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Wind Integration Cost and CostCausation

Preprint

M. Milligan,1 B. Kirby,2 H. Holttinen,3 

J. Kiviluoma,3 A. Estanqueiro,4 S. MartinMartinez,5

 E. Gomez-Lazaro,5 I. Peneda,6 

and C. Smith7

1 National Renewable Energy Laboratory

2 Consultant

3 VTT Technical Research Centre

4 Laboratório Nacional de Energia e Geologia 

5 University of Castilla–La Mancha 6 European Wind Energy Association

7 Utility Variable-Generation Integration Group

To be presented at the 12th International Workshop on Large-Scale 

Integration of Wind Power Into Power Systems

London, England

October 22–24, 2013

Conference Paper

NREL/CP-5D00-60411

October 2013
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Wind Integration Cost and Cost-Causation

Michael Milligan and Brendan Kirby (Consultant) 

National Renewable Energy Laboratory, CO, USA

Hannele Holttinen and Juha Kiviluoma

VTT Technical Research Centre, Finland

Ana Estanqueiro 

Laboratório Nacional de Energia e Geologia, Lisbon, Portugal

Sergio Martin-Martinez and Emilio Gomez-Lazaro

University of Castilla–La Mancha, Spain

Iván Pineda 

European Wind Energy Association, Brussels, Belgium

Charlie Smith

Utility Variable-Generation Integration Group, VA, USA

Abstract—The question of wind integration cost has received 

much attention in the past several years. The methodological 

challenges to calculating integration costs are discussed in this 

paper. There are other sources of integration cost unrelated to 

wind energy. A performance-based approach would be 

technology neutral, and would provide price signals for all 

technology types. However, it is difficult to correctly formulate 

such an approach. Determining what is and is not an integration 

cost is challenging. Another problem is the allocation of system 

costs to one source. Because of significant nonlinearities, this can 

prove to be impossible to determine in an accurate and objective 

way. 

Keywords-wind; integration cost 

I. INTRODUCTION

Increasing deployment of wind energy in many parts of the 

world, coupled with a desire to accurately assess and assign 

costs to their source, has given much attention to the question 

of integration costs in the past several years. Although the basic 

idea appears to be quite simple, it turns out to be much more 

difficult in practice. The fundamental interest is to estimate the 

costs that are imposed on the power system for accommodating 

wind power, consisting primarily of the operational impact of 

wind power’s variability and uncertainty and investments in 

grid infrastructure. This information is needed on the one hand 

for policy makers to ensure that the benefits of increasing wind 

energy will not be offset by negative impacts, and on the other 

hand for system operators and regulators to ensure fair 

treatment of all producers when designing market rules, tariffs, 

and allocation of costs. For policy makers, the integration costs 

could be compared with the benefits of wind power. For 

system operators and regulators, it is also important to see how 

current tariffs take into account these costs, such as network 

charges (to cover investments in network) and imbalance 

payments (to cover extra balancing costs). In many regions, 

wind power producers also pay for direct investment costs for 

grid connection. To treat wind power producers fairly, the 

same cost-calculation methodology should also be applied to 

other generation assets.

Any change in the resource mix, whether in shares of wind 

power or other forms of generation, will likely result in shifts 

in total system costs and changes in the costs incurred by other 

generators. Determining which of these costs are “integration 

costs” has proven to be surprisingly difficult. Integration costs 

are not directly observable, and this has resulted in numerous 

methods to calculate them. The use of different methods means 

that it is difficult, or impossible, to compare integration costs 

from different power systems or studies. Allocating integration 

cost to wind, or to any other technology, is really a policy 

question, and there may be multiple plausible (but not 

necessarily correct) ways to do so. Production cost modeling is 

now quite good at comparing costs between defined future 

scenarios. The problem is in specifying the scenarios to 

compare so that integration costs can be determined. 

