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Contract No. DE-AC36-08GO28308

Grid Integration and the 

Carrying Capacity of the U.S. 

Grid to Incorporate Variable 

Renewable Energy

Jaquelin Cochran, Paul Denholm, 

Bethany Speer, and Mackay Miller

National Renewable Energy Laboratory

Technical Report

NREL/TP-6A20-62607

April 2015
NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, CO 80401

303-275-3000 • www.nrel.gov

Grid Integration and the 

Carrying Capacity of the U.S. 

Grid to Incorporate Variable 

Renewable Energy 

Jaquelin Cochran, Paul Denholm, 

Bethany Speer, and Mackay Miller

National Renewable Energy Laboratory

Prepared under Task Nos. EPSA.0103, EPSA.X422

Technical Report

NREL/TP-6A20-62607

April 2015
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iii

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Abstract

In the United States and elsewhere, renewable energy (RE) generation supplies an increasingly large 

percentage of annual demand, including nine U.S. states where wind comprised over 10% of in-state 

generation in 2013. This white paper summarizes the challenges to integrating increasing amounts of 

variable RE, identifies emerging practices in power system planning and operation that can facilitate grid 

integration, and proposes a unifying concept—economic carrying capacity—that can provide a 

framework for evaluating actions to accommodate higher penetrations of RE.

There is growing recognition that while technical challenges to variable RE integration are real, they can 

generally be addressed via a variety of solutions that vary in implementation cost. As a result, limits to 

RE penetration are primarily economic, driven by factors that include transmission and the flexibility of 

the power grid to balance supply and demand. This limit can be expressed as economic carrying capacity, 

or the point at which variable RE is no longer economically competitive or desirable to the system or 

society.

Power systems already have some degree of operational flexibility, an ability to respond to change in 

demand and supply, as they must accommodate variable and uncertain load. Power system operators have 

thus been able to accommodate increased variable RE largely without substantial new investment in 

system flexibility, such as new storage, demand response, or generation dedicated to addressing RE 

variability and uncertainty. To achieve higher penetration levels, multiple grid integration studies in the 

United States have evaluated scenarios where an economic carrying capacity of at least 30% is achieved 

via transmission expansion and largely understood changes to system operations. Studies have also 

demonstrated that carrying capacity is not fixed and can be improved through technical and institutional 

changes. This creates the possibility to achieve even higher penetration levels through strategic 

investments in both demand- and supply-side sources of flexibility.
iv

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Acknowledgments

The authors are greatly indebted to the many reviewers of this analysis, including Carla Frisch, Stephen 

Capanna, Cynthia Wilson, Philip Overholt, Judi Greenwald, Erin Boyd, Eric Hsieh, Alex Breckel, Lara 

Pierpoint, Carl Pechman, Caitlin Callaghan, David Ortiz, Eric Rollison, and John Agan (U.S. Department 

of Energy), J. Charlie Smith (Utility Variable Integration Group), Aidan Tuohy (EPRI), Mark Ahlstrom 

(WindLogics), Drake Bartlett (Xcel Energy), Andrew Mills (Lawrence Berkeley National Laboratory), 

Karin Haas, Robin Newmark, David Mooney, Gian Porro, Jeffrey Logan, Ben Kroposki, Michael 

Milligan, Aaron Bloom, Dan Steinberg, Trieu Mai, Ted James, Jessica Katz, and Marissa Hummon 

(National Renewable Energy Laboratory).
v 

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Table of Contents

1 Introduction ........................................................................................................................................... 1

2 Grid Integration of Variable RE: Overview......................................................................................... 2

2.1 Impacts of Variable RE on System Operations and Planning....................................................... 2

2.2 Emerging Best Practices................................................................................................................ 4

3 The Carrying Capacity of the U.S. Power Grid to Incorporate Variable RE.................................... 9

3.1 The Concept of Economic Carrying Capacity .............................................................................. 9

3.2 Estimating the Economic Carrying Capacity of the U.S. Power Grid .......................................... 9

4 Conclusion .......................................................................................................................................... 14

References................................................................................................................................................. 15

Appendix: Renewable Electricity Market Status.................................................................................... 20

United States in the Context of the Global Renewable Electricity Market .......................................... 21

Manufacturing ...................................................................................................................................... 22

Technology Cost and Performance Characterizations.......................................................................... 22

Projections of Future Renewable Electricity Deployment ................................................................... 23

Figures and Tables

Figure 1. Greater need for operational flexibility ......................................................................................... 3

Figure 2. Types and relative economics of integration options.................................................................... 7

Figure 3. Simulated impacts of obtaining 33% energy from wind and solar.............................................. 10

Table 1. Characteristics of Power from Variable RE Sources, Potential Grid Integration Challenges, 

and Mitigation Options ............................................................................................................ 2

Table 2. Estimates of Transmission-related Curtailment of Variable RE in Recent U.S. Integration 

Studies.................................................................................................................................... 12

Table 3. Evaluations of Transmission Costs in Recent U.S. Integration Studies ....................................... 13

Table A-1. 2013 U.S. Installed Capacity and Generation for Renewable Energy Technologies ............... 20

Table A-2. 2013 Global Installed Capacity and Generation for Renewable Energy Technologies............ 21

Table A-3. Technology Characterizations.................................................................................................. 23

Table A-4. Projected Global RE Capacity by Technology, from 2013 – 2017 (GW)................................ 24
1 

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1 Introduction

In the United States and elsewhere, renewable energy (RE) generation supplies an increasingly large 

percentage of annual demand, including nine U.S. states where wind comprised over 10% of in-state 

generation in 2013 (Wiser and Bolinger 2014). Variable REprimarily wind and solaraccount for the 

majority of newly-built RE capacity in the United States (Esterly 2014). 

Compared to conventional thermal generation, wind and solar are marked by five characteristics of 

particular concern to power grid operators: variability, uncertainty, location-specificity, non-synchronous 

generation, and low capacity factor. Due to these characteristics and the rapid increase in deployment, the 

operation and planning of power systems are evolving, and grid integration of renewable energy has 

become a focal point of national and international research and collaboration.

This white paper summarizes the challenges to integrating variable RE, identifies emerging practices in 

power system planning and operation that can facilitate grid integration, and proposes a unifying 

concept—economic carrying capacity—that can provide a framework for evaluating actions to 

accommodate higher penetrations of RE. The appendix provides an overview of the U.S. renewable 

electricity market.

The concept of carrying capacity has evolved over time. While historically there has been a perception 

that physical or technical issues will fundamentally limit the penetration of variable RE, recently 

conducted grid integration studies and operational experience have created a more nuanced 

understanding. Specifically, there is growing recognition that while technical challenges to variable RE 

integration are real, they can generally be addressed with a variety of solutions, each with an associated 

implementation cost. As a result, the limit to RE penetration is primarily economic, driven by factors that 

include transmission availability and operational flexibility, which is the ability of the power grid to 

balance supply and demand. This limit can be expressed as economic carrying capacity, or the level of 

variable RE generation at which that generation is no longer economically competitive or desirable to the 

system or society.1 The economic carrying capacity of any power system is highly region-specific and can 

change over time, for example, as inflexible generators retire, demand and renewable supply forecasting 

improves, market designs evolve, or as enabling technologies become more affordable (e.g., storage). 

The recognition that technical and institutional changes could improve economic carrying capacity

provides a framework for developing strategic investments and updating grid management practices

across the U.S. energy landscape. System-specific research and analysis to develop a more precise 

understanding of grid integration dynamics could enable greater grid integration at lower costs, and would 

enhance U.S. international leadership in RE deployment.

 

1 Economic carrying capacity is a distinct concept from effective load carrying capacity, which is the amount by 

which a system's loads can increase when a generator is added to the system while maintaining the same reliability.
2 

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2 Grid Integration of Variable RE: Overview

2.1 Impacts of Variable RE on System Operations and Planning

Renewable energy generated from variable sources such as wind and solar offers a low-carbon source of 

electricity. At high penetration levels, the challenges of variable RE must be considered in the planning 

and operation of the power grid. Variable renewable electricity possesses five characteristics of particular 

concern to power grid operators, as described in Table 1. 

Table 1. Characteristics of Power from Variable RE Sources, Potential Grid Integration Challenges, 

and Mitigation Options

Wind and Solar Characteristics

Potential Grid 

Integration 

Challenges

Mitigation Options

Variability

Generator output can 

vary as underlying 

resource fluctuates.

Balancing generation with 

electricity load requires 

more flexibility.

In many power systems, sufficient flexibility exists to 

integrate additional variability, but this flexibility may not be 

fully accessible without changes to power system 

operations or other institutional factors (e.g., increased 

ramping of generation and improved coordination across 

markets and balancing areas) (Lew et al. 2013).

Uncertainty

Generation cannot be 

predicted with perfect 

accuracy (day-ahead, 

day of).

System operators could 

need additional reserves 

and/or an improved ability 

to dispatch generation.

Integration of advanced renewable supply forecasting into 

dispatch and market operations has reduced uncertainties, 

improved scheduling of other resources to reduce reserves 

and fuel consumption, and enabled variable RE to 

participate as dispatchable resources (IEA 2014; Lew et al. 

