaches to sophisticated probabilistic models. The choice of method depends on system characteristics, data availability, and the desired level of analytical rigor.
Deterministic Methods
Deterministic approaches to capacity margin calculation use fixed assumptions about system conditions and establish margins based on engineering judgment or historical experience. While traditional methods use deterministic approaches, modern power systems increasingly rely on probabilistic methods, with the transmission reliability margin calculated as a fixed fraction or percentage of the total transfer capability, based on engineering judgment or historical data, assuming static system conditions and predefined margins for uncertainties.
The deterministic method is simple and efficient but limited in terms of accounting for the dynamic variability from renewable energy sources, often leading to over-conservative or underutilized margins and inefficient transmission network use. Despite these limitations, deterministic methods remain widely used due to their simplicity and ease of implementation, particularly in systems with relatively stable generation portfolios.
Probabilistic Methods and Loss of Load Metrics
Probabilistic methods provide a more sophisticated approach to capacity margin calculation by explicitly modeling the uncertainties in both demand and generation availability. The most common probabilistic reliability metric is Loss of Load Expectation (LOLE).
The LOLE is a probabilistic measure that seeks to quantify over the period of a year the number of hours of failure, and it is expected in any one year that the power system network may fail. The adequacy standard should satisfy the chosen reliability index, typically the loss of load expectation (LOLE) of 1 day in 10 years (so called “1-in-10”), with installed reserve margin (IRM) being the amount of the generating capacity in excess of the expected load, calculated to satisfy the loss of load expectation, typically 1 day in 10 years.
Utilities confirm the adequacy indicated by the calculated reserve margins through detailed reliability simulations that compare expected load profiles with specific generating unit forced outage rates and maintenance schedules to yield LOLE, LOLP or expected unserved energy (EUE) values. These simulations account for the statistical nature of both load variations and generation outages, providing a more accurate assessment of system reliability than deterministic methods.
Capacity Expansion Modeling
The planning reserve margin is the predominant metric used in long-term planning models to ensure the resource adequacy of projected power systems. Capacity expansion models optimize the timing and type of new generation investments needed to maintain adequate reserve margins over multi-year planning horizons. These models consider factors such as load growth projections, planned retirements of existing generation, fuel costs, capital costs of new resources, and environmental constraints.
Stochastically analyzing a utility’s potential loads over a wide range of system conditions and combining that with a stochastic analysis of the availability of resources to meet these loads is the foundation of the expected unserved energy (EUE) calculation, with models like the Renewable Energy Capacity Planning Model (RECAP), an open-source, loss-of-load-probability model that calculates system reliability as a function of detailed system parameters.
Economic Optimization Approaches
Rather than targeting a fixed reliability standard, economic optimization approaches seek to balance the costs of maintaining reserve capacity against the costs of potential service interruptions. The economically optimal reserve margin occurs where the marginal benefits of additional capacity matches the marginal cost of a new unit, with this approach providing an overview of these methods and explaining the choice of economic analysis for power systems.
This approach requires estimating the value of lost load (VOLL), which represents the economic cost to customers of electricity service interruptions. The value of lost load is often represented at values such as $9,000/MWh, which falls in the lower end of the spectrum of various analyses and is believed to be a conservative assumption. By comparing the cost of adding capacity to the expected reduction in customer outage costs, utilities can determine the economically optimal planning reserve margin.
Factors Influencing Required Capacity Margins
The appropriate level of capacity margin for a given power system depends on numerous factors related to system characteristics, resource mix, and operational requirements. Understanding these factors is essential for effective capacity planning.
Peak Demand Forecasting and Variability
Accurate forecasting of peak demand is fundamental to capacity margin calculations, yet demand forecasting involves significant uncertainty. Utilities set the level of reserve margin by making reference to the maximum electricity demand as one of the most important indicators for planning and operation, which is in line with the practices adopted in the electricity industry all over the world. Factors affecting peak demand include weather patterns, economic growth, energy efficiency improvements, electrification trends, and changes in customer behavior.
The adequacy reserve margin specifies the amount of “extra” resource needed, above the forecast weather-normalized load, to cover future uncertainties, such as temperature variations and resource outages, with a separate ARM calculated for energy needs and for capacity needs. Temperature-driven demand variations represent one of the largest sources of uncertainty, particularly in regions with significant heating or cooling loads.