Allocating integration costs to a single resource type is 

challenging. The principles of cost-causation and 

methodological challenges to calculating integration costs have 

been discussed in [1]. Cost-causation-based tariffs provide 

transparent signals to markets and regulators that, if well 

defined, provide appropriate incentives for efficient investment 

and behavior [2]. Common errors and important assumptions in 

integration cost analyses are reported in [3]. 

Integration costs, once calculated, are not always applied in 

the same way. One application is to add the integration cost to 

the cost of energy from wind power to provide a comparison of 

wind energy to a more dispatchable technology, such as natural 

gas. Another application is to use increases in balancing costs 

or ancillary services in tariffs that aim to allocate the cost of the 

variability and uncertainty impacts of wind power. However, as 

wind turbine technology advances so that some ancillary 

services can be provided by wind power, estimating the need 

for more ancillary services as a result of wind power is no 

longer enough. This calls for a more rigorous assessment 

method that can capture both consumption and provision of 

ancillary services. Further, a performance-based approach 

would be technology neutral and provide price signals for all 

technology types.

In this paper, the focus is first on the issue of operational 

integration cost. We then discuss total system cost (fixed plus 

variable). The focus is on total portfolio cost, which can be 

compared for two or more portfolios. Methods for the 

estimation of integration costs and benefits are discussed. 
2 

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II. WHAT ARE INTEGRATION COSTS AND WHY CALCULATE 

THEM? 

The idea of integration costs at first seems quite simple. 

They are supposed to be the “extra” costs imposed on the 

power system as it accommodates an unusual resource. 

Integration costs, once calculated, are sometimes used to 

compare wind power with some form of conventional power, 

which presumably has no integration cost itself (we show that 

may not be true in a later section of this paper). Production 

cost modeling appears to provide an ideal tool for the analysis. 

Production cost modeling with security-constrained unit 

commitment and security-constrained economic dispatch is 

quite accurate when it is coupled with time-synchronized, 

high-quality wind resource time-series data, time-series load 

data, and good data on the conventional generators’ costs and 

capabilities. The power system can be modeled with and 

without wind generation for a year or more, and the costs can 

be compared. However, if the without-wind case is the power 

system load supplied by conventional generators, then the 

difference between the with and without wind cases will be 

dominated by the fuel, emissions, and water savings that wind 

offers. These fuel, emissions, or water savings are benefits of 

wind energy. Only the “extra” costs imposed by wind 

variability and uncertainty should be included as an 

integration cost. As will be discussed below, one possible 

solution is to include a suitable proxy resource that does not 

have fuel, emissions, or water costs, but even this solution is 

not ideal.

The question of what constitutes integration costs is more 

complex than simply accounting for fuel, emissions, and 

water. Integration cost for conventional generation will be 

discussed more fully in Section IV, but here we examine a few 

costs imposed by conventional generators that have not 

historically been allocated to them as integration costs to show 

that assigning similar costs to wind may not be appropriate or 

consistent across multiple technologies. 

Increased cycling of conventional generators is often 

considered a wind integration cost, but a new thermal 

generator can also cause increased cycling of existing 

generators. A new high-efficiency baseload generator will 

displace existing baseload and mid-merit power plants, 

causing them to cycle more. This increased cycling of existing 

generators has not been considered an integration cost for new 

thermal plants [1]. 

Adding new wind power will also result in the reduction in 

capacity factors for some conventional generators unless 

reductions are offset by simultaneous load increases. Large 

wind penetration levels mean that the optimum composition of 

the remaining generation fleet is different than in cases of no 

wind. There will be less baseload units and more midmerit/peaking units. This also means that the total costs 

(operating and capital costs) of the electricity system may be 

higher (or lower, depending on relative fuel prices and costs). 