2011). Examples: Xcel Energy, U.S. RTOs/ISOs (Porter et 

al. 2012).2

Locationspecificity

Generation is more 

economical where 

highest quality 

resources are 

available.

More transmission and 

more advanced planning 

could be needed.

Competitive Renewable Energy Zones (CREZ) in Texas 

are an example of an approach to quickly develop 

generation and transmission in coordination (18.5 

GW3,600 milescompleted nine years after CREZ 

legislation was signed), to access wind resources in 

remote parts of the state.

Nonsynchronous 

generation

Generators provide 

voltage support and 

frequency control in a 

different manner than 

traditional resources. 

Voltage and frequency 

stability from variable RE 

generators or additional 

equipment comes at 

added capital and/or 

opportunity costs.

Grid code3

 requirements are evolving in response to 

technological advances and anticipation of high RE 

penetration levels. For example, ERCOT, which is a small 

interconnection and more vulnerable to frequency 

excursions, now requires wind generators to provide 

inertial response, which helps keep a system stable in the 

initial moments after a disturbance (Bird, Cochran, and 

Wang 2014).

Low capacity 

factor

Availability of the 

underlying energy 

resource limits the runtime of the plant.

Existing conventional 

generators could be 

needed to meet demand, 

but run less than originally 

anticipated, affecting cost 

recovery.

Capacity payments or markets, potentially tied to flexible 

performance, could ensure sufficient cost recovery. The 

potential for stranded assets is not unique to variable RE, 

and can occur whenever generation with lower marginal 

costs is added to the system. For example, low natural gas 

prices have reduced the market competitiveness of nuclear 

plants, contributing to recent retirements (Wernau and 

Richards 2014).

 

2 The U.S. regional transmission organizations (RTOs) and independent system operators (ISOs) are California ISO 

(CAISO), Electric Reliability Council of Texas (ERCOT), ISO New England (ISO-NE), Midcontinent ISO (MISO), 

NYISO (formally New York ISO), PJM Interconnection (PJM), and Southwest Power Pool. 3 Grid codes establish the performance requirements of a grid-connected generator including response during a 

system disturbance. 
3 

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Figure 1 demonstrates how variability can impact power system operations, and introduces the concept of 

net load—i.e., the demand that must be met by other generation sources if all wind and solar power is 

consumed (Milligan 2011). All power systems have some level of variability, even without the presence 

of wind and solar generators, and require flexibility in system operations and generation to rapidly 

manage changing system conditions.

Wind and solar generation can create 

the need for more flexibility. This figure 

illustrates how wind generation can lead 

to steeper ramps, deeper turn downs, 

and shorter peaks in system operations. 

Ramps - The rate of increase or 

decrease in dispatchable generation to 

follow changes in net load. Ramps can 

be steep if wind generation is 

decreasing at the same time that 

demand rises. 

Turn-downs - Operation of dispatchable 

generators at low levels. High wind 

output during periods of low demand 

creates a need for generators that can 

turn down output to low levels but 

remain available to rise again quickly. 

Shorter peaks – Periods during which 

generation is supplied at a higher level. 

Peaks are shorter in duration, resulting 

in fewer operating hours for 

conventional plants, affecting cost 

recovery and long-term security of 

supply.

Figure 1. Greater need for operational flexibility

Source: Cochran et al. 20144

Flexibility can reduce the need to curtail (decrease the output of) solar and wind output; improve investor 

confidence in RE and revenue streams; decrease the risk of negative market pricing (which results when 

conventional generators cannot sufficiently reduce output during times of oversupply); and reduce 

environmental impacts by increasing system efficiency and maximizing the utilization of clean RE. See

Text Box 1 for example indicators of a lack of flexibility, or inflexibility, on a system. 

 

4 Another example is the CAISO “duck curve” showing ramping requirements of up to 13,000 MW in three hours in 

the period around sunset (CAISO 2013). See 

http://www.caiso.com/Documents/FlexibleResourcesHelpRenewables_FastFacts.pdf. 
4 

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Text Box 1. System Inflexibility Indicators

Examples of inflexibility are more commonly documented than flexibility. 

Indicators of inflexibility include: 

Difficulty balancing demand and supply, resulting in violations to Area Control Error (i.e., 

deviations from schedule of the area power balance), frequency excursions, or dropped load.

Significant renewable energy curtailments, occurring when available generation is routinely 

not fully utilized on the system due to transmission constraints or when output of other generation 

cannot be reduced. 

In addition, in competitive wholesale markets: 

Negative market prices, which can signal several types of inflexibility, including conventional 

plants that cannot reduce output, load that cannot absorb excess supply, surplus RE generation, 

and limited transmission capacity to balance supply and demand across broader geographic 

areas. Negative prices can occur in systems without RE but may be exacerbated as RE

penetration increases.

Electricity price volatility (the variation of price over time), which can reflect limited transmission 

capacity; limited availability of ramping, fast response, or peaking supplies; or limited ability for 

load to reduce demand. 

Source: Cochran et al. 2014

2.2 Emerging Best Practices

Despite these grid integration challenges, many countries have integrated high levels of variable RE as a 

share of annual electricity generated.5 In these systems the impact of variable RE has not compromised 

reliability because policy makers, planners, and system operators have made changes to regulations, 

market designs, and system operations to address the grid integration challenges. While there is no “onesize-fits-all” approach, these experiences have informed a set of best practices that address the grid 

integration challenges. These best practices include improving integrated planning methodologies and 

increasing system flexibility. 

2.2.1 Improve Integrated Planning Methodologies 

Coordinated and integrated planning allows decision makers to anticipate how variable RE might impact 

the power grid and its operations, and evaluate options that could minimize costs across a system. 

Planning that is segregated by type (e.g., generation, transmission, and system performance each 

evaluated separately) or restricted to a small geographic scale adversely impacts the ability to employ best 

practices to accommodate RE, including diversifying RE locations and enlarging balancing authorities 

(Cochran et al. 2012). For example, the addition of new physical generation capacity can be minimized if 

considered in coordination with improvements to system operations that increase access to existing 

flexibility. Also, co-optimized development of transmission and RE generation can reduce overall costs of 

transmission, and facilitate lower-cost access to high-quality RE resources (see, for example, Competitive 

RE Zones in Texas and Irish “Gate Process” or group-processing approach in Miller et al. 2013). 

Regarding geographic scale, FERC Order 1000 compels U.S. states to participate in regional transmission 

planning (FERC 2014). 

 

5 Example power systems with high variable RE penetration levels include Denmark (39% from wind in 2014) (The 

Local 2015), Spain (20.9% from wind), and Italy (7.8% from solar PV) (REN21 2014). 
5 

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2.2.2 Increase System Flexibility Across All Elements of the Power System

Although simple-cycle natural gas plants, which can be designed to change output quickly, are frequently 

viewed as a natural pairing with variable RE, other sources of flexibility may already exist across the 

power system: operations, demand, energy storage, generation, and transmission. 

Flexible system operations and markets: Two central components to system operation are unit 

commitment and dispatch. Unit commitment is the scheduling of generators to be available, typically dayahead. Dispatch is the method by which system operators choose among available generators to deliver 

energy at least operating cost. The system operator seeks to access the physical system (e.g., generators) 

in the most flexible manner. But access to the physical system is mediated by the institutional framework, 

namely the rules for how decisions are made in scheduling and dispatch. Changes to system operation 

practices and markets can allow access to significant existing flexibility, often at lower economic costs 

than options requiring new sources of physical flexibility (IEA 2014; Cochran et al. 2014). For example, 

establishing short-term market products for flexible generation (e.g., CAISO and MISO’s proposed 

ramping products) can help ensure that existing physical flexibility is available when needed (AbdulRahman et al. 2012). 

As another example, incorporating renewable supply forecasting into unit commitment and dispatch can 

improve the scheduling of other generators to reduce reserves, fuel consumption, and operating and 

maintenance costs (Lew et al. 2011). Regulations increasingly require variable RE generators to forecast 

power output and the potential for forced outages (FERC 2012). Integrating these data into market 

operations can help variable RE plants participate efficiently in electricity markets (Reisz and Milligan

2014). 

Additional examples of operational changes that improve access to flexibility include a higher dispatch 

frequency (the timeframe over which a generator must follow a specified output level) (Milligan et al.

2011) and use of smart network technologies and advanced network management practices that minimize 

bottlenecks and optimize transmission usage (Miller et al. 2013). 

Flexible demand and energy storage: Demand responseincreasing the responsiveness of electricity 

demand to operator controls and/or price signalsimproves flexibility by enabling or encouraging 

consumers, particularly in the industrial and commercial sectors, to vary their demand in response to 

system events or economic conditions (Hummon and Kiliccote 2013). Demand response mechanisms 

include automated load control by the system operator, real-time pricing, and time-of-use tariffs. A 

typical response time for automated demand response is seconds to minutes (Watson et al. 2012). 

Although participation by individual customers might be limited in duration (e.g., a few hours a year), 

aggregating participation over a broad customer class can provide a reliable source of system flexibility. 

