Energy Policy 33 (2005) 1661–1675

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doi:10.1016/j.enp

Wind power planning: assessing long-term costs and benefits

Scott Kennedy*

Technology and Development Program, Massachusetts Institute of Technology, 1 Amherst St., MIT, E40-221, Cambridge, MA 02139, USA

Abstract

In the following paper, a new and straightforward technique for estimating the social benefit of large-scale wind power production

is presented. The social benefit is based upon wind power’s energy and capacity services and the avoidance of environmental

damages. The approach uses probabilistic load duration curves to account for the stochastic interaction between wind power

availability, electricity demand, and conventional generator dispatch. The model is applied to potential offshore wind power

development to the south of Long Island, NY. If natural gas combined cycle and integrated gasifier combined cycle (IGCC) are the

alternative generation sources, wind power exhibits a negative social benefit due to its high capacity cost and the relatively low

emissions of these advanced fossil-fuel technologies. Environmental benefits increase significantly if charges for CO2 emissions are

included. Results also reveal a diminishing social benefit as wind power penetration increases. The dependence of wind power

benefits on CO2 charges, and capital costs for wind turbines and IGCC plant is also discussed. The methodology is intended for use

by energy planners in assessing the social benefit of future investments in wind power.

r 2004 Elsevier Ltd. All rights reserved.

Keywords: Wind power; Energy planning; Capacity planning

1. Introduction

Wind power has been one of the fastest growingenergy technologies of the past decade (AWEA, 2002).As the industry itself has grown, the size of individualwind turbines and the number of turbines grouped intosingle developments have also increased. With devel-opers looking further offshore, greater economies ofscale will likely push the rating of individual windturbines into the 3–5MW range with maximum windfarm capacity potentially reaching 1000MW (Milbor-row, 2003b). Wind farms of this scale have the potentialto provide a sizeable percentage of local power demandand generation capacity, particularly when their outputis fed into small or transmission constrained powergrids. To advise future investments in this technology,energy planners need tools that can assess the value ofsuch large wind power installments while also includingthe net impact of wind power on the operation andcapacity requirements of the surrounding power system.Wind power’s value has traditionally been assessed

based on ‘‘the direct savings that would result due [to]the use of wind rather than the most likely alternative’’

2538104; fax: +1-6174522265.

ss: sknndy@mit.edu (S. Kennedy).

front matter r 2004 Elsevier Ltd. All rights reserved.

ol.2004.02.004

(Manwell et al., 2002, p. 443). These savings includeavoided fuel costs, capital costs, and environmentaldamage. The value is therefore not strictly intrinsic tothe wind power technology itself, but rather to thecharacter of the power system in which it operates.Quantifying wind power value is much more difficultthan calculating its cost because the operation of theentire power system must be incorporated into theanalysis. Due to the complexity of modeling an entirepower system, there is much less consensus on standardmethodologies for determining wind power’s value,especially when positive externalities are included, suchas a reduction in environmental damages (e.g., CO2

emissions).The simplest techniques for examining the social

benefit of wind power tend to compare the private costsof wind power generation with the avoided fuel costsand avoided emissions from an average kWh of fossil-fuel generation (Manwell et al., 2002). This type ofanalysis neglects the impact of wind power on powersystem capacity requirements and cannot accuratelydistinguish which type of conventional plant is beingdisplaced by wind power at any given time. As windfarms increase in size, this technique leads to misleadingresults. For example, if wind installments are largeenough, or the grid is small enough, total wind power

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CO2 damage costs

SO2, NOx, and PM10damage costs

Fixed O&M

Installed capital cost

Additional variable O&M

Fuel cost

ENVIRONMENTAL COST

CAPACITY COST

ENERGY COST

TOTAL SOCIAL COST

Fig. 1. Components of the total social cost of electricity generation.

S. Kennedy / Energy Policy 33 (2005) 1661–16751662

output could occasionally exceed the total electricitydemand, and the excess wind power would have zerovalue, far less than what would be assumed by lookingat the average cost of a displaced kWh. While this is anextreme example, it illustrates that the value of largepenetrations of wind power cannot be accuratelydetermined without considering the stochastic interac-tion among wind power availability, electricity demand,and the dispatching of other generators on the powersystem.Production-costing and reliability models, which have

traditionally been used to guide capacity investments(Billinton and Allan, 1996), can be applied to explorewind power’s effect on the reliability and capacityrequirements of conventional generation resources(Milligan and Graham, 1997). These models tend torely on detail at the individual plant level, which canquickly become burdensome when attempting to ac-count for avoided emissions. They are less appropriatewhen assessing the value of wind power in a lessspecified power system. For example, one might want toexamine how wind power fares in a future power systemthat utilizes natural gas combined cycle (NGCC) andintegrated gasifier combined cycle (IGCC) technologies(as will be discussed later), without knowing detailsabout individual plants. The work described herefocuses on resolving this problem through the develop-ment of a generalized technique for assessing windpower’s value, without requiring extensive data at theindividual plant level.In the following paper, a new valuation model that