In many countries, the power system is in a transition period 

in which the system is adapting to the widespread introduction 

of wind power plants. This transition will likely involve costs 

that will be reduced or eliminated when the system has 

evolved to a new generation mix. The power plant fleet will 

not reach the new capacity equilibrium immediately because 

power plant retirements and new investments take years. This 

is already seen in some power systems with high penetration 

levels of wind power; the remaining conventional power 

plants are used less and are not as profitable. This is an 

important cost component for the power system; however, the 

same happens when new thermal power plants are built. Older 

power plants with higher operational costs will generate less.

Adding storage or demand response can have a similar impact 

on reducing the capacity factors of higher cost generators. 

This is not typically considered an integration cost that should 

be charged against storage or demand response. 

Similarly, contingency reserves are required, in part, to 

compensate for the sudden loss of the largest generator. Yet 

contingency reserves are not allocated to generators as 

integration costs. A new large generator may increase the 

contingency requirement within a reserve-sharing pool, 

causing the cost of supplying a higher reserve level to be 

incurred by others. 

Hourly block scheduling of generation and of interbalancing authority area transactions increase regulation 

requirements more than 5-minute dispatch and schedules, but 

are not typically considered an integration cost or allocated the 

increased cost of the required regulation [7]. 

In conclusion, determining integration costs is much more 

complex than simply calculating differences in total between a 

production cost simulation with and without wind.

III. SOURCES FOR WIND INTEGRATION COSTS

In this section, we review what properties of wind 

generation cause integration costs, starting with a short 

discussion of power system characteristics.

A. Characteristics of the power system

The costs to integrate the variability and uncertainty of 

wind power will change considerably between power systems 

[9], [6]. They are impacted by the cycling and efficiency 

characteristics of existing power plants as well as rules and 

regulations of the system [10]. When estimating integration 

costs for future systems, the integration cost can be heavily 

impacted by the assumptions of available flexibility options 

[11]. The opportunities for future cost reductions are not yet 

fully known, which causes a layer of uncertainty for the 

results. 

B. Variability 

Variability of wind power, even if correctly forecasted, 

will result in increased regulation and ramping of the 

remaining system. 

Variability also impacts the planning time scale for power 

system resource (capacity) adequacy. During peak load 

situations, only a little wind may be available. Because 

capacity is typically valued explicitly, this will tend to be 

captured as a reduction in the value of wind rather than as an 

integration cost. This issue also typically applies to hydro 

power. 
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C. Uncertainty 

Uncertainty of wind power will result in increases in 

(flexibility/operating) reserves over multiple time frames. 

Large amounts of wind typically have only a modest impact 

on the second-to-second regulation requirements, 1 but the 

need for load-following/ramping reserves can be higher. It can 

result in keeping more reserves (allocation and dimensioning 

reserve requirements) and more use of reserves. Uncertainty

can be mitigated through better forecasting. The economic 

impact is greatly reduced in regions with subhourly energy 

scheduling for all generators.

D. Location of resources—transmission infrastructure 

All forms of generation include grid-connection costs, and 

they are usually borne by the producers as part of their 

investment costs. On top of that, depending on the site and the 

grid adequacy, reinforcements to the existing grid may also be 

needed—or they may prove cost efficient to get all electricity 

generated to load centers. Grid reinforcements usually also 

provide a reliability benefit to the system and may also

decrease existing bottlenecks and therefore decrease 

operational costs of the power system. In some cases, adding 

new transmission can reduce the need for installed capacity. 

This occurs because transmission additions may have a 

capacity value, as shown in [8]. 

IV. INTEGRATION COSTS FOR CONVENTIONAL GENERATION

Integration impacts are not exclusive to wind and solar. 

Nearly all generators can impose costs on the power system or 

other generators when they are added to the power system. 

These impacts are seldom calculated as integration costs and 

never applied to conventional generators as integration costs. 

Thermal power plants are different in their design and 

flexibility and possibilities of providing ancillary services. 

There can be units that have difficulty following automatic 

generation control (AGC), for example [3]. 

Adding a new baseload plant can also increase the costs of 

operating other generators, in the same manner as wind 

power—decreasing the operation time (capacity factors) for 

mid-merit power plants and increasing cycling of these plants 

[1]. 