For example, ERCOT has broadened its demand response program, which now includes loads as 

resources that bid in both energy and ancillary services markets, loads that are willingly interrupted 

during emergencies and loads that adjust output in response to price signals from their retail electricity 

providers. Demand response can be inexpensive but requires new regulations related to response time, 

minimum magnitude, reliability, and verifiability of demand-side resources (Cappers et al. 2012). 

Additionally, energy storage technologies—including pumped hydroelectric, compressed air, thermal 

storage, and batteriesalso hold value at high penetrations of variable RE (Denholm et al. 2013). Storage 

can absorb energy when its value is low, reduce RE curtailment, and provide additional operational 

flexibility through its fast response time. Many storage technologies (e.g., batteries, flywheels, 

supercapacitors) have fast response rates (seconds to minutes) available over a short timeframe; other 

storage technologies, such as pumped hydro energy storage and compressed air energy storage, are better 

suited to offering flexibility in the time frame of hours to days (Miller et al. 2013). Several thermal 
6 

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storage technologies also can provide flexibility, such as end-use thermal storage, which can shift demand 

for heating and cooling, or concentrating solar power, which can use highly efficient thermal storage and 

become a dispatchable resource with high capacity value (Denholm et al. 2014). Many available storage 

technologies have a higher capital cost relative to other options for flexibility currently available. In most 

power systems, further technology improvements will be needed for storage to be cost competitive with 

other flexibility options. 

Flexible conventional generation: Conventional power plants and dispatchable, non-variable RE 

generators such as biomass or geothermal plants provide flexibility if they have the ability to rapidly 

ramp-up and ramp-down output to follow net load; quickly shut-down and start-up; and operate 

efficiently at a lower minimum level during high RE output periods (IEA 2014). New and retrofitted 

large-scale power plants, as well as smaller-scale distributed generation (e.g., micro combined heat and 

power units), can supply flexible generation (Holttinen 2013). Examples of flexible thermal generation 

include aeroderivative turbines and reciprocating engines, as well as older power plants that are operated 

flexibly, such as coal plants in Germany. 

Flexibility comes with an incremental cost for conventional thermal generators that were not designed to 

cycle frequently, such as older coal plants.

6

 Cycling damages these plants and reduces their life 

expectancy, but associated costs can be reduced with strategic modifications, proactive inspections and 

training programs, and other types of maintenance (Cochran, Lew, and Kumar 2013). The second phase 

of the Western Wind and Solar Integration Study (WWSIS-2) directly addressed the impacts of high wind 

and solar penetration levels on thermal plant cycling. For a 33% RE scenario, WWSIS-2 concluded that 

from a system perspective, annual increases in cycling costs ($35 million to $157 million) are small 

compared to overall reductions in operational costs ($7 billion) due to avoided fuel costs (Lew et al. 

2013).

7 Although there have been questions about the potential for emissions to increase in systems with 

high RE due to plant cycling, WWSIS-2 also concluded that 98-99% of the benefit of reductions in 

emissions due to generating electricity from wind and solar is maintained; increases in emissions due to 

added cycling reduces the emissions savings by 1-2% (Lew et al. 2013). Cycling-related costs may pose 

additional challenges to revenue adequacy in competitive wholesale markets.

Flexible variable RE: Technology advancements now enable wind plants to provide the full spectrum of 

balancing services (synthetic inertial control, primary frequency control, and automatic generation 

control), an increasingly common requirement for systems with high levels of RE generation (Ela et al. 

2014). For example, ERCOT requires wind turbines to provide an autonomous response to changes in 

power grid frequency, and the Colorado utility Xcel Energy requires many turbines to be on automatic 

generation control, which allows the computerized control system to directly control wind generation 

output (Bird, Cochran, and Wang 2014). PV plants are also starting to implement similar grid 

requirements (Morjaria et al. 2014). 

Modern turbines and solar plants can also provide voltage support. Distributed solar PV systems can be 

configured with smart inverters that monitor local grid conditions and autonomously provide system grid 

services. The California Public Utility Commission, for example, is updating interconnection 

requirements for distributed PV to include smart inverters that provide local voltage support, meet ramp 

rate requirements after an outage, and ride through frequency and voltage events (CPUC 2014). 

 

6 Cycling includes the range of operations in which a plant’s output changes, including starting up and shutting 

down, ramping up and down, and operating at part-load (less than full output). 7 Costs are also relatively small from a single generator perspectivetypical operating costs, including fuel, are 

$20-$40/MWh, whereas increased operations and maintenance costs due to RE-induced cycling were $0.5-

$1.3/MWh. Nevertheless, these costs can be significant from a generator perspective if profit margins are tight.
7 

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Revising grid codes early allows hardware and procurement agreements to be designed in advance of high 

variable RE penetration levels and reduces the financial burden associated with implementing such 

requirements retroactively. Germany’s requirements for installed solar photovoltaic (PV) to provide grid 

services were applied retroactively, at considerable cost (Cochran et al. 2012). 

Interconnected transmission networks: Transmission capacity is often considered an integral part of 

system flexibility as it offers an alternative to using variable RE generation only locally. Transmission

capacity allows variable RE to instead be transmitted to other regions where the energy can be used. In 

addition, improving connections with neighboring transmission networks, including extending existing 

lines, provides the power system greater access to a range of balancing resources (Cochran et al. 2012; 

IEA 2014; Porter et al. 2012). The aggregation of all generation assets through such interconnection both

improves flexibility and reduces net variability across the power system. Weather patterns become less 

correlated over larger areas, smoothing the output of wind and solar plants (Bird, Milligan, and Lew 

2013). Enlarging balancing areas also lowers the net variability and uncertainty of load and variable RE. 

2.2.3 Evaluate Integration Options

The appropriate combination of flexibility options for a given power system will be specific to that 

system and depends on the relative economics of the options available (see Figure 2), among other 

factors. Through a grid integration study, planners can evaluate relative costs and benefits for different 

options (e.g., improvements to forecasting, financial signals to incentivize fast response) by 

systematically testing and evaluating various combinations of new operational practices and other sources 

of flexibility. 
8 

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Figure 2. Types and relative economics of integration options 

Note: Relative costs are illustrative, as actual costs are system-dependent. In general, changing system operation and 

market designs are low capital-cost options, but may require significant changes to the institutional context (Cochran 

et al. 2014). 

a. There is a tradeoff between costs of flexibility and benefits of reduced (or no) curtailment, hence a certain level of 

curtailment may be a sign that the system has an economically optimal amount of flexibility.

b. Joint system operation typically involves a level of reserve sharing and dispatch co-optimization but stops short of 

joint market operation or a formal system merger.

c. Wind power can increase the liquidity of ancillary services and provide generation-side flexibility. Curtailed 

energy is also used to provide frequency response in many systems, for example Xcel Energy, EirGrid, 

Energinet.dk.

By adopting many of these integration best practices, utilities in many regions have successfully 

incorporated large amounts of variable RE. Annual energy penetrations of wind in the United States have 

exceeded 20% of total generation in two states (South Dakota and Iowa), while instantaneous penetration 

in the Xcel service territory in Colorado has exceeded 60% several times without compromising 

reliability (Xcel Energy 2015). 
9 

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3 The Carrying Capacity of the U.S. Power Grid to 

Incorporate Variable RE

3.1 The Concept of Economic Carrying Capacity

Given the challenges and required changes to power system operations to incorporate high levels of 

variable RE, a natural follow-up question is “How much variable RE can an existing power system 

handle?” We use this question to frame the concept of carrying capacity of a power grid to incorporate 

variable RE. No single metric can measure carrying capacity, but it can be approximated both technically 

and economically.

Technically, carrying capacity is the point at which the addition of variable RE produces a system that 

cannot reliably be operated due to technical challenges or, more simply stated, the point at which the 

addition of variable RE breaks the grid. An example would be the point at which there is insufficient 

inertia in the system to recover from normal faults (Ela et al. 2014).

8 However, regional grid integration 

studies conducted to date have indicated that there is nearly always a technological fix that can be adopted

at some cost (e.g., a change in operation or piece of hardware that can be added to the grid). So, simply 

deploying extremely large amounts of transmission and storage (or some other set of technologies), and 

modifying the RE generation to maintain system operational parameters could enable 100% penetration 

of wind and solar. As such, carrying capacity defined exclusively on technical factors is of limited 

practical value. 

Because mitigation strategies that can address challenges to the integration of variable RE have associated 

costs and benefits, we use the term economic carrying capacity to represent the economically desirable

limit associated with adding variable RE to any existing power system. One approach to estimating this 

limit is to determine the penetration of variable RE (fraction of a system’s energy met by variable RE) at 

which the costs outweigh the benefits and additional variable RE is no longer economically desirable. The 

economic carrying capacity could be evaluated for a static grid, such as today’s transmission 

configuration and operational practices, as well as under a variety of potential future conditions to 

consider the incremental costs and benefits associated with increasing the carrying capacity. 