can assist energy planners in assessing the social benefitof future investments in wind power is described. Socialbenefit is based upon avoided energy, capital, andenvironmental costs due to the replacement of conven-tional power sources with wind generated electricity. Inthe present context, conventional power refers to allgeneration resources except wind power. Section 2introduces the definition of social benefit and themethodology for calculating the social benefit for agiven installment of wind power. The valuation modelutilizes a joint distribution function for electricity loadand wind power availability to determine the expectedconventional output and capacity displaced by wind.Load and capacity requirements are allocated amongthe conventional generators such that a specifiedreliability level is met and long-run average costs areminimized. Due to the latter condition, the system isconsidered to be at long-run equilibrium. The modelallows for the variation of wind and conventionalgenerator technology and cost, wind power penetration,and environmental damage costs.In Section 3 the valuation model is applied to a case

study of potential offshore wind power developmentsouth of Long Island, NY. Results are discussed for fourcases: (1) 2500MW of wind power, (2) variation of wind

power penetration, (3) joint variation of wind powercapital cost and CO2 damage costs, and (4) low and highcost estimates for IGCC plant. Section 4 summarizes theresults of the case study and posits some implications forthe planning of future wind power development.In the case study and the following discussion, fixed,

variable, and environmental costs for all technologiesare determined exogenously. In considering that fossilfuel combustion can incur a wide scope of external costs,including the replacement costs of natural resources,damage to ecosystem services, and the direct healtheffects of pollution, the values chosen here for environ-mental damage costs are conservative. The effect thatthese estimates have on the case study results and thepeculiarities of the hypothetical wind power develop-ment near Long Island area will be highlighted.

2. Description of valuation model

2.1. Wind power’s social benefit

The total social benefit of a given quantity of installedwind power capacity is determined by the net change inthe total social cost of electricity generation before andafter that capacity is installed. The uniqueness of theapproach developed here is that the total social costincludes energy, reliability, and environmental costs,and in both cases (before and after wind power capacityis installed) the generation mix is assumed to settle to asteady-state long-run economic equilibrium. Because ofthe latter condition, newly installed capacity does notsimply replace the most expensive alternative, but causesa reallocation of capacity among all types of generationsuch that long-run average costs are minimized. Thisreallocation will be illustrated clearly in the case study.The breakdown of the total social cost of electricity

generation is shown in Fig. 1. Sample fixed and variable

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Table 1

Typical fixed and variable costs for advanced fossil-fuel technologies, nuclear power, and offshore wind energy

Generator type Overnight capacity

cost ($/kW)

Fixed O&M

($/kW-year)

Fixed cost

($/MWh)aVariable O&M

($/MWh)

Efficiency (higher

heating value)

Variable cost

($/MWh)b

Pulverized coal steam-electric

plant (PCSE)c1090 16 15.9 2 0.355 12.1

IGCCd 1090 21 16.5 2.1 0.438 10.3

NGCCd 445 16 7.6 1.5 0.541 20.8

Nuclear (PBMR) 1000–2090e 33 16.7–30.8 NA NA 3.8

Offshore wind turbinef 1425–1600 15 23.3–25.9 0 NA 0

Cost estimates for fossil-fuel plants from UNDP (2000).aFixed cost (or rental cost of capacity) is amortized over a plant lifetime of 25 years for thermal plants, and 15 years for wind turbines, at a

discount rate of 10.5%. Fixed cost is not levelized over energy output and requires no assumption of capacity factor.bVariable costs include variable O&M and fuel costs. Coal and natural gas plants based on a fuel cost of $1.00 and $2.90/GJ, respectively.c Includes flue gas desulphurisation.dUses steam-cooled turbines.eLow estimate from Nicholls (1998), high estimate from Kadak (1999).fCapacity cost includes balance of plant costs (e.g., foundations, electric lines, etc.). All operational costs are combined into fixed O&M costs. Cost

estimates from Milborrow (2003a).

Table 2

Emissions and environmental cost for fossil-fuel generator technologies

Generator type SO2 (kg/MWh) NOx (kg/MWh) PM10 (kg/MWh) CO2 (kg/MWh) Environmental cost

($/MWh)a

Pulverized coal steam-electric

plantb0.46 0.87 0.15 870 35.3–57.9

IGCC 0.075 0.082 0.0025 710 2.7–21.1

NGCC 0 0.092 0 330 1.8–10.4

Emissions in kg/MWh from IEA (2002). Cost per kg of pollutant are given in Rabl and Spadaro (2000).aLow end of range does not include CO2 cost and high end uses $95 per ton of carbon.bUtilizes flue gas desulphurization.

S. Kennedy / Energy Policy 33 (2005) 1661–1675 1663

costs for each generation technology and sampleenvironmental damage costs are shown in Tables 1and 2, respectively. The fossil-fuel plants-IGCC, pulver-ized coal steam electric (PCSE), and NGCC—all havenon-zero energy, capacity, and environmental costs. Thenuclear plant has only fixed and variable costs, and windturbines are assumed to incur only a capacity cost. Windturbines may also impose some environmental costs,particularly through their visual impact. Fixed environ-mental costs, which are incurred irrespective of output,have been neglected for all types of generation.