Contingency reserve requirements result largely because 

some conventional generators are large. No generator is 100% 

reliable, and the power system must continuously stand ready 

to respond if a large generator or transmission facility suddenly 

fails. Exact rules vary from region to region and country to 

country, but contingency reserve requirements are typically 

based on the size of the largest generator. Each balancing 

authority area or reserve sharing pool must keep enough 

spinning and nonspinning reserve ready to respond if a 

generator fails. The cost of maintaining these reserves is not 

allocated to the generators that cause the need, however. 

 1 In the United States, regulation is provided by units on 

automatic generation control, and typically covers the 

variability that occurs between successive economic 

dispatches.

Instead the cost is broadly spread across all users of the 

transmission system. This has the effect of allocating costs 

based on capacity or output rather than on size or contribution 

to contingency reserve requirements. Costs could be allocated 

based on cost-causation, as shown in Fig. 1, but they are not.

Instead, these costs are socialized, and have been for many 

years. Current practice has the effect of subsidizing the large 

generators at the expense of the small generators (or their 

customers) [5][12]. 

Figure 1. Contingency reserve costs could be allocated to 

each generator based on their contribution to the contingency 

reserve requirements [5]. 

Hydro generation with storage is typically very responsive 

with low cycling costs. There is both seasonal and annual 

variability and uncertainty in the water availability, which can 

reduce the long-term capacity value. There are cases in which 

constraints can cause inflexibility and also an integration 

burden. For example recent environmental restrictions in the 

United States associated with preserving endangered fish have 

reduced the flexibility available to the power system from 

many hydro projects. If excess water cannot be spilled but must 

be run through the turbine generators, the power system must 

accept the excess power. This may require uneconomic cycling 

of thermal power plants or curtailing wind with a loss in 

production tax credits and renewable certificates. What was 

previously only a lost economic opportunity (spilled water that 

did not generate electricity revenue) is now a direct cost 

(uneconomic cycling of thermal plants and curtailed wind 

production). This represents a real integration cost of 

constrained hydro. There are ways to mitigate the impacts—

just as for excess wind power during light loads—with load 

processes redesigned to make profitable use of the surplus 

energy, or with storage systems in the future, but there may be 

costs associated with implementing these solutions as well. 

Run-of-river hydro is variable and somewhat uncertain, 

though both the short-term variability and uncertainty of hydro 

are typically much less than for wind or solar generation. The 

same analysis techniques used to determine wind and solar 

integration costs are appropriate for run-of-river hydro. 
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Gas scheduling and contracting can limit the flexibility of 

gas-fired generators significantly below the physical capability. 

Although there is ample physical capability to respond to 

changing load conditions and changes in the rest of the 

generation fleet up until the operating hour, the gas scheduling 

restricts this flexibility if gas is nominated day-ahead, 

committing the generator to operate or not operate in 

essentially the same time frame as coal plant commitment. This 

problem is compounded on weekends and holidays. 

Another issue that can impact gas availability is extreme 

weather conditions that can result in gas shortages impacting 

all gas-fired generators in a region. This may represent a much 

larger contingency than the power system is designed to 

survive. This occurred in Texas and the Southwest region of 

the United States in February 2011, when system operators 

were forced to shed firm load to cope with the loss of 

generation. 

V. DIFFICULTIES IN ALLOCATING INTEGRATION COSTS

System costs in many places are not allocated to generators 

but borne by consumers (contingency reserve costs, for 

example). Allocation is often a policy choice. Because of the 

difficulty of calculating and allocating costs to parts of system, 

in some cases simple rules of thumb are used. This will mute 

incentives for generators to improve their performance, and 

may not be consistent with cost-causation. Two wind plants, 

for example, may have different variability per unit, yet the 

variability impact, if assigned based on a rule of thumb, would 

not differentiate between them. There may also be unintended 

consequences of allocation. 