A critical step to effectively employ this framework is to determine the specific types of benefits to 

include in the benefit-to-cost ratio, such as the value of avoided fuel, avoided capacity additions, avoided 

emissions, and reduced fuel price volatility. While suggesting a particular set of benefits is beyond the 

scope of this report, the economic carrying capacity is nonetheless a useful framework with which to 

consider the integration challenges of variable RE and how the evolution of the grid and grid flexibility 

can mitigate these challenges.

3.2 Estimating the Economic Carrying Capacity of the U.S. Power Grid

Economic carrying capacity is an emergent concept, and as such has not been calculated for any specific 

grid. Challenges to calculating the economic carrying capacity include lack of consensus on the 

components of a cost-benefit ratio, and the complexity of valuing many of these cost and benefit 

components. These valuations require detailed grid modeling of both the generation and transmission 

system. Additional benefits such as the value of fuel diversity, local economic benefits, air quality, and 

others may be even more difficult to estimate. 

 

8 These challenges include the lack of real inertia and primary frequency response from inverter-based wind and PV 

systems. Available technologies, including active power controls from wind, can act to mitigate these concerns but 

have yet to be deployed at large scale.
10 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Nevertheless, results from power grid simulations, which simulate the operation of the power grid with 

increased generation from variable RE, offer some indication of economic carrying capacity of future 

power grids. Grid integration studies typically simulate a limited set of variable RE generation scenarios, 

along with grid conditions that exist in the present or may exist in some future year. These studies 

evaluate the operation of individual generators that respond to net system variability as well as the ability 

of the transmission system to accommodate additional power flows that might occur with the introduction 

or increase of variable RE. Many studies have identified a decrease in the value of variable RE as it 

begins to represent a larger percentage of an electric grid, particularly in systems that are unable to 

increase system flexibility (as discussed in Section 2). Decreases in value result from reduced capacity 

value (due to the shift in net load) and reduced energy value (due to merit order effect and/or curtailment 

from insufficient system flexibility or transmission). The value of other generation sources falls as well as 

a function of increases in their penetration, but the drop is typically less pronounced.

9

 

A detailed grid integration study can explore how increasing system flexibility, including adding 

transmission capacity, can maintain value for variable RE as its generation increases. The importance of 

overall system flexibility to respond to variable RE is illustrated in Figure 3, which provides the results of 

a grid simulation of the western United States. This scenario, one of several assessed in WWSIS-2, 

evaluates the impacts of obtaining 33% of the Western Interconnection’s generation from wind and solar. 

This particular illustration is a dispatch stack for a one-week period from the simulation of the high-solar 

scenario (15% contribution from solar PV, 10% solar CSP with storage, and an 8% contribution from 

wind). This week is a particularly challenging one from an integration standpoint, as it features a very low 

net load, typical in the spring, and significant thermal plant cycling. 

Figure 3. Simulated impacts of obtaining 33% energy from wind and solar

Source: Lew et al. 2013

During the first five days of this week, the system, which relies on a large number of coal-fired power 

plants, encounters flexibility constraints due to minimum generation levels of the thermal fleet. Flexibility 

 

9 For example, the capacity value of PV falls significantly as a function of penetration because the normal U.S. peak 

load, which typically occurs in the late afternoon, is met by PV; the new net load is effectively shifted later, to the 

early evening, when PV output has dropped substantially. Because the output of a conventional thermal plant is 

independent of time of day, the plant can maintain high capacity value. For additional discussion of the value of RE 

as a function of penetration, see Mills and Wiser 2012.
11 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

is also potentially limited by the need to run conventional generators to provide reserves to address 

forecast errors and provide active power control. In combination, these factors create a minimum 

percentage of energy that must be provided by conventional generation, which in turn results in

curtailment of solar generation. 

The consequence of curtailment is decreased value of the variable RE generation, as 1 MWh of variable 

RE displaces less than 1 MWh of thermal generation. Alternatively, curtailment can manifest in increased 

generation cost, as the decreased variable RE capacity factor results in an increased levelized cost of 

energy (LCOE) from the plant.10 Because of the impacts of curtailment on RE value, many grid 

integration studies use curtailment as an important metric of system performance. Curtailment can occur 

due to either transmission constraints or limits on other sources of system flexibility. Many grid 

integration studies therefore add transmission or make changes to system flexibility to keep curtailment 

rates under a particular threshold. 

Results from grid integration studies can provide an indication of economic carrying capacity. The key 

difference between a grid integration study and an economic carrying capacity study is that many grid 

integration studies select an RE penetration level (e.g., 30%), and evaluate the actions necessary to 

maintain value for variable RE at the target penetration level. The studies do not directly evaluate the 

cost-benefit threshold of RE penetration for those same actions, which could be higher or lower than the 

targeted RE level, depending on the components of the cost-benefit analysis. Therefore, we use the results 

of the grid integration studies to infer what actions are necessary to achieve an economic carrying 

capacity of a target RE penetration level.

Table 2 summarizes curtailment estimates for a number of recent integration studies that have evaluated 

variable RE generation at levels of at least 30% of total system generation: WWSIS-2, a PJM wind 

integration study, a 2011 study on wind integration in the Eastern Interconnect (EWITS), and two recent 

studies of California.

11 These studies use different methods and models, so their results are not directly 

comparable; however, they share several common themes. First, all show the importance of additional 

transmission capacity to integrating increasing amounts of variable RE and imply that the current carrying 

capacity of the grid is likely limited by existing transmission capacity, especially in scenarios of 

significant wind development.

 

10 The LCOE from a wind or solar plant is equal to the annual carrying cost divided by annual energy sold. As the 

curtailment rate increases, the annual energy sold decreases, which results in a relative cost equal to the base cost 

divided by (1 – curtailment rate). 11 These studies primarily explore the impact of variable RE generation on the bulk transmission system, and do not 

consider the impacts on or the carrying capacity of the distribution network. 
12 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table 2. Estimates of Transmission-related Curtailment of Variable RE in Recent U.S. Integration 

Studies

Study RE 

Penetration

Curtailment 

PJM

(GE Energy 2014) Up to 30% 1.5-3% with new transmission. 

Western United 

States (WWSIS-2)

(Lew et al. 2013)

33% 

10% without new transmission, 1.5% with new transmission 

(High Wind Scenario). Curtailment with transmission in other 

scenarios as high as 5%. 

California 

(CAISO 2011) 33% <1%, Relies in part on flexibility from surrounding regions. 

California

(E3 2014) 33-50%

0.2% in 33% scenario, 1.8% in 40% scenario, and 8.9% in 50% 

high solar scenario. Also evaluates marginal curtailment rates 

and mitigation options. Mitigation options reduce curtailment 

rates by 50% or more.

Eastern United 

States (EWITS)

(Corbus et al. 2011)

Up to 30%

19%-48% without new transmission depending on scenario. 

With transmission 1%-7% in 20% scenario and 3-10% in 30% 

scenario. 

These studies also suggest that relatively low curtailment rates are possible with the addition of sufficient 

transmission, with both WWSIS-2 and EWITS comparing curtailment with and without additional 

transmission. Table 3 summarizes the transmission addition in three grid integration studies.12

Transmission is needed not only to increase system flexibility, but to enable access to new sources of 

wind generation. Evaluating the transmission system is complicated because of the need to consider 

multiple aspects of transmission availability. This includes direct transmission access (the ability of wind 

to access the transmission network) and the ability of the system to transmit energy over large areas to 

take advantage of the spatial diversity of wind and solar resources, loads, and generator flexibility. 

The specific approach used to evaluate additional transmission needs varies by study. For example in 

WWSIS-2, the study incrementally adds transmission across zonal interfaces where the price of energy 

typically exceeds a threshold value of $10/MWh, based on comparing the net benefit of additional 

transmission to the estimated transmission costs of about $1,600/MW-mile for 250 miles of new 

transmission with an 11% fixed-charge rate.13 Similarly, the EWITS study (Corbus et al. 2011) estimates 

new transmission capacity requirements by comparing estimated costs to benefits of congestion relief. 

Table 3 includes an estimated cost of this new transmission, with a uniform comparison provided in the 

last column in terms of cost per MWh of variable RE (using a fixed charge rate of 11% in all cases). 

 

12 These studies do not typically differentiate between transmission capacity developed for wind and for solar. Also, 

many challenges associated with new transmission, including permitting, siting, and cross-jurisdictional 

coordination, are not considered in grid integration studies.

13 The fixed charge rate is defined as the amount of revenue per dollar of investment that must be collected annually 

from customers to pay the carrying charges on that investment.
13 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Table 3. Evaluations of Transmission Costs in Recent U.S. Integration Studies

Study RE 

Penetration 

Variable 

RE Added 

(GW)

Total 

Transmission 

Added 

Total 

Additional 

Transmission 

Costs ($B)

Transmission 

Cost per unit 

RE energy 

($/MWh)a

PJM

(GE Energy 

2014)

30% 102-114 1,000- 3,000 miles 5.0-13.7 2.1-5.9

WWSIS-2 

(Lew et al. 