Generation Resource Availability and Forced Outage Rates
The reliability characteristics of generating units significantly impact required capacity margins. Different generation technologies have different forced outage rates and maintenance requirements. Generation reserve margin is a measure which shows how the capacity of power system exceeds the peak consumption, with percent reserve evaluation calculated by comparing the total installed generating capacity at peak with the peak load.
The loss of load probability is defined as the probability of the system load exceeding available generating capacity under the assumption that the peak load is considered as constant through the day, though the loss of load probability does not really stand for a probability but expresses statistically calculated value representing the percentage of hours or days in a certain time frame when energy consumption cannot be covered considering the probability of losses of generating units.
System Size and Interconnection
The level of reserve margin required is dependent on a number of factors including the size of the power system and the reliability level required, with the higher the need for reliability in a small power system, the higher the percentage reserve margin tends to be. Smaller systems typically require higher percentage reserve margins because the loss of a single large generating unit represents a larger fraction of total system capacity.
A larger area changes the diversity of loads and variable generation, increasing reliability measured with a lower Loss-of-Load-Probability (LOLP). Interconnections with neighboring systems can effectively increase system size and reduce required reserve margins by allowing access to additional resources during emergencies.
Renewable Energy Integration and Intermittency
The increasing penetration of variable renewable energy sources such as wind and solar power has significant implications for capacity margin calculations. Should there be a large penetration of variable resources, whose contribution to the peak load is less certain, the Planning Reserve Margin may increase because the capacity value of variable generation is typically a relatively small percentage of its installed capacity, depending on the level of variable generation penetration.
It is entirely possible to have wind, for example, contributing 60 percent of its installed capacity toward capacity adequacy in one area and none in another area. The capacity contribution of renewable resources depends on the correlation between renewable energy production and peak demand periods. Solar generation, for instance, may have high capacity value in systems with summer afternoon peaks but lower value in systems with evening or winter peaks.
Considerable work has been done to estimate the contribution of variable renewable energy resources, such as wind and solar, to the planning reserve margin, but little work has been done to assess what planning reserve margin should be used in planning models. This represents an ongoing area of research as power systems transition toward higher renewable energy penetrations.
Effective Load Carrying Capability (ELCC)
For renewable and other variable resources, the concept of Effective Load Carrying Capability (ELCC) provides a more accurate measure of capacity contribution than simple nameplate capacity. ELCC represents the amount of additional load that can be served at the same reliability level when a resource is added to the system.
Probably the most famous approximation method is due to Garver (1966), with the Garver technique to estimating ELCC applied to conventional generators and developed to overcome the limited computational capabilities that were available at the time, and the approach approximates the declining exponential risk function (LOLP in each hour, LOLE over a high-risk period). Modern computational capabilities allow for more sophisticated ELCC calculations that better capture the reliability contribution of variable resources.
It is necessary to have a sufficient data record to be able to evaluate, with confidence, the statistical attributes of variable generation and identify any statistical relationships with other important parameters, such as load levels (via temperature), in order to quantify contribution to capacity. This data-intensive approach requires multiple years of historical generation and load data to establish reliable statistical relationships.
Regional Variations in Capacity Margin Requirements
Capacity margin requirements vary significantly across different regions and power systems based on local characteristics and regulatory frameworks. NERC Regional Entities set their region’s target reserve margin. These regional differences reflect variations in resource mix, load characteristics, system size, and reliability preferences.
Load responsible entities in SPP’s region must have access to enough generating capacity to serve their peak consumption with at least 36% margin during the winter season and at least 16% margin during the summer, marking the first time a winter PRM requirement has been defined separately from SPP’s summer PRM requirement and was taken to ensure member utilities appropriately acquire enough generating capacity for both seasons. This example illustrates how some regions now recognize the need for different reserve margins in different seasons.
Different systems may require different Planning Reserve Margins to attain the same LOLE target, with one system potentially requiring a 15 percent Planning Reserve Margin to attain the same LOLE target as another system. This variation occurs because systems with different generation portfolios, load patterns, and operational characteristics face different reliability challenges.