The allocation of grid investments to wind power is also 

challenging, and rarely done by transmission system operators, 

because new infrastructure usually benefits all users and 

investments are also made for improving electricity markets 

and increasing the security of the system.2

 If grid adequacy is 

insufficient during limited time periods, grid reinforcements 

could be compared with the option of not using all available 

wind or altering the operation of other generation. This 

comparison can yield information about the most economic 

option. Building new transmission, or increasing transmission 

capacity that has only a marginal impact on congestion, may be 

less economic than occasionally curtailing wind power (or 

other options).

Aggregation is a very powerful concept that has been used 

to increase system reliability and reduce costs for more than a 

century. It provides benefits because many individual 

requirements (contingency reserves, peak load, regulation, etc.) 

are not 100% correlated. Consequently, the total system needs 

far less reserves than would be required if each individual had 

to supply its own needs. Contingency reserves provide a clear 

example in which many generators can share the same reserve 

pool.

 2 A radial connection from a wind plant to a grid is often an 

exception.

However, it is not straightforward to allocate the reduced 

requirement among participants. Incremental allocation may 

make sense, but the drawback is that it is dependent on the 

order (wind plant 1, 2) added to the system. It is possible to 

allocate regulation requirements based on an individual entity’s 

contribution to the total requirement, but this has never been 

done for any resource other than wind [12]. Interestingly, 

although individual steel arc furnaces have a dramatically 

higher impact on power system regulation requirements, they 

are never allocated their cost of regulation. Instead, the rest of 

the customers subsidize the arc furnace regulation 

requirements. 

The allocation problem is made more difficult because the 

differences between allocation method results are often subtle

and it is not immediately obvious which method is “better.” 

Some methods can be shown to be simply wrong. Charging 

each entity for the full amount of reserves required to meet its 

own needs if it were independent fails to recognize the physical 

aggregation benefits and dramatically over collects for 

resources the power system does not actually acquire and costs 

it does not incur. Many of the methods are numerically 

“correct” (they sum to the physical requirement), but can still 

lead to dramatically different results for each of the 

participating resources. Many are not “fair” in that the 

allocation results depend on sub-aggregation and/or the order 

individuals are included [13]. 

The comparability principle—that all entities should 

receive comparable treatment under similar circumstances—

would seem to require that (a) all integration costs should be 

calculated and allocated to all cost-causers, or (b) integration 

costs should be considered as part of system costs and should 

not be allocated to individual entities. Allocating costs to one 

type of generator and not to other types does not treat entities 

comparably, unless based on some type of performance metric 

related to the cost-causation in question. Similarly, allocating 

variability costs to individual generators but not to individual 

loads does not treat all entities comparably. The principle of 

comparability is consistent with a performance-based approach 

that is applied to all generators and loads.

VI. METHODS FOR ESTIMATING INTEGRATION COSTS

Many methods have been used to calculate an integration 

cost. All of the methods discussed here rely on securityconstrained unit commitment and security-constrained 

economic dispatch production cost modeling covering a year or 

more and including time-series load data, time-series of actual 

or modeled wind resource data [14], and data on the 

capabilities and costs of each conventional generator. 

Production cost modeling has advanced significantly, and it 

produces reasonably good results. Integration costs are 

calculated as the difference between a production cost 

modeling run with wind and a production cost modeling run 

without wind, but with something else that supplies the energy 

that wind would have if it were available. Variability and 

uncertainty (forecasting errors) are handled well. The problem 

with calculating integration costs is not with the production 

cost modeling itself, but rather with determining what to 

include in the without-wind case.
5 

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Across the integration cost-estimation methods there are 

inconsistent definitions, and as a result also inconsistent cost 

estimates. Because wind integration cost is not directly 

observable, multiple definitions and approaches have yielded 

different estimates, most of which are not comparable. Given 

the difficulties discussed above, we propose a few alternative 

approaches that may be useful to calculate integration cost. 