2013)

33% 90-107

10,500-16,500 MW 

zonal transfer 

capacity

3.6-5.3 1.5-2.2

EWITS

(Corbus et 

al. 2011)

30% 338 23,000 miles 92.5 9.1

a Fixed charge rate of 11% applied in all scenarios

In addition to new transmission, these studies also assume several other changes to the grid, primarily in 

how the system is operated in high variable RE scenarios. For example, a significant assumption in the 

WWSIS-2 study is larger balancing areas than currently adopted, effectively allowing greater sharing of 

resources via frictionless exchange of energy across most of the Western Interconnection. This 

assumption enables substantial benefits from spatial diversity of both generation and load, including 

reduced variability, but would require new markets or policies to actually implement. In contrast the 

Eastern Interconnection contains several large ISOs/RTOs, which already enable efficient exchange of 

energy over large geographic boundaries—this market structure has been instrumental in enabling high 

wind penetration levels in several Midwestern states (FERC 2011). 

Overall, studies to date imply that an economic carrying capacity of 30% in much of the United States

will require largely understood changes to operational practices as well as transmission capacity 

expansion. However, further analysis is needed to establish more detailed regional carrying capacities 

across the United States, particularly at penetrations above 30%. This is particularly important as 

preliminary studies indicate that marginal or incremental curtailment rates can increase dramatically at 

penetrations beyond 30% without more substantial measures to increase grid flexibility. Economic 

carrying capacity is not a fixed value and can (and will) change as the power grid evolves. A thorough 

examination of regional carrying costs requires detailed analysis that considers the variable RE mix, 

power grid configuration, transmission expansion availability and costs, fuel cost, and operational 

practices, among other factors. 
14 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

4 Conclusion

Operators of power systems throughout the United States have already integrated significant levels of 

variable RE generation while maintaining reliability. All power systems have an inherent level of 

flexibility, designed to accommodate variable and uncertain load and contingencies related to network 

and conventional power plant outages. Because of this existing flexibility, power system operators have 

accommodated increased variable RE largely without substantial new investment in system flexibility, 

such as new storage, demand response, or generation dedicated to addressing RE variability and 

uncertainty. 

To achieve higher penetration levels, multiple grid integration studies in the United States have evaluated 

scenarios where an economic carrying capacity of at least 30% is achieved by implementing largely wellunderstood changes to system operations along with transmission capacity expansion. Specific 

transmission requirements have depended on the type of added generation (e.g., wind, utility-scale or 

distributed PV). 

Still higher levels of, variable RE generation is technically feasible but could test the economic carrying 

capacity of the U.S. power grid. High RE penetrations could significantly change dispatch requirements 

and use of conventional generators, requiring a reassessment of market mechanisms to ensure revenue 

sufficiency. System-specific analyses can be conducted to identify the combination of technologies, grid 

infrastructure, and market and operational changes that will increase the carrying capacity of the power 

grid to exceed 30% of variable RE. 

In the United States and internationally, grid integration issues—and potential solutions—are coming into 

focus through real-world experience and sophisticated research and analysis. The concept of economic 

carrying capacity provides an organizing framework in which meaningful grid integration analysis can be 

designed. Equally importantly, this framework provides a basis for tailoring such analysis to regionspecific considerations. Understanding that carrying capacity is not fixed, but could be improved through 

technical and institutional changes, creates the opportunity to enable levels of RE generation beyond 30% 

through strategic investments in both demand- and supply-side sources of flexibility.
15 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

References

Abdul-Rahman, K.; Alarian, H.; Rothleder, M.; Ristanovic, P.; Vesovic, B.; Lu, B. (2012). “Enhanced 

System Reliability Using Flexible Ramp Constraint in CAISO Market.” IEEE Power and Energy Society 

General Meeting Proceedings; July 22–26, 2012, San Diego, California. New York, NY: IEEE.

Bird, L.; Cochran, J.; Wang, X. (2014). Wind and Solar Energy Curtailment: Experience and Practices in 

the United States. TP-6A20-60983. Golden, CO: National Renewable Energy Laboratory. Accessed 

August 2014: http://www.nrel.gov/docs/fy14osti/60983.pdf

Bird, L.; Milligan, M.; Lew, D. (2013). Integrating Variable Renewable Energy: Challenges and 

Solutions. NREL/TP-6A20-60451. Golden, CO: National Renewable Energy Laboratory. Accessed 

August 2014: http://www.nrel.gov/docs/fy13osti/60451.pdf

Blair, N. (forthcoming). Annual Technology Baseline and Standard Scenarios. Golden, CO: National 

Renewable Energy Laboratory.

Bloomberg New Energy Finance (BNEF). (2013). 2030 Market Outlook: Focus on Americas. New York, 

NY: Bloomberg New Energy Finance. Accessed December 2014: 

http://bnef.folioshack.com/document/v71ve0nkrs8e0/106y4o

California ISO (CAISO). (2011). “Summary of Preliminary Results of 33% Renewable Integration 

Study—2010 CPUC LTPP Docket No. R.10-05-006.” Folsom, CA: California ISO. Accessed August 

2014: http://www.cpuc.ca.gov/NR/rdonlyres/E2FBD08E-727B-4E84-BD98-

7561A5D45743/0/LTPP_33pct_initial_results_042911_final.pdf. 

California ISO (CAISO). (2013). “Fast Facts: What the duck curve tells us about managing a green grid.” 

Folsom, CA: California ISO. Accessed December 2014: 

http://www.caiso.com/Documents/FlexibleResourcesHelpRenewables_FastFacts.pdf

California Public Utilities Commission (CPUC). (2014). “Interim Decision Adopting Revisions to 

Electric Tariff Rule 21 for Pacific Gas and Electric Company, Southern California Edison Company, and 

San Diego Gas & Electric Company to Require “Smart” Inverters.” Decision 14-12-035. San Francisco, 

CA: California Public Utilities Commission. Accessed February 2015: 

http://docs.cpuc.ca.gov/PublishedDocs/Published/G000/M143/K827/143827879.PDF

Cappers, P.; Mills, A.; Goldman, C.; Wiser, R.; Eto, J.H. (2012). "An assessment of the role mass market 

demand response could play in contributing to the management of variable generation integration issues." 

Energy Policy (48): pp. 420-429.

Cochran, J.; Bird, L.; Heeter, J.; Arent, D.J. (2012). Integrating Variable Renewable Energy in Electric 

Power Markets: Best Practices from International Experience. TP-6A00-53732. Golden, CO: National 

Renewable Energy Laboratory. Accessed August 2014: http://www.nrel.gov/docs/fy12osti/53732.pdf

Cochran, J.; Lew, D.; Kumar, N. (2013). Flexible Coal: Evolution from Baseload to Peaking Plant. 

NREL/BR-6A20-60575. Golden, CO: 21st Century Power Partnership. Accessed August 2014: 

http://www.nrel.gov/docs/fy14osti/60575.pdf
16 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Cochran, J.; Miller, M.; Milligan, M.; Ela, E.; Arent, D.; Bloom, A.; Futch, M.; Kiviluoma, J.; Holtinnen, 

H.; Orths, A.; Gómez-Lázaro, E.; Martín-Martínez, S.; Kukod, S.; Garcia, G.; Møller Mikkelsen, K.; 

Yongqiang, Z.; Sandholt, K. (2013). Market Evolution: Wholesale Electricity Market Design for 21st 

Century Power Systems. NREL/TP-6A20-57477. Golden, CO: 21st Century Power Partnership. Accessed 

August 2014: http://www.nrel.gov/docs/fy14osti/57477.pdf

Cochran, J.; Miller, M.; Zinaman, O.; Milligan, M.; Arent, D.; Palmintier, B.; O’Malley, M.; Mueller , S.; 

Lannoye, E.; Tuohy, A.; Ben Kujala, B.; Sommer, M.; Holttinen, H., Kiviluoma, J.; Soonee, S.K. 

Flexibility in 21st Century Power Systems. (2014). NREL/TP-6A20-61721. Golden, CO: 21st Century 

Power Partnership. Accessed August 2014: http://www.nrel.gov/docs/fy14osti/61721.pdf

Corbus, D.; King, J.; Mousseau, T.; Zavadil, R.; Heath, B.; Hecker, L.; Lawhorn, J.; Osborn, D.; Smith, 

J.T.; Hunt, R.; Moland, G. (2011). Eastern Wind Integration and Transmission Study. NREL/SR-5500-

47078. Work performed by EnerNex Corporation, Knoxville, TN. Golden, CO: National Renewable 

Energy Laboratory. Accessed August 2014: http://www.nrel.gov/docs/fy11osti/47078.pdf

Denholm, P.; Jorgenson, J.; Hummon, M.; Palchak, D.; Kirby, B.; Ma, O.; O'Malley, M. (2013). Impact 

of Wind and Solar on the Value of Energy Storage. TP-6A20-60568. Golden, CO: National Renewable 

Energy Laboratory. Accessed August 2014: http://www.nrel.gov/docs/fy14osti/60568.pdf

Denholm, P.; Wan, Y.H.; Hummon, M.; Mehos, M. (2014). “The Value of CSP with Thermal Energy 

Storage in the Western United States.” Energy Procedia (49): pp. 1622-1631. 