Transmission Reliability Margin
In addition to generation capacity margins, transmission system reliability requires its own margin calculations. The transmission reliability margin (TRM) accounts for the uncertainties associated with the transmission system, and deregulation of power systems has increased the need for defensible calculations of transfer capability and related quantities such as the TRM.
The Transmission Reliability Margin Methodology Reliability Standard (MOD-008-1) provides for the calculation of transmission reliability margin, which describes the reliability aspects of determining and maintaining a transmission reliability margin and the components of uncertainty that may be considered when making that determination, with the purpose of this Reliability Standard being to promote the consistent and reliable calculation, verification, preservation, and use of transmission reliability margin to support analysis and system operations, as transmission reliability margin is transmission transfer capability set aside to mitigate risks to operations, such as deviations in dispatch, load forecast, outages, and similar such conditions.
Uncertainty in the parameters causes uncertainty in the transfer capability and it is assumed that this uncertainty in the transfer capability is the uncertainty to be quantified in the TRM, with the uncertain parameters including factors such as generation dispatch, customer demand, system parameters and system topology. Proper calculation of transmission reliability margins ensures that the transmission system can reliably deliver power from generators to load centers under various operating conditions.
Challenges in Modern Capacity Margin Calculations
As power systems evolve, capacity margin calculations face new challenges that require updated methodologies and approaches. Understanding these challenges is essential for maintaining reliable power systems in the future.
Dynamic System Conditions
The conventional TRM calculation method has several drawbacks: fixed confidence factors may lead to overly conservative or insufficient TRM estimates based on real-time conditions; it does not dynamically adjust to uncertainties like rapid loads or renewable generation changes; and the TRM is recalculated periodically rather than continuously updated to reflect the real-time system changes.
The traditional TRM calculation methods typically rely on fixed margins or predetermined safety factors, which do not adapt to the rapidly fluctuating conditions inherent in modern renewable-rich grids. This limitation has driven research into dynamic margin calculation approaches that can adapt to changing system conditions in real-time.
Data Quality and Availability
Accurate TRM estimation depends on high-quality data, which may not always be readily available or reliable, especially in regions with less advanced monitoring infrastructures, and inadequate data quality can lead to suboptimal asset management decisions, affecting the reliability and efficiency of power systems. The increasing complexity of power systems with diverse generation resources requires more extensive data collection and analysis capabilities.
Balancing Reliability and Economics
The carrying cost of additional capacity is modest but incurred each year, and through time, both result in equivalent average costs, but the difference in costs for a specific year can be dramatically different, depending on whether a reliability event occurred, and to the extent that utility customers are risk-averse, they will seek less variance in total annual costs and should prefer a higher PRM to a lower PRM given that the incremental annual systems costs are equal.
Reserve margins below regulatory requirements indicate a need for additional capacity or enhanced flexibility resources, while excessively high reserve margins suggest underutilized resources, leading to unnecessary costs. Finding the right balance between reliability and cost-effectiveness remains a central challenge in capacity margin planning.
Practical Steps for Capacity Margin Analysis
Power system planners and operators follow systematic processes to calculate and maintain appropriate capacity margins. These practical steps ensure that capacity planning decisions are based on sound analysis and comprehensive data.
Data Collection and Assessment
The first step in capacity margin analysis involves gathering comprehensive data about the power system. Required data includes total installed generation capacity by plant type (MW), peak demand data (historical and forecasted) for the analysis period, outage rates and maintenance schedules for generation assets, contribution of renewable energy sources during peak demand periods, and reserve margin requirements specified by regulators or grid operators.
Historical data collection requires significant effort, with a longer dataset needed to insure robustness of results when studying power system reliability relative to other utility applications. Multi-year datasets help capture the full range of variability in both load and generation performance.
Scenario Development and Modeling
Planners use the formula Reserve Margin (%) = [(Total Available Capacity – Peak Demand) / Peak Demand] × 100 and perform calculations for different scenarios, such as normal operations and high-demand periods, while assessing the total generation capacity, accounting for planned outages and derated capacities of plants.
Analysis outputs include a table summarizing peak demand, available capacity, and calculated reserve margins for each scenario, a line chart showing reserve margin trends over time or under different conditions, scenario models illustrating the impact of contingencies on reserve margins, and a risk matrix identifying periods of high reserve inadequacy risk.