However, we caution that these proposed methods, along with 

other potential methods, cannot be objectively characterized as 

“correct” methods, because allocation of these costs can 

conceivably be done in several ways. 

Because other types of generators can impose integration 

costs that consist of adding to system variability and 

uncertainty, a robust measure of integration cost would need to 

be technology neutral and performance based. Calculating 

integration cost would therefore not be restricted to certain 

types of generators or loads, but would be applied to all and 

with the same method of calculation. One would expect to find 

differences among technologies, and among the same 

technologies, depending on the configuration of the generator.

Any proposed metric and approach to calculating 

integration cost should be examined to see how it would 

perform under alternative conditions. For example, Kirby et al. 

[2] describe a scenario in which a wind power plant can 

provide AGC (or regulation). In this case, the wind plant would 

clearly be providing regulation service, not consuming it. 

Therefore, using cost-causation principles would suggest that 

this plant should be paid for providing AGC, and would not 

cause an increase in cost to provide regulation. Conversely, [1]

shows that some thermal units that are attempting to sell 

regulation are actually consuming regulation—increasing the 

regulation burden of the system. In this case, the thermal plant 

has an integration cost based on its consumption of regulation. 

In yet a third example, a wind plant that does not provide 

regulation likely consumes it, and therefore would incur an 

integration cost for this regulation.

A. Possible approaches to calculate integration cost

Here we discuss several potential approaches to calculating 

integration cost.

1) Flat block

The most direct approach to calculating integration costs is 

to model the power system with and without wind. One option 

for the without wind case is to use a flat block of energy that 

equals the wind plants’ annual energy production in the 

without case. The flat energy block prevents the wind energy 

from being supplied by fuel burning and expensive generation. 

This method fails because a flat block has capacity value that 

the wind does not claim. The flat block likely has more onpeak hours than wind, hence the energy itself has a higher 

value. (Conversely, for solar a flat block has fewer on-peak 

hours and the solar energy has a higher value.) Because the 

integration cost is supposed to represent the added cost of 

integrating wind, it is inappropriate to include shifts of energy 

value. More-complex methods are needed.

2) Separating variability and uncertainty

Ignoring for now the impact of uncertainty, we focus on 

how to measure the cost impact of variability. With variability 

alone, and temporarily assuming perfect knowledge of the 

future, wind (and solar) generation still varies. This will 

increase the system regulation and following requirements 

(unless the wind/solar plants provide regulation). It will also 

increase cycling of other generators. This cycling cost has two 

components: (1) heat rate penalties and (2) cycling damage 

[14]. The regulation impact of each generator can be measured, 

and the cycling costs can be estimated. Together these would 

comprise the variability cost component of integration cost. To 

estimate the cycling cost, two simulations would be executed.

The first case would represent the system without including the 

cycling cost as part of each unit’s variable cost. The second 

case would include this cycling cost [14], and the difference in 

cost between the two cases would represent the cycling cost.

It should be noted that these costs are a function of the 

installed generation fleet characteristics as well as correlation 

of wind generation with electricity demand. Even with perfect 

foresight, wind variability would increase the regulation, 

following, and cycling cost of some generators. However, it is 

also clear that any new baseload generation that is introduced 

to the power system at low variable cost would increase the 

cycling of at least one generator that moves up the merit-order 

and must cycle more than before. Thus, it must be recognized 

that variability cost may be imposed by conventional 

generators. 

Uncertainty costs can be measured by evaluating additional 

flexibility reserves and calculating their cost using either 

market data or production simulation. Reserves are needed to 

protect against the unknown future. Although methods to 

calculate flexibility reserves are still evolving [14], the 

integration cost component that is calculated in this way would 

reasonably represent the incremental uncertainty cost from the 

variable generation.