EirGrid. (2013). “Ireland Making Progress in Meeting 2020 Renewable Energy Targets: World Leading 

Levels of Wind Energy on Grid.” EirGrid News Release, November 28, 2013. Accessed August 2014: 

http://www.eirgrid.com/media/AnnualRenewablePressRelease.pdf

Ela, E.; Gevorgian, V.; Fleming, P.; Zhang, Y.C.; Singh, M.; Muljadi, E.; Scholbrook, A.; Aho, J.; 

Buckspan, A.; Pao, L.; Singhvi, V.; Tuohy, A; Pourbeik, P.; Brooks, D.; Bhatt, N. (2014). Active Power 

Controls from Wind Power: Bridging the Gaps. NREL/TP-5D00-60574. Golden, CO: National 

Renewable Energy Laboratory. Accessed August 2014: http://www.nrel.gov/docs/fy14osti/60574.pdf

Energy and Environmental Economics, Inc. (E3). (2014). Investigating a Higher Renewables Portfolio 

Standard in California. San Francisco, CA: Energy and Environmental Economics, Inc. Accessed August 

2014: https://ethree.com/documents/E3_Final_RPS_Report_2014_01_06_with_appendices.pdf

Energy Information Administration (EIA). (2014a). “Total Energy: Annual Energy Review.” Electricity 

Category. Washington, D.C.: U.S. Department of Energy Office of Integrated and International Energy 

Analysis. Accessed February 2015: http://www.eia.gov/totalenergy/data/annual/index.cfm#electricity

Energy Information Administration (EIA). (2014b). “Levelized Cost and Levelized Avoided Cost of New 

Generation Resources in the Annual Energy Outlook 2014.” Washington, D.C.: U.S. Department of 

Energy Office of Integrated and International Energy Analysis. 

http://www.eia.gov/forecasts/aeo/electricity_generation.cfm 

Energy Information Administration (EIA). (2014c). Annual Energy Outlook 2014 with Projections to 

2040. DOE/EIA-0383(2014). Washington, D.C.: U.S. Department of Energy Office of Integrated and 

International Energy Analysis. Accessed August 2014: 

http://www.eia.gov/forecasts/aeo/pdf/0383(2014).pdf
17 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Environmental Protection Agency (EPA). (2014). “Carbon Pollution Emission Guidelines for Existing 

Stationary Sources: Electric Utility Generating Units.” Proposed Rule. Washington, D.C.: United States 

Environmental Protection Agency. Accessed January 2014: 

https://www.federalregister.gov/articles/2014/06/18/2014-13726/carbon-pollution-emission-guidelinesfor-existing-stationary-sources-electric-utility-generating

Federal Energy Regulatory Commission (FERC). (2011). “Order Conditionally Accepting in Part and 

Rejecting In Part Tariff Filing and Requiring Compliance Filings.” Docket No. ER11-1991. Issued 

February 28, 2011. 134 FERC ¶ 61,141. 

FERC. (2012). “Integration of Variable Energy Resources.” Order No. 764 (June 22, 2012): 139 FERC ¶ 

61,246.

FERC. (2014). “Transmission Planning and Cost Allocation.” Order No. 1000 (December 18, 2014): 149 

FERC ¶ 61,247.

GE Energy Consulting. (2014). PJM Renewable Integration Study. Schenectady, NY: GE Energy 

Consulting. 

Esterly, S. (2014). 2013 Renewable Energy Data Book. DOE/GO-102014-4491. Washington, D.C.: U.S. 

Department of Energy Office of Energy Efficiency and Renewable Energy. Accessed December 2014: 

http://www.nrel.gov/docs/fy15osti/62580.pdf

Goggin, M. (2013). “Xcel Colorado sets U.S. record with over 60% wind.” Into the Wind: The AWEA 

Blog. November 1, 2013. Accessed December 2014: http://aweablog.org/blog/post/xcel-colorado-sets-usrecord-with-over-60-wind

Hawaiian Electric, Maui Electric, and Hawai’i Electric Light. (2014). Maui Electric Power Supply 

Improvement Plan. Page 3-1, table 3-1. Accessed December 2014: 

http://files.hawaii.gov/puc/1_Dkt%202011-0092%202014-08-26%20MECO%20PSIP%20Report.pdf. 

Holttinen, H. (2013). Expert Group Report on Recommended Practices: Wind Integration Studies. 

International Energy Agency Wind Task 25. Paris, France: International Energy Agency. Accessed 

August 2014: 

http://www.ieawind.org/index_page_postings/100313/RP%2016%20Wind%20Integration%20Studies_A

pproved%20091213.pdf

Hummon, M.; Kiliccote, S. (August 2013). “DR Resources for Energy and Ancillary Services in the 

West.” PR-6A20-60165. Presented at the 2013 EUCI Conference: Western Energy Imbalance Market 

Demand Side and Transmission Solutions. Accessed August 2014: 

http://www.nrel.gov/docs/fy14osti/60165.pdf

Intergovernmental Panel on Climate Change (IPCC). (2011). IPCC Special Report on Renewable Energy 

Sources and Climate Change Mitigation. Prepared by Working Group III of the Intergovernmental Panel 

on Climate Change [O. Edenhofer, R. Pichs-Madruga, Y. Sokona, K. Seyboth, P. Matschoss, S. Kadner, 

T. Zwickel, P. Eickemeier, G. Hansen, S. Schlömer, C. von Stechow (eds)]. Cambridge, UK and New 

York, NY: Cambridge University Press. http://srren.ipcc-wg3.de/report

International Energy Agency (IEA). (2013a). Renewable Energy Medium-Term Market Report 2013. 

Paris, France: International Energy Agency. http://www.iea.org/textbase/npsum/mtrenew2013sum.pdf
18 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

International Energy Agency (IEA). (2013b). “Renewable Energy Outlook,” Chapter 6. World Energy 

Outlook 2013. Paris, France: International Energy Agency. 

http://www.worldenergyoutlook.org/media/weowebsite/2013/WEO2013_Ch06_Renewables.pdf

International Energy Agency (IEA). (2014). The Power of Transformation: Wind, Sun and the Economics 

of Flexible Power Systems. Paris, France: International Energy Agency. 

Lazard. (2014). Lazard’s Levelized Cost of Energy Analysis – Version 8.0. New York, New York: Lazard. 

Accessed December 2014: http://www.lazard.com/PDF/Levelized%20Cost%20of%20Energy%20-

%20Version%208.0.pdf

Lew, D. (2010). Western Wind and Solar Integration Study. NREL/SR-550-47434. Work performed by 

GE Energy, Schenectady, NY. Golden, CO: National Renewable Energy Laboratory. Accessed August 

2014: http://www.nrel.gov/docs/fy10osti/47434.pdf

Lew, D.; Brinkman, G.; Ibanez, E.; Florita, A.; Heaney, M.; Hodge, B.-M.; Hummon, M.; Stark, G.; 

King, J.; Lefton, S.A.; Kumar, N.; Agan, D.; Jordan, G.; Venkataraman, S. (2013). The Western Wind and 

Solar Integration Study Phase 2. NREL/TP-5500-55588. Golden, CO: National Renewable Energy 

Laboratory. http://www.nrel.gov/docs/fy13osti/55588.pdf

Lew, D.; Milligan, M.; Jordan, G.; Piwko, R. (2011). “The Value of Wind Power Forecasting.” Presented 

at the 91st American Meteorological Society Annual Meeting, the Second Conference on Weather, 

Climate, and the New Energy Economy, January 26, 2011. NREL/CP-5500-50814. Golden, CO: National 

Renewable Energy Laboratory. Accessed August 2014: http://www.nrel.gov/docs/fy11osti/50814.pdf

The Local. (2015). “Danish wind energy has record year.” The Local Denmark, Science and Technology. 

January 6, 2015. Accessed January 2015: http://www.thelocal.dk/20150106/danish-wind-energy-hasrecord-year

McCrone, A.; Usher, E.; Sonntag-O’Brien, V.; Moslener, U.; Grüning, C. (2014). Global Trends in 

Renewable Energy Investment 2014: Key Findings. Frankfurt, Germany: Frankfurt School-UNEP 

Centre/Bloomberg New Energy Finance. Accessed August 2014: http://fs-unepcentre.org/publications/gtr-2014

Miller, M.; Bird, L.; Cochran, J.; Milligan, M.; Bazilian, M.; Denny, E.; Dillon, J.; Bialek, J.; O’Malley, 

M.; Neuhoff, K. (2013). “RES-E-NEXT: Next Generation of RES-E Policy Instruments.” Washington, 

DC: International Energy Agency’s Implementing Agreement on Renewable Energy Technology 

Deployment (IEA-RETD). Accessed August 2014: http://iea-retd.org/wp-content/uploads/2013/07/RESE-NEXT_IEA-RETD_2013.pdf

Milligan, M. (2011). “Capacity Value of Wind Plants and Overview of U.S. Experience.” Presented at the 

2011 Power Systems Computation Conference. NREL/PR-5000-52856. Golden, CO: National Renewable 

Energy Laboratory. Accessed August 2014: http://www.nrel.gov/docs/fy11osti/52856.pdf

Milligan, M.; King, J.; Kirby, B.; Beuning, S. (2011). “Impact of Alternative Dispatch Intervals on 

Operating Reserve Requirements for Variable Generation.” CP-5500-52506. Ackerman, T., ed. 