Benchmarking and Comparison
Planners benchmark reserve margin calculations against minimum regulatory requirements or regional standards and industry benchmarks for reserve margins, comparing reserve margin performance with similar utilities or regions to identify best practices. This comparative analysis helps identify whether a system’s reserve margins are appropriate relative to similar systems and regulatory expectations.
Regional estimates of reserve margins are compared to pre-determined target levels to assess supply adequacy. Regular monitoring and comparison against targets enable early identification of potential reliability concerns.
Strategies for Maintaining Adequate Capacity Margins
When capacity margin analysis reveals potential shortfalls or identifies opportunities for improvement, power system planners can implement various strategies to maintain adequate margins and enhance reliability.
Capacity Additions and Resource Procurement
Utilities can invest in additional capacity, such as fast-ramping peaking plants or renewable energy projects, to increase available resources, and optimize maintenance schedules to ensure high availability during peak demand periods. The type and timing of capacity additions should be carefully planned to address specific system needs while minimizing costs.
Building a power supply that meets the PRM requirement is expected to maintain reliable operation while meeting unforeseen increases in future load (e.g. extreme weather) and unexpected outages of existing capacity, and from a planning perspective, planning reserve margin trends indicate whether capacity additions are keeping up with load growth.
Demand-Side Resources
Utilities can deploy energy storage systems to provide contingency support during demand spikes or outages, and enhance demand response programs to reduce demand during critical periods and improve flexibility. Demand-side resources provide an alternative to traditional generation capacity for maintaining adequate margins, often at lower cost and with faster deployment timelines.
Demand response programs allow utilities to reduce peak demand by providing incentives for customers to curtail or shift electricity consumption during critical periods. These programs effectively increase the capacity margin by reducing the denominator in the reserve margin calculation rather than increasing the numerator through additional generation.
Enhanced Interconnection and Resource Sharing
Utilities can strengthen grid interconnections to leverage external resources during shortfalls, and regularly update demand forecasts and capacity assessments to ensure reserve margins align with evolving conditions. Interconnections with neighboring systems provide access to additional resources during emergencies and allow for more efficient use of generation capacity across larger geographic areas.
A reliable bulk power system with high penetrations of variable generation may require an iterative approach between generating resource and transmission planning, as the transmission system increases the availability of remote generation (and loads) that alters the character of the resource mix. Coordinated planning of generation and transmission resources is essential for maintaining adequate capacity margins in modern power systems.
The Future of Capacity Margin Calculations
As power systems continue to evolve with increasing renewable energy penetration, electrification of transportation and heating, and changing load patterns, capacity margin calculation methodologies must also advance to address new challenges and opportunities.
Advanced Analytical Techniques
Research examines the potential for machine learning, artificial intelligence, and real-time forecasting models to optimize TRM calculations in dynamic power system environments, offering a comprehensive analysis of the TRM estimation methods, emphasizing the challenges posed by high renewable energy integration and system uncertainties, and by identifying the gaps in the dynamic modeling approaches and exploring the integration of data-driven techniques, aims to provide actionable insights for developing adaptive and resilient TRM assessment strategies suited to modern power grid environments.
Machine learning and artificial intelligence offer promising approaches for improving capacity margin calculations by identifying complex patterns in historical data and providing more accurate forecasts of both demand and renewable energy production. These advanced techniques can adapt to changing system conditions more rapidly than traditional statistical methods.
Dynamic and Adaptive Margins
Research presents significant advancements in the evaluation of margins for power systems with high levels of renewable energy integration, with the primary finding being the development of a dynamic margin calculation framework, which holds substantial potential for overcoming the limitations of the traditional static methods. Dynamic margin calculations that adjust in real-time based on current system conditions represent a significant advancement over traditional static approaches.
The inability to dynamically adjust the security margins leads to inefficient transmission capacity use and increased grid instability risks, and the complex interactions of system uncertainties call for a more adaptive approach to ensure grid security and operational efficiency. Future capacity margin methodologies will likely incorporate real-time data and adaptive algorithms to optimize reliability while minimizing costs.
Evolving Reliability Standards
The expected Planning Reserve Margin is not useful without providing a corresponding target Planning Reserve Margin value and LOLE target, as by itself the expected Planning Reserve Margin cannot communicate how reliable a system is. As power systems change, reliability standards and metrics may need to evolve to better reflect the actual reliability experienced by customers.