2.1.1. Estimating environmental costs

For the majority of fossil-fuel power plants, the mostsignificant environmental impacts result from the con-sumption of a primary fuel and its conversion intoelectricity (Rabl and Spadaro, 2000). The discussion onavoided environmental impact will therefore focus onlyon the impacts related to plant operations. Hence,displacing energy output will generate an environmentalbenefit while displacing capacity will not. Only damagecosts associated with avoided emissions of PM10, NOx,SO2, and CO2 are examined. The environmental impactfrom power generation is certainly not limited to these

four emissions; toxic metals and liquid and solid wastesare among other important pollutants not beingconsidered here. In addition, the replacement cost ofnon-renewable resources and damages to ecosystemservices from fuel acquisition and plant emissions arealso not considered. With these omissions in mind, thisanalysis provides only a conservative estimate of theenvironmental costs.The cost of each pollutant for different plant types

shown in Table 2 comes from a pollution dispersion andimpact model tested by Curtiss and Rabl (1996), whichassumes a uniform receptor density and depletionvelocity. The model was calibrated for the Long Islandarea by adjusting the parameter for population density.The economic valuation of the impacts utilizes cost dataas estimated by the ExternE (External Costs of Energy)Project of the European Commission (ExternE, 1995,1998).

2.1.2. Long-run equilibrium

A long-run equilibrium in an electricity market isachieved when the entry and exit of different generatortypes into and out of the power market have broughteach individual firm’s economic profit to zero and the

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1 If the horizontal axis of a historical LDC is normalized to range

from 0 to 1 and the horizontal and vertical axes are then switched, the

S. Kennedy / Energy Policy 33 (2005) 1661–16751664

sum of all firms’ average cost is minimized (Stoft, 2002).While the likelihood of an electricity market actuallyoperating at long-run equilibrium may be small, findingthe generation mix with the lowest average cost providesa useful reference point from which one can compare thetotal social cost of electricity generation with andwithout wind power. This analysis therefore follows asteady-state approach as effects such as time lags inconstructing new capacity, variability in fossil-fuelprices, or electricity demand growth, have beenneglected.

2.2. Formula for total social cost

Using the definition given above, the total social costfor electricity generation across an entire power systemcan be found by summing individual costs across allgenerator types. Eq. (1) shows the total social cost SC

for N generator types:

SC ¼XN

i

ðVCi þ ENVCiÞEi þ FCici: ð1Þ

All variables on the right-hand side are specified withrespect to a particular plant type i: The cost variables,VCi; ENVCi; and FCi are variable cost, environmentaldamage cost, and fixed cost, respectively. Ei is a randomvariable representing energy output and ci is a determi-nistic variable for the installed capacity. The energyoutput must be defined over some time period duringwhich the installed capacity of each plant type remainsfixed. An hourly time scale is chosen to capture theinter-hour variations in output required to meet astochastic load. It is important to remember that outputand capacity are specified for a plant type and notindividual plants. While individual plants may vary theiroutput significantly during the course of a single hour,the output across all plants of a common type is likely tobe much steadier. For wind power, studies have shownthat the variance of aggregate wind power output overthe course of an hour decreases significantly as thenumber of wind turbines increases and geographicexpanse broadens (Czisch and Ernst, 2001).The main challenge in calculating the total social cost

is determining the energy output and installed capacityof each plant type. As Ei is a random variable, anexpected value for the social cost will be found by usingthe expected value of energy output, or %ei: The processfor calculating both %ei and ci is described in thefollowing section.

2.3. Determining %ei and ci

In the present section a simple and straightforwardmethod is described to calculate the expected energyoutput and installed capacity of each plant type that

minimize long-run average cost and satisfy reliabilityconstraints. The procedure is most easily describedgraphically through the use of screening curves andprobabilistic load duration curves (LDCs). Before theprocedure is explained, a brief description of these twocurves will be helpful.

2.3.1. Screening curves

Screening curves are a common power industry toolfor comparing the relative economies of different planttypes as a function of their capacity factor. Screeningcurves plot the average cost of maintaining andoperating a generator versus the generator’s capacityfactor. A formula for average capacity cost, ACK ; isgiven below (Stoft, 2002):

ACK ¼ FC þ cfVC: ð2Þ

VC and FC are variable and fixed cost and cf is thecapacity factor. ACK ; FC; and VC; are all expressed in$/MWh. Fixed cost in this context is a capacity rentalcost, amortized over the lifetime of the plant to yield theappropriate units.Fig. 2 shows the screening curves for the generator

types in Table 1 plotted on a common axis. The highcost estimate of nuclear power is shown here (Kadak,1999). NGCC is the cheapest option for capacity factorsless than 85% and IGCC should operate at capacityfactors higher than 85%. Even though PCSE has alower fixed cost than IGCC, it is more expensive tooperate at capacity factors greater than 35% because ofa higher variable cost. It should be noted that fairlyoptimistic cost estimates are used for IGCC, whichassume the use of steam-cooled turbines that yieldhigher efficiencies than conventional air-cooled turbines(UNDP, 2000). Also, environmental costs have not yetbeen internalized. If higher cost estimates for coal-firedtechnology are used or environmental costs are inter-nalized, then NGCC would be the only economicallyviable option. To maintain a mix of conventionalgenerator options, low-end estimates for IGCC arechosen and environmental costs are calculated later aspart of the total social cost.