Proceedings of the 10th International Workshop on Large-Scale Integration of Wind Power into Power 

Systems as well as on Transmission Networks for Offshore Wind Power Plants; 25-26 October 2011, 

Aarhus, Denmark. Langen, Germany: Energynautics GmbH. 
19 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Morjaria, M.; Anichkov, D.; Chadliev, V.; Soni, S. (2014). “A Grid-Friendly Plant.” IEEE Power & 

Energy Magazine May-June 2014. Accessed February 2015: 

http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6802493&tag=1. 

Novas Energias. “Portugal registou a maior produção diária de energia eólica de sempre no último 

domingo, por causa do forte vento que se fez sentir durante todo o dia.” November 18, 2011. Accessed 

August 2014: http://novasenergias.org/energia-eolica/portugal-bateu-recorde-de-producao-de-energiaeolica/

Porter, K.; Mudd, C.; Fink, S.; Rogers, J.; Bird, L.; Schwartz, L.; Hogan, M.; Lamont, D.; Kirby, B. 

(2012). Meeting Renewable Energy Targets in the West at Least Cost: The Integration Challenge. 

Denver, CO: Western Governors’ Association. Accessed August 2014: 

http://www.westgov.org/component/docman/doc_download/1610-meeting-renewable-energy-targets-inthe-west-at-least-cost-the-integration-challenge-full-report?Itemid

Porter, M. (1990). “The Competitive Advantage of Nations.” Harvard Business Review (March-April 

1990); pp. 73-91. 

Reisz, J.; Milligan, M. “Designing electricity markets for a high penetration of variable renewables.” 

(2014). WIREs Energy and Environment (July 2014). 

REN21. Renewables 2014: Global Status Report. (2014). Paris, France: REN21. Accessed August 2014: 

http://www.ren21.net/ren21activities/globalstatusreport.aspx

Sills, B. (2012). “Spanish Wind Power Reaches Record as North Suffers Weather Alert.” Bloomberg 

News (April 16, 2012). Accessed August 2014: http://www.bloomberg.com/news/2012-04-16/spanishwind-power-reaches-record-as-north-suffers-weather-alert.html

Shurey, D. (2014). H2 2014 North America Wind Market Outlook. New York, NY: Bloomberg New 

Energy Finance.

Watson, D.; Matson, N.; Page, J., Kiliccote, S.; Piette, M.; Corfee, K.; Seto, B.; Ralph Masiello, R.; 

Masiello, J.; Molander, L.; Golding, S.; Sullivan, K.; Johnson, W.; Hawkins, D. (2012). Fast Automated 

Demand Response to Enable the Integration of Renewable Resources. LBNL-5555E. Berkeley, CA: 

Lawrence Berkeley National Laboratory. Accessed August 2014: http://drrc.lbl.gov/sites/all/files/LBNL5555E.pdf

Wernau, J.; Richards, A. (2014). “As Exelon threatens to shut nuclear plants, Illinois town fears fallout.” 

Chicago Tribune March 9, 2014. 

Wiser, R.; Bolinger, M. (2014). 2013 Wind Technologies Market Report. LBNL-6809E. Berkeley, CA: 

Lawrence Berkeley National Laboratory. Accessed January 2014: http://emp.lbl.gov/sites/all/files/lbnl6809e.pdf

Xcel Energy. (2015). “Do You Know: Learn more about our use of renewable energy.” Accessed January 

2015: http://www.xcelenergy.com/Environment/Renewable_Energy/Solar/ci.Do_You_Know.de
20 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Appendix: Renewable Electricity Market Status

RE systems, including hydro, wind, biomass, geothermal, and solar, generated 515 terawatt hours of 

electricity in the United States in 2013, equivalent to 13.1% of total U.S. electricity generation (Esterly 

2014). Of this, variable RE generationwind and solarcontributed 4.6% of annual U.S. electricity 

generation. Table A-1 documents the capacity, capacity growth, generation, and top U.S. state markets for 

RE sources in 2013. The largest installed capacities are hydropower (78 GW) and wind (61 GW14), but 

the fastest growth technologies were PV (12 GW) and concentrated solar power (CSP) (1 GW) markets 

(Esterly 2014). California leads U.S. states in terms of total installed RE capacity, ranking first for solar 

PV, solar CSP, geothermal, and biomass (Esterly 2014). Texas is the leading wind producer, and 

Washington remains first in hydropower production. Expansion of the U.S. renewables market has been 

driven primarily by reduced generation costs, federal tax incentives, and state-level renewable portfolio 

standards (IEA 2013b; McCrone et al. 2014).

15 

Table A-1. 2013 U.S. Installed Capacity and Generation for Renewable Energy Technologies

Technology 

Cumulative 

Capacity

(GW)

2013 

Additional 

Capacity 

(GW)

Capacity 

Growth 

from 

2012

Electricity 

Generation

(GWh) and 

(Share of 

Total U.S. 

Generation)

Top Five States by Installed Cumulative 

Capacity, 

Hydro 78.5 0.2 0% 269,137

(6.6%)

Washington, California, Oregon, New York, 

Alabama

Winda 61.1 1.1 2% 167,663

(4.1%) Texas, California, Iowa, Illinois, Oregon

Biomass 14.7 0.7 5% 59,894

(1.5%) California, Florida, Virginia, Georgia, Alabama

Solar PVa 12.1 4.7 64% 21,074

(0.5%) 

California, Arizona, New Jersey, North 

Carolina, Massachusetts 

Solar CSPa 0.9 0.4 80% California, Arizona, Florida, Nevada, Hawaii

Geothermal 3.8 0.4 12% 16,518

(0.4%) California, Nevada, Utah, Hawaii, Oregon

Total RE 171.1 7.5 5% 534,286

(13.1%) California, Washington, Texas, Oregon, Iowa

a

Wind, solar PV, and solar CSP (when deployed without thermal storage) constitute variable RE technologies. Solar 

PV includes grid-connected centralized and distributed PV, but not off-grid PV.

Source: Esterly 2014

 

14 Including Puerto Rico’s installed wind capacity. 15 For example, new wind capacity additions totaled 1,087 MW in 2013, a large decrease from the 13,089 MW 

installed in 2012. This drop can be attributed largely to a late extension of and modified eligibility requirements for 

the federal production tax credit (PTC) (Wiser and Bolinger 2014). Based on projections of projects under 

construction, greater incremental increases in wind capacity are expected for 2014 and 2015 as compared to 2013, 

but are not anticipated to exceed the 2012 additions (Shurey 2014).
21 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

United States in the Context of the Global Renewable Electricity 

Market

Global RE power capacity grew to 1,560 GW in 2013, equivalent to 27% of the world’s total installed 

electricity capacity, including 8% from variable RE technologies (Esterly 2014). Worldwide, the highest 

capacity growth from 2012 occurred in the solar PV (39%), solar CSP (36%), and wind markets (12%). 

Table A-2 presents information equivalent to that in Table A-1 but at a global scale. The United States

ranks in the top five countries in terms of installed capacity in each of the major commercialized RE 

technologies. China, the United States, Brazil, Canada, and Germany have the most installed renewable 

electricity capacity. 

RE generation also meets increasingly high percentages of annual demand, including in the countries of 

Denmark (33.2% from wind), Spain (20.9% from wind), and Italy (7.8% solar PV) (REN21 2014).

Instantaneous RE penetration levels – the percentage of electricity supplied from RE at any given time – 

have reached over 90% in Denmark and Portugal (Novas Energias 2011), and over 60% in Spain (Sills 

2012) and Colorado (Goggin 2013). The island grids of Ireland and Hawaii have also experienced high 

levels of instantaneous renewable energy penetration, with incidents of 50% and 60.7% respectively 

(EirGrid 2013; Hawaiian Electric et al. 2014). These levels are especially significant given that the two 

power systems do not have access to large interconnections with which they can balance variability.

Table A-2. 2013 Global Installed Capacity and Generation for Renewable Energy Technologies

Technology 

Cumulative 

Capacity

(GW)

2013 

Additional 

Capacity 

(GW)

Capacity 

Growth 

over 

Previous 

Year

Generation 

(GWh) and 

(Share of 

Total Global 

Generation)

Top Five Countries by 

Installed Capacity 

(for grid-tied systems)

Winda 318.0 35.0 12% 835,704

(3.7%)

China, United States, 

Germany, Spain, India

Solar PVa 139.0 39.0 39% 177,916

(0.8%)

Germany, China, Italy, Japan, 

United States

Solar CSPa 3.4 0.9 36% Spain, United States, United 

Arab Emirates, India, Algeria 

Geothermal 12.0 0.5 3% 73,584

(0.3%)

United States, Philippines, 

Indonesia, Mexico, Italy

Biomass 88.0 5.0 6% 416,275

(1.8%)

United States, Germany, 

China, Brazil, India

Hydro 1,000.0 40.0 1% 3,591,600 

(15.9%)

China, Brazil, United States, 

Canada, Russia

Total RE 1,560.0 120.4 6% 5,095,079

(22.6%)

China, United States, Brazil, 

Canada, Germany

a

Wind, solar PV, and solar CSP (when deployed without thermal storage) constitute variable RE technologies.