3) Comparison to a “perfect” unit

This approach would involve comparing wind case with a 

“perfect unit” and simulating with wind variability and 

uncertainty. A “perfect” unit would have the same shape as 

load. With this approach, many different types of units would 

have an integration cost: baseload, wind, etc. Using this 

approach, a generator that can achieve a profile that is identical 

to load would have no integration cost. Conversely, the more 

diverse the generation profile becomes in relationship to the 

load, the higher its integration cost would be. This approach 

suffers from the inclusion of differences in the value of the 

energy in the calculated integration cost, as does the flat block 

approach discussed above. This approach is relatively new and 

has not received wide application or attention.

The many complex interactions among components of the 

power system and assumptions regarding the no-wind base 

case all have important influences on integration cost estimates, 

and in fact raise questions of whether cost components that are 

commonly thought to be integration costs can be correctly 

untangled [1]. 
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B. Total costs—including fixed costs

Another approach is to compare total costs (including fuel 

and investments) with and without wind (and solar). This 

method does not attempt to separate and identify integration 

cost, but instead allows for a full comparison of costs. It can be 

applied to operating costs only, or to the combined fixed and 

variable cost, depending on the objective of the analysis. This 

approach therefore avoids the methodological and practical 

difficulty of extracting integration cost correctly, but may have 

similar challenges regarding the comparison of cases. 

This approach compares the all-in cost of alternative 

portfolios. The impacts of modelling assumptions may increase 

considerably. One approach would require generation 

optimization to take into account the changes in the optimal 

power plant portfolio [14] and preferably assess operational 

costs with a unit commitment and dispatch model [18]. 

It is not realistic to assume that a power system can be 

operated on wind power alone. Other types of plants are 

needed. Similarly, the power system cannot be run on baseload 

power alone because there is a need for more flexibility to 

follow load variability than some baseload plants can provide. 

Indeed, some plants cannot provide AGC or have very limited 

ramping capability, requiring the addition of mid-merit units 

that have more flexibility. When such a plant is added to the 

power system, the cost of the needed flexibility is not charged 

or allocated to that plant. The system needs flexibility, voltage 

support, AGC, and many other products to be run reliably. 

There is no history of allocating the cost of ancillary services 

that are needed, but not supplied by the plant. Hence, allocating 

additional capacity or other costs to wind power would appear 

to be inappropriate for the same reasons. 

Alternative portfolios and their respective costs and 

benefits can, and should, be compared. One example is the 

approach taken in an Irish integration study [19]. Costs of 

different portfolios are compared with the benefits, as the 

amount of CO2 emissions from each scenario. 

One part of total costs is the investment for upgrading the 

network. However, the difficulty of comparison is that the 

benefit of increased system of security is not quantifiable.

Allocating integration costs to individual generators, 

whether in the operational or investment time domain, is 

difficult at best, and is something that appears to apply only to 

wind power even though there are other sources of integration 

costs. Energy markets allow for the internalization of many 

costs, and the existence of ancillary service markets helps to 

provide for generally cost-effective provision of these services. 

VII. SUMMARY AND DISCUSSION

Although it is relatively straightforward to calculate total 

system costs under different sets of assumptions with and 

without wind or solar in the generation mix, it is difficult to 

determine integration costs that do not include fuel impacts. It 

is also difficult to allocate integration costs without some 

unintended consequences. The concept of comparability 

requires that integration costs be allocated to all generators 

(and loads) based on performance if they are to be allocated to 

any. 

However, total portfolio costs can be compared without 

arguing over cost allocation. Having a cost impact does provide 

signals for suppliers to provide what is valuable to power 

system operations. If costs cannot be calculated and allocated 

directly, other mechanisms should be found to encourage all 

generators (and loads) to minimize adverse system impacts and 

to provide the flexibility the system needs to lower costs for all 

users. 

ACKNOWLEDGMENT

This research was supported by the U.S. Department of 

Energy under Contract No. DE-AC36-08-GO28308 with the 

National Renewable Energy Laboratory. 

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