Typically, U.S.-based models use the North American Electric Reliability Corporation (NERC)-recommended reserve margin levels, however, historical reserve margins have often exceeded the NERC-recommended levels, suggesting that the use of NERC-recommended levels in planning models may negatively bias projected future capacity investments relative to real-world trends. Ongoing research and analysis will help refine appropriate reserve margin targets for future power systems.
Key Considerations for Capacity Margin Planning
Effective capacity margin planning requires careful consideration of multiple factors and stakeholder perspectives. Power system planners must balance competing objectives while ensuring reliable service to customers.
Critical Planning Factors
Several key factors must be considered when establishing and maintaining appropriate capacity margins:
- Peak demand forecasts: Accurate projections of future peak demand considering weather variability, economic growth, energy efficiency, and electrification trends
- Generation availability: Realistic assessment of generation resource availability accounting for forced outages, planned maintenance, and performance degradation
- Demand response capabilities: Evaluation of demand-side resources that can reduce peak demand during critical periods
- Renewable energy variability: Proper assessment of the capacity contribution of variable renewable resources using methods such as ELCC
- Transmission constraints: Recognition of transmission limitations that may prevent available generation from reaching load centers
- Regulatory requirements: Compliance with applicable reliability standards and reserve margin requirements
- Economic considerations: Balancing reliability costs against the value customers place on avoiding service interruptions
- Risk tolerance: Establishing appropriate reliability targets that reflect stakeholder preferences for risk
Stakeholder Coordination
Capacity margin planning involves coordination among multiple stakeholders including utilities, grid operators, regulators, policymakers, and customers. Comprehensive approaches call for increased engagement, collaboration and consensus among government energy regulators, elected policymakers, utilities, regional transmission organizations and customers. Effective communication and coordination among these stakeholders is essential for developing and implementing appropriate capacity margin policies.
The traditional definition of resource adequacy includes two parts: development of a reliability target and application of a method to determine whether a given system meets the target. Both components require stakeholder input and agreement to ensure that capacity margin policies reflect community values and priorities.
Monitoring and Reporting Requirements
Regular monitoring and reporting of capacity margins provides transparency and enables early identification of potential reliability concerns. Each fall NERC issues an annual Long-Term Reliability Assessment that presents a ten-year outlook addressing issues related to the reliability of the bulk power system, and NERC also issues Summer and Winter Short-Term Reliability Assessments in May and October, respectively, that present estimates for the upcoming peak demand season.
These regular assessments provide valuable information to policymakers, regulators, and market participants about the adequacy of generation resources and identify regions where capacity margins may be insufficient. The assessments also track trends over time, helping to identify emerging reliability challenges before they become critical.
Utilities and grid operators typically conduct their own internal capacity margin assessments more frequently, often on a monthly or quarterly basis, to ensure they maintain adequate resources to meet reliability requirements. These internal assessments inform decisions about resource procurement, maintenance scheduling, and operational planning.
Conclusion
Calculating capacity margins remains a fundamental practice for ensuring power system reliability in an increasingly complex and dynamic electricity sector. While the basic concept of maintaining generation capacity in excess of peak demand is straightforward, the methods for determining appropriate margin levels have evolved significantly to address new challenges posed by renewable energy integration, changing load patterns, and evolving customer expectations.
Effective capacity margin planning requires sophisticated analytical tools, comprehensive data, and careful consideration of multiple factors including demand uncertainty, generation outages, renewable energy variability, transmission constraints, and economic considerations. As power systems continue to evolve, capacity margin calculation methodologies must also advance, incorporating dynamic approaches, advanced analytics, and real-time data to optimize the balance between reliability and cost-effectiveness.
The future of capacity margin planning will likely involve more adaptive and responsive approaches that can adjust to rapidly changing system conditions while maintaining the high levels of reliability that customers expect. By continuing to refine capacity margin calculation methods and implementing comprehensive planning strategies, power system operators can ensure reliable electricity service even as the grid undergoes fundamental transformation.
For additional information on power system reliability and capacity planning, visit the North American Electric Reliability Corporation, the U.S. Energy Information Administration, the Federal Energy Regulatory Commission, the National Renewable Energy Laboratory, and the Electric Power Research Institute.