2.3.2. Probabilistic versus historical LDCs

A LDC is used to graphically illustrate the variationof electricity load over a defined period of time, typicallyhourly load values spanning 1 year. In the present paper,a historical LDC is defined as an LDC constructed froma sample of observed load values. Assuming that theload (in this case, hourly) can be represented by arandom variable with a known distribution, a probabil-istic LDC is generated from the load’s cumulativedistribution function.1 A probabilistic LDC has an

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 15

10

15

20

25

30

35

Capacity Factor [%]

Ave

rage

Cap

acity

Cos

t [$/

MW

h]

NGCCPCSEIGCCNuclear

Fig. 2. Screening curves plot the average cost of capacity ACK versus capacity factor cf for NGCC, pulverized coal steam-electric plant (PCSE), coal

integrated gasifies combined cycle (IGCC), and pebble bed modular nuclear reactor. Intersections reveal boundaries between optimal ranges of

capacity factors for each generator type.

S. Kennedy / Energy Policy 33 (2005) 1661–1675 1665

advantage over a historical LDC in possessing well-defined tails. For either type of LDC, it will beconvenient in the following discussion to alwaysnormalize the horizontal axis to range from 0 to 1.For convenience, LDC will refer to a probabilistic LDCunless otherwise noted.

2.3.3. Combining screening curves and LDC

As explained above, screening curves determine anappropriate capacity factor for each plant type, while anLDC illustrates the variation of hourly load. In thepresent section, the two plots are combined togetherfollowing a method described by Stoft (2002) todetermine the portion of the total load satisfied by eachplant type and their corresponding required capacities.Fig. 3 shows a screening curve plotted on top of a

probabilistic LDC. The capacity factor ranges forNGCC and IGCC can be traced onto the LDC todetermine what portion of the load should be satisfiedby each generator type. All load occurring more than85% of the time is satisfied by IGCC, while all load witha shorter duration is satisfied by NGCC. For IGCC thiscorresponds to an installed capacity of c2; while NGCC

(footnote continued)

resulting plot approximates the complementary cumulative distribu-

tion function. The reverse process can be used to generate an LDC

from the load’s distribution function (Kennedy, 2003).

must cover the remaining capacity requirement, c1:Because the LDC in Fig. 3 is a probabilistic LDC, thereis a non-zero probability that the load will reach anarbitrarily large magnitude. Yet, it would be impracticalto install generation capacity to supply loads occurringwith a very small probability. We can set a ‘‘cap’’ on theprobabilistic LDC; above the cap all load will remainunmet. The magnitude of this cap is the total installedcapacity requirement, which is discussed in the nextsection.Both c1 and c2 represent firm capacity requirements,

i.e., the installed capacity must be available whenever itis needed. In actual operation, both plants experienceforced and unforced outages and are not available 100%of the time. However, we are eventually interested in thenet difference in energy, capacity, and environmentalcosts after contributions from wind power have beenincluded. Extra costs associated with these outages willpartially cancel each other out, and are assumed to havea minor impact on the final results.

2.3.4. Total installed capacity

The total required installed capacity can be deter-mined using the distribution function of the hourly loadand a specified loss of load probability (LOLP).2 If the

2For a definition of the LOLP and detailed discussion of power

system reliability, refer to Billinton (1996).

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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 15

10

15

20

25

30

35

Capacity Factor [%]

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Load[M

W]

Duration

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Duration

5

4

3

2

1

0

Load [G

W]

10

5

15

20

25

30

35

Ave

rage C

apaci

ty C

ost

[$/M

Wh]

c2

c1

1

2

e

e

Fig. 3. Using screening curves and a probabilistic LDC to determine each generator type’s installed capacity ci and expected energy output %ei for a

long-run equilibrium. The area denoted by %e1 is the energy generated by NGCC and %e2 is for IGCC.

S. Kennedy / Energy Policy 33 (2005) 1661–16751666

distribution function of the hourly load L is known,then the relationship between the LOLP and the load’sdistribution can be expressed as follows (Ferreira andCarvalho, 1999):

LOLP ¼ PðLXctotÞ ¼ F�L ðctotÞ: ð3Þ

Here F�L ðctotÞ is the complementary cumulative

distribution function for L and ctot is a deterministicvariable representing total firm capacity, or capacitythat is available, but not necessarily operating, 100% ofthe time.Let a represent an acceptable value for the LOLP. If

the total load will be met 99.99% of the time, then a ¼1� 10�4: Taking the inverse of the complementarycumulative distribution function for L (which isequivalent to the probabilistic LDC), the total requiredinstalled capacity necessary to satisfy a is:

#ctot ¼ F��1L ðaÞ: ð4Þ

The required firm capacity is now simply the value ofthe probabilistic LDC at a:

2.3.5. Energy production for each generator type

The area underneath a LDC represents the expectedvalue of the energy demanded per hour, or equivalentlythe expected average load during that hour (Kennedy,2003). The LDC in Fig. 3 is divided into two regions,one for the energy supplied by IGCC ð%e2Þ and one forenergy supplied by NGCC ð%e1Þ: A triangular portion of

area below the LDC and above the maximum capacityc1 þ c2 ¼ #ctot; which is too small to be see in the currentfigure, represents the portion of load occurring withprobability less than a; which will remain unmet.The expected energy output and installed capacity, %ei

and ci; have now been determined without any installedwind power. Performing the same calculation with windpower installed connected to the power system isdescribed next.

2.3.6. Including wind power generation

With wind power installed on the power system, theexpected output and installed capacity of the conven-tional plant types are found using an identical procedureexcept that the LDC is replaced by a residual loadduration curve (RLDC). The residual load is the totalhourly load subtracted by the simultaneous wind poweroutput. If W is hourly wind power output, then theresidual load, R; is simply

R ¼ L � W : ð5Þ

Because both L and W are random variables that mayexhibit some correlation, finding the distribution of R isnon-trivial (Kennedy, 2003). For the present analysis,we assume that the distribution function for the residualload is known. An example of an LDC and RLDCplotted on a common axis is shown in the results of thecase study in Fig. 6.

ARTICLE IN PRESSS. Kennedy / Energy Policy 33 (2005) 1661–1675 1667

It may be possible for the wind power production toexceed the system load, which is shown by the negativevalue of the tail end of the RLDC. The economic valueof this ‘‘dump load’’ may be the price received forexporting power outside of the local power zone or foran alternative and deferrable use of power such as waterpumping or chemical production. In the presentanalysis, all dump load is assumed to have zero value.

2.4. Social benefit calculation

With the expected energy output and installedcapacities of each plant type known, the expected socialbenefit can know be calculated as the difference in thetotal social cost with and without wind power. If %ei andci represent the expected energy output and installedcapacity without wind, and %eW

i and cWi represent the

expected energy production and capacity with windpower, then the expected social benefit of wind poweroperating over a time horizon of t hours, can be writtenas:

SB ¼ tXN

i¼1

ðVCi þ ENVCiÞð%ei � %eWi Þ þ FCiðci � cW

i Þ: ð6Þ

Here, the last generator type, i ¼ N; is for windpower, and %eN ¼ 0 and cN ¼ 0:

3. Model results and discussion

In the present section the wind power valuation modelis used to determine the expected social benefit of ahypothetical 2500MW offshore wind power develop-ment south of Long Island, NY. In subsequent cases,the penetration level, CO2 costs, wind power capitalcost, and IGGC capital cost are all varied to determinetheir impact on the social benefit. The data used togenerate joint distribution functions for wind poweroutput and electric load are introduced below. Theprocedure for generating the distribution functions isdescribed more fully by Kennedy (2003).

3.1. Data

Wind speed observation data was acquired from buoystation 44025 operated by the National Data BuoyCenter (NDBC). The buoy is located approximately 33NM south of Islip, NY.3 Historical data from the buoywas used to estimate a wind speed simulation modelwith a mean wind speed value of 8.62m/s and standarddeviation of 4.30m/s. Hourly wind speed values were

3Wind speed data is available online at http://www.ndbc.noaa.gov/.

Gaps have been filled and all values have been extrapolated from a

measurement height of 5m to a turbine hub height of 65m using a

shear exponent of 0.1.

then generated and converted into wind power outputusing manufacturer supplied power curves from aNordex N80/2500 kW wind turbine (Nordex, 2002).Individual wind turbine output was scaled up toestimate aggregate output while accounting for thespatial smoothing effects of geographically dispersewind turbines; array losses due to turbine interferencewere neglected.Fig. 4 shows the areas for offshore wind power

development south of Long Island, NY as estimated bya recent study (AWS Scientific, 2002). Additionaldevelopment could occur within 4.8 km of the shorelineat water depths of less than 15m, but the proximity tothe coast will likely lengthen the permitting process.Hourly averaged load data from the Long Island load

zone in 2001 was acquired from the New YorkIndependent System Operator (NYISO) for estimatingthe load model and generating the load distributionfunction.4 The Long Island load zone includes bothSuffolk County and Nassau County. The load profile isillustrated by the LDC in Fig. 6.

3.2. Case 1—2500 MW installed wind power capacity

For the first scenario, the expected social benefit for2500MW of installed wind power capacity is calculated.All of the output is fed into the Long Island load zone.Any output that exceeds the zonal load (‘‘dump load’’)has zero value. At 2.5MW per turbine, this capacitylevel requires the installation of 1000 WTG’s; asignificant development. In Section 3.3, the change inbenefits for smaller and larger amounts of wind powercapacity is examined. The two options for conventionalgeneration are NGCC and IGCC, also referred to aspeaking and baseload capacity, respectively.

3.2.1. Dispatch and capacity requirements

To determine the social benefit, the conventionalgeneration capacity and output required to satisfy thezonal load is calculated before and after installing2500MW of wind power. After the wind powerinstallment, IGCC capacity shrinks from 1792 to444MW and the expected hourly output decreases from1710 to 398MWh. For NGCC the installed capacityincreases from 2998 to 3831MW and expected hourlyoutput increases from 653 to 988MWh.The capacity and expected output of each plant type

are illustrated by the LDCs in Fig. 5. The top plot showsa LDC without any wind power installed. The areaunderneath the curve is divided into two sections,representing the expected hourly demand met by eitherNGCC or IGCC. The capacity for each plant typeequals its span along the vertical axis. In the bottomplot, the ‘‘wind’’ area is the expected hourly output from

4Load data is available online at http://mis.nyiso.com/public/.

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Fig. 4. Areas where the average wind speed is greater than 8m/s at 65m, the water depth is less than 30m, and that are outside the state’s 4.8 km limit

and shipping lanes (AWS Scientific, 2002).

S. Kennedy / Energy Policy 33 (2005) 1661–16751668

wind power. With wind power installed, NGCC andIGCC plants only need to meet the residual load. TheRLDC is the line that forms a boundary below the windand dump areas. The dump load is the wind poweroutput that exceeds the zonal load.A comparison of these two plots reveals the influence

of wind power on the requirements for peaking andbaseload capacity. The RLDC in the bottom plot issteeper than the original LDC, implying that a higherpercentage of the residual load occurs over a shorterduration. The residual load therefore requires a higherproportion of peaking capacity compared to the originalload; NGCC capacity and output increase, while IGCCcapacity and output decrease.It is inaccurate to state simply that wind power

replaces either baseload or peaking capacity. As shownby the two plots, the wind power contribution alters theshape of the RLDC, necessitating a reallocation ofcapacity types to settle at a new long-run equilibrium. Inthe present case, not only does wind power replaceIGCC capacity, but it also causes a shift from IGCC toNGCC capacity. This shift has significant implicationsfor wind power’s social benefit, particularly with regardto CO2 emissions, as will be shown later.

3.2.2. Wind power capacity credit

Wind power’s capacity credit can be determined bythe difference between the amount of firm conventionalcapacity required with and without wind power.Graphically, this is illustrated in Fig. 6 as the differencebetween the vertical-axis intercepts of the LDC andRLDC. Both curves have been ‘‘capped’’ (see Section2.3.4) so that all loads occurring with a probability lessthan 1� 10�4 will be unmet. If the load must be met99.99% of the time, then the 2500MW wind installmentprovides a capacity credit of 450MW. Expressed as a

percentage of the rated capacity for the 2500MWdevelopment, the capacity credit is 18%.

3.2.3. Social costs and net social benefit

Wind power’s social benefit can now be calculated asthe difference in the total social cost with and withoutany wind power. The bar plots in Fig. 7 show expectedhourly social costs broken down into four categories:energy, capacity, environmental, and CO2 costs. Non-CO2 environmental costs represent the damage costs ofPM10, SO2, and NOx alone. The social benefit per houris the difference in the last two columns of total cost, or�$12,365. A more meaningful representation is foundby dividing the hourly social benefit by the expectedhourly demand. In this case, the social benefit �$5.35/MWh. This analysis examines changes in socialcost, but makes no assumption of how electricity ispriced. Therefore, we cannot determine whether con-sumers, producers, or both suffer from the decrease insocial benefit due to a 2500MW installment of windpower.The largest positive contribution to wind power’s

social benefit comes from the reduction in CO2 charges,which in this case are calculated at $95/tonC. Theenvironmental benefit due to reductions of PM10, SO2,and NOx is much smaller because the advanced fossil-fuel technologies examined in this model emit only smalllevels of these pollutants (see Table 2). For a powersystem with advanced fossil-fuel technologies, the costper ton of CO2 emissions is therefore one of the mostimportant parameters in determining wind power’ssocial benefit.While it is useful to have a precise measure of wind

power’s social benefit, this value depends significantlyon the parameters chosen for a particular case study. Itwill be more informative in the following sections toexamine how this measure of wind power’s value

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Fig. 5. LDCs with shaded areas illustrating expected energy served by each capacity group. (a) Without any installed wind power, NGCC and IGCC

generate 653 and 1710MWh, respectively. (b) With 2500MW of wind power capacity installed, NGCC and IGCC now generate 988 and 398MWh,

respectively, and wind provides 992MWh. Wind power produces an expected excess of 15MWh (shown as the dump load).

S. Kennedy / Energy Policy 33 (2005) 1661–1675 1669

changes with different parameter estimates. In the nextsection, we discuss how wind power’s social benefitdecreases as more wind power is installed.

3.3. Case 2—sensitivity to penetration

In this section, the dependence of wind power benefitson penetration level is examined. In the followingdiscussion, wind power penetration refers to the ratedcapacity of wind power divided by the total ratedcapacity of all generation resources. In the previousexample, 2500MW of wind, 3831MW of NGCC, and

444MW of IGCC correspond to a wind powerpenetration of approximately 37%.In Fig. 8, the energy output and installed capacity for

each generation type is shown as a function of windpower penetration. As the penetration rises from 0% to44%, the curves show an increase in capacity and outputfor NGCC and a decrease in capacity and output forIGCC. Wind power not only replaces IGCC capacityand output, but also encourages a switch from IGCC toNGCC capacity. An increase in peaking capacity and areduction in baseload capacity are required to satisfy theresidual load at the lowest long-run average cost.

ARTICLE IN PRESS

Fig. 6. LDC and RLDC for 2.5GW of installed wind power capacity. Inset shows loads with a probability less than 0.001. Capacity credit is the

difference between the vertical-axis intercepts of the two curves, which is approximately 450MW.

0.00

20.00

40.00

60.00

80.00

100.00

120.00

140.00

160.00

Wind

NG

Coal

8.47

0.00

16.93

25.40

33.87

42.33

50.80

59.27

67.73

Exp

ecte

d co

st p

er h

our

(tho

usan

ds o

f do

llar

s)

Nor

mal

ized

cos

t ($

/MW

h of

tota

l loa

d)

Energy

Cost

Capacity

Cost

Environmental Cost (no CO2)

CO2

Cost

Total

Cost

No wind No wind No wind No wind No windWind Wind Wind Wind Wind

Fig. 7. Expected hourly producer cost for 2.5GW of installed wind power capacity with ‘‘advanced’’ fossil-fuel technologies as conventional

generation. Installed conventional capacity without wind power is 3.00GW NGCC and 1.73GW IGCC; with wind power it is 3.83GW NGCC and

0.444GW IGCC. Introduction of wind power capacity has reduced IGCC capacity and increased NGCC capacity. Reductions in energy and

environmental costs are more than offset by an increase in capacity cost, resulting in a negative net social benefit for wind power.

S. Kennedy / Energy Policy 33 (2005) 1661–16751670

Above 44% penetration, it is no longer economical tohave any IGCC capacity. Additional installments ofwind power can only displace NGCC capacity andoutput. This threshold penetration at which IGCC is

displaced completely is important in terms of windpower’s social benefit. Without any IGCC capacity,wind no longer obtains the substantial benefit ofavoided CO2 emissions from IGCC output. As is

ARTICLE IN PRESS

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.80

0.5

1

1.5

2

2.5

3

Penetration

Exp

ecte

d en

ergy

out

put [

GW

h] IGCCNGCCWindDump

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.80

2

4

6

8

Penetration

Inst

alle

d ca

paci

ty [G

W]

IGCCNGCCWind

(a)

(b)

Fig. 8. (a) Expected hourly energy output, and (b) installed capacity for each generator type. The top plot also shows the excess wind power

produced (i.e., dump load).

Fig. 9. Benefits (in dollars per MWh of demand) as a function of wind power penetration. Social benefit is the sum of energy, capacity,

environmental, and CO2 benefits.

S. Kennedy / Energy Policy 33 (2005) 1661–1675 1671

illustrated in Fig. 9, the marginal social benefit ofadditional wind power development drops off rapidlyabove 44% penetration.All four categories of benefits and their sum, the

social benefit, are shown in Fig. 9. Benefits represent thechange in producer cost for a given category (energy,capacity, or environmental) due to wind power. They

are given in terms of dollars per MWh of aggregateelectricity demand. A positive benefit implies that thetotal social cost decreases after wind power is installed.Based on the conventional generation costs chosen here,Fig. 9 illustrates that wind power’s social benefit is neverpositive and exhibits a diminishing social benefit withincreasing penetration. The kink in the social benefit

ARTICLE IN PRESS

Fig. 10. Wind power producer cost and conventional generation savings due to wind power production. The cost of wind increases with installed

capacity faster than the savings in conventional generation. This disparity results in the diminishing social benefit of wind power.

S. Kennedy / Energy Policy 33 (2005) 1661–16751672

curve at 44% penetration results from the lowerenvironmental benefit associated with replacing NGCCand opposed to IGCC output.The uppermost curve for the CO2 benefit confirms

that this component is the largest contributor to thesocial benefit over all penetration levels. The CO2 benefitis followed by the energy benefit and the otherenvironmental benefits in terms of their contributionto the social benefit. Because wind power has such ahigh capital cost, the capacity benefit is negative over thewhole range.The shapes of the benefit curves in Fig. 9 are

dependent upon the energy, capacity, and environmen-tal costs of each generator type. As we will see in laterexamples, these curves can change significantly withdifferent estimates of capital costs or charges for CO2

emissions.The diminishing social benefit of wind power arises

because wind power costs increase more rapidly than thedisplaced costs of conventional generation. Fig. 10 plotsthese two series on the same axes. Fig. 10 also clearlyillustrates the implication of exceeding 44% penetration;the displaced conventional generator costs, or conven-tional generator savings, begin to diverge rapidly fromthe wind power costs after all IGCC has been displaced.

3.4. Case 3—joint sensitivity to CO2 and wind power

capital cost

The damage cost for CO2 emissions and the windpower capital cost are now varied simultaneously todetermine their impact on wind power’s social benefit.

In Fig. 11, the parameter space is divided into tworegions, corresponding to either positive or negativesocial benefit. The boundary between the regions isshown for a penetration of 5% and 44%. The vertical-axis intercept of either curve shows the maximum windpower capital cost that will achieve a positive socialbenefit with a zero CO2 emissions cost; $540/kW and$520/kW is required for 5% and 44% penetration,respectively. These costs are less than 40% of currentestimates for offshore capital costs and are unlikely tobe achieved anytime soon. Therefore, providing somekind of CO2 credit for wind power production at thissite is necessary to compete against these cost estimatesfor IGCC and NGCC.The boundaries for either penetration level begin to

diverge at higher CO2 costs because more CO2 isdisplaced per unit of installed wind power at 44%penetration than at 5% penetration. The higher CO2

displacement results from a conversion of IGCC toNGCC capacity. This conversion is greater at 44%penetration because the larger contribution from windpower results in a more steeply sloping RLDC.

3.5. Case 4—sensitivity to IGCC capital cost

In all previous examples, the installed capital cost ofIGCC was set to $1090/kW. This estimate assumes theuse of steam-cooled turbine blades, which have a higherefficiency and lower capital cost than systems with air-cooled turbine blades (UNDP, 2000). If air-cooledturbine blades, which are currently commercially avail-able, are used instead, the capital cost for IGCC

ARTICLE IN PRESS

Fig. 11. Boundary between positive and negative social benefit for a range of wind power capital costs and CO2 charges. Results are shown for 5%

penetration (250MW of wind power) and 44% penetration (3250MW of wind power).

S. Kennedy / Energy Policy 33 (2005) 1661–1675 1673

increases to approximately $1320/kW (UNDP, 2000).Increasing the capital cost of IGCC has a significantimpact on wind power’s social benefit. If the capital costrises above $1185/kW, then IGCC is no longer thecheapest option at any capacity factor and conventionalgeneration is supplied solely by NGCC and PCSE.PCSE has a much more significant environmentalimpact than IGCC. In terms of SO2, NOx, and PM10,PCSE has approximately 6, 11, and 60 times theemissions rate of IGCC (see Table 2). The non-CO2

environmental benefit of wind power increases substan-tially when PCSE is used as opposed to IGCC. Fig. 12illustrates that wind power has a positive social benefitfor all penetrations under approximately 63% when thecapital cost of IGCC is greater than $1185/kW.

4. Conclusion

In the present paper, a valuation model that assessesthe social benefit of wind power has been introduced.The model utilizes probabilistic LDCs to calculate thecapacity and expected energy output of conventionalplant types both with and without a fixed installment ofwind power. A limited approximation of environmentaldamage costs are added to private fixed and variablecosts to determine the social cost of electricity genera-tion. The social benefit is then determined by the netdifference in the social cost before and after wind poweris installed.The analysis follows a steady-state approach whereby

capacity and energy output are allocated among

conventional generators such that long-run averagecosts are minimized and reliability constraints aresatisfied. We show that it is erroneous to assume thatan installment of wind power simply displaces one planttype or another. Instead, wind power encourages areallocation of capacity and output among the differentgenerator options to settle at a new long-run equili-brium. The valuation model has an important advan-tage of requiring a relatively small amount of data,which allows for considerable flexibility in the combina-tion of technologies that can be tested.

4.1. Summary of results

The valuation model has been applied to a hypothe-tical power system that utilizes NGCC, IGCC, andPCSE for conventional generation and 2.5MW Nordexwind turbines located off the southern shore of LongIsland, NY. Aggregate generation satisfies an hourlydemand profile generated from historical Long Islandload data. When compared to NGCC and fairlyoptimistic capital cost estimates of IGCC, wind power’ssocial benefit is negative and decreases with increasingpenetration. With the cost of carbon set to $95/tonC, theavoidance of CO2 emissions provides the largestcontribution to wind power’s social benefit. Thesignificance of CO2 emissions results in an importantdependence on whether IGCC or NGCC capacity isreduced by a wind installment. At low penetrations,IGCC capacity is displaced, while above a certainthreshold, only NGCC remains to be displaced, yieldinga much lower social benefit for wind.

ARTICLE IN PRESS

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8-70

-60

-50

-40

-30

-20

-10

0

10

20

30

Penetration

Ben

efit

[$/M

Wh]

Energy BenefitCapacity BenefitEnvironmental Benefit (No CO

2)

CO2 Benefit

Social Benefit

Fig. 12. Social benefit versus penetration for IGCC capital cost greater than $1185/kW. At this higher IGCC cost, conventional generation is

supplied solely by NGCC and PCSE. Note the significant non-CO2 environmental benefit relative to all other cases.

S. Kennedy / Energy Policy 33 (2005) 1661–16751674

In the present case, wind power exhibits a negativesocial benefit for two salient reasons. First, wind poweroutput is non-dispatchable and replaces only a limitedamount of conventional capacity, and second, therelatively low level of SO2, NOx, and PM10 emissionsgenerated by NGCC and IGCC plants reduces windpower’s environmental benefits. A positive social benefitcan be achieved at very low wind power capital costs,high environmental costs, or a combination of the two.The choice of conventional generation technologiesobviously plays a critical role in determining windpower’s environmental benefit. In most scenarios, IGCCand NGCC were the two plant types compared withwind. However, upon using a slightly higher estimate ofthe capital cost for IGCC, we found that PCSE becomesthe cheapest option for baseload capacity. The muchhigher emission rates for PCSE led to a positive socialbenefit for wind power at penetrations below 66%.Wind power penetration has an important influence

on the social benefit. Two effects contribute to theirinverse relationship. First, wind power becomes lesseffective at replacing conventional capacity because newinstallments do not reduce conventional generator loadduring periods of no wind. Second, above some‘‘threshold penetration’’, all baseload capacity (i.e.,IGCC) has been replaced by either wind power orpeaking capacity (i.e., NGCC). If the baseload capacityhas a higher CO2 emission rate, then above thethreshold penetration the environmental benefit of

additional installments of wind power is reduced,relative to lower penetrations.

4.2. Implications for future development

The present case study has provided a fairly idealizedexample and it is important to emphasize characteristicsthat may differ from other real world systems. First,wind power is only compared with three advancedfossil-fuel options (NGCC, IGCC, and PCSE) that havesignificantly lower emissions levels than most plants incurrent use. Wind power deployed in most present daypower systems, with a diversity of old and new fossil-fuel plants, would obtain a greater credit for avoidedemissions. Second, wind power produced in excess of thesystem load could potentially be wheeled into aneighboring system, as opposed to being ‘‘dumped’’ atzero-value. This added value of exported power wouldincrease wind power’s social benefit, but only atpenetrations above 40%, when excess power becomessignificant (see Fig. 8). Third, the analysis does notconsider any overarching policy goals (e.g., fueldiversification, renewable portfolio standards, etc.)which would put additional constraints on the choiceof generation