Source: REN21 2014

Despite significant increases in installed capacity in 2013, global investments in new RE electricity 

generation and fuel production capacity decreased by 23% from the 2011 high of US$279 billion and by 
22 

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14% from 2012, settling at $214 billion in 2013 (REN21 2014; McCrone et al. 2014).16 By comparison, 

$36 billion was invested in 2013 in the United States (McCrone 2014).

Reduced investment may be due to several factors, including flat or decreasing growth in electricity 

demand, decreased RE capital costs, and policy uncertainty. Annual U.S. electricity generation declined 

from a high in 2007 of 4.2 trillion kWh to 4.1 trillion kWh in 2013 (EIA 2014c). RE capital costs have 

dropped as wellfrom Q1 of 2010 through Q1 of 2014, the levelized costs of energy for crystalline 

silicon PV and onshore wind have fallen by 53% and 15%, respectively (McCrone et al. 2014). 

Despite reduced capital and generation costs, a key driver of RE deployment is government policy 

incentives (REN21 2014). Targets, feed-in tariffs, tenders, and tax incentives are offered in over 100 

countries, although the recent drop in costs may provide room for governments to lower financial 

incentives (IEA 2013b).17 However, recent sudden and extreme policy changes have created perceptions 

of additional investment risk and subsequent market booms and busts, most notably in the solar PV and 

onshore wind markets (IEA 2014). 

Manufacturing

Globally, the manufacturing of renewable energy equipment has grown significantly over the past 10

years, with new annual investment increasing from US$600 million in 2004 to $12.5 billion in 2013 

(REN 21 2014). Sixty-four percent of solar modules are produced in China, followed by the rest of Asia 

(16%), Europe (9%), Japan (6%), and the United States (2%) (Esterly 2014).18 China, Denmark, 

Germany, India, Spain, and the United States lead in wind turbine manufacturing (REN21 2014). 

As with other industries, decisions on where to manufacture RE components are driven by several factors, 

including market and policy considerations, learning effects such as economy-of-scale benefits, the ease 

of doing business, and regulatory contexts (Porter 1990). In some instances, developers have been drawn 

to regions with low-cost financing, and may choose locations that minimize specific business risks, such 

as exchange rate risks, trade fees and barriers, and shipping costs (IEA 2013b). High levels of competition 

among manufacturers, policy changes, and dynamic market conditions are driving consolidation among 

solar PV and wind manufacturers in select regions (IEA 2013b). 

Technology Cost and Performance Characterizations

Table A-3 summarizes renewable electricity system cost and performance characterizations for the United 

States, as determined by the Energy Information Administration (EIA) and Bloomberg New Energy 

Finance (BNEF), in terms of typical capacity factor, cost, and lifespan. LCOEs are project-specific and 

are highly dependent on climate and location, among other factors. Nevertheless, based on national 

averages, EIA’s 2014 Annual Energy Outlook Reference Case projects that the LCOE for wind will be 

cost-competitive with new coal and nuclear ($100/MWh) as well as natural gas ($70/MWh) by 2019 (EIA 

2014b). Other projections, including those by BNEF, indicate even lower LCOEs by 2019, with solar PV 

potentially also competitive with coal and nuclear. Even for those plants that are not cost-competitive, 

 

16 Bloomberg New Energy Finance (BNEF) investment data includes biomass, geothermal, wind, and hydropower 

projects over 1 MW; all ocean energy; all biofuels over 1 million liters; and all solar with small-scale projects (less 

than 1 MW) estimated separately (REN21 2014). Investments are reported in nominal dollars. 17 A feed-in tariff is a production-based incentive for renewable energy often coupled with additional supportive 

policies, such as priority access to the power grid. A tender is a public competitive bidding process whereby 

independent power producers make offers to supply certain amounts and types of energy at a specific price to a 

utility or power system operator. 18 Modules are one of many components in the PV supply chain.
23 

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long-term, unsubsidized power purchase agreements for wind or solar can be a hedge against future fuel 

prices (IEA 2013a).19

Table A-3. Technology Characterizations

Approximate Typical

Capacity Factor20

Expected LCOE in 2019

($/MWh)

Economic 

Lifespan

(years)

Winda 35% $37 - $81 24

Solar PVa 

(Utility Scale) 25% $72 to $86 20 - 30

Solar CSPa 20% to 56% $11821 to $130 (with storage) 20 - 30

Geothermala 92% $89 to $142 25 - 30

Biomass 83% $87 to $116 20

Hydropower 53% $85 40 - 80

a

Wind, solar PV, and solar CSP (when deployed without thermal storage) constitute variable RE technologies.

Geothermal characterization applies to conventional hydrothermal resources.

Sources: Capacity factors and hydropower LCOE estimate: EIA 2014b; Other LCOE estimates: Lazard 2014; 

Lifespan: IPCC 2011. 

Projections of Future Renewable Electricity Deployment

According to the EIA’s 2014 Annual Energy Outlook Reference Case, between 2013 and 2017, installed 

RE capacity in the United States is expected to have a five-year compound annual growth rate (CAGR) in 

a range of 0.82% to 3.50% (EIA 2014c), assuming existing incentives and associated expirations in 

current law. A high rate of growth in installed capacity is projected for solar PV in the near term, with 

moderate rates of growth in solar CSP, and wind. No offshore wind deployment is anticipated in the 

United States in the near term, and projections exclude emerging technologies (EIA 2014c).22 Looking 

further into the future, from 2018 through 2022, the EIA scenarios estimate a significantly decreased rate 

of growth with approximately 1% annual growth for all technologies.23 Other organizations, such as 

BNEF offer alternative projections. BNEF anticipates a five-year CAGR of 8.28% between 2013 and 

2017 for new wind capacity, assuming no extension of the production tax credit and with continued 

implementation of current state renewable portfolio standards (BNEF 2013).24 

Although not modeled in the EIA projections, EPA’s Clean Power Plan proposal could result in states 

implementing additional policies to meet state-level carbon emissions targets (EPA 2014). It is unclear 

 

19 See Blair (forthcoming) for another set of cost and performance assumptions that will inform NREL’s electric 

sector analysis conducted in 2015, http://www.nrel.gov/analysis/data_tech_baseline.html. 20 The capacity factors included within this table are typical and not system-specific. Power grid operators have 

likely defined their own capacity factor estimates for aggregated systems over time, which may differ from the 

capacity factors included here. 

21 The estimated $130 LCOE for solar CSP is for a molten salt power tower, in real $2010 and with a capacity factor 

of 56% (10 hours of thermal energy storage) (DOE 2014). 

22 These emerging technologies include Engineered Geothermal Systems (EGS) and a variety of ocean technologies. 23 For details on the EIA projection assumptions, see Appendix E of the 2014 AEO Report: 

http://www.eia.gov/forecasts/aeo/pdf/0383%282014%29.pdf. 24 See Blair (forthcoming) for another set of deployment projections based on a range of assumption scenarios and 

using NREL’s electric sector capacity expansion and dispatch model, the Regional Energy Deployment Systems 

(ReEDS). 
24 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

what impact this plan will have on renewables markets, if and when implemented, both because the plan 

may change significantly before it is finalized and because states have many ways to meet these targets. 

For example, states could improve coal plant efficiency, use combined cycle gas, operate existing nuclear 

plants, and increase energy efficiency and/or RE to meet carbon emissions goals.

Global renewable electricity capacity is projected to grow significantly over the next five years (IEA 

2013a), including double-digit CAGRs in offshore wind, solar CSP, and solar PV (IEA 2013a), as 

illustrated in Table A-4.

Table A-4. Projected Global RE Capacity by Technology, from 2013 – 2017 (GW)

2013 2014 2015 2016 2017 5-Year 

CAGR

Commercialized 

Technologies 

Wind - 

onshorea 313 357 399 442 486 9.2%

Solar PVa 128 161 194 230 268 15.9%

Solar CSPa 4 6 7 8 10 20.1%

Geothermal 12 12 13 14 14 3.1%

Hydropower 1,138 1,173 1,209 1,249 1,291 2.6%

Emerging 

Technologies 

Wind - 

offshorea 8 11 14 17 22 22.4%

Ocean 1 1 1 1 1 0.0% 

Total RE 1,69325 1,815 1,941 2,073 2,211 5.5%

a Onshore wind, offshore wind, solar PV, and solar CSP (when deployed without thermal storage) constitute variable 

RE technologies. 

Source: IEA 2013a

 

25 Note the IEA reported and projected renewable electricity capacity numbers include both on- and off-grid 

systems. Also, IEA’s reported installed capacity for 2013 differs from the REN21 Renewable Energy Global Status 

Report estimate of 1,560 GW installed as of 2013. See Table A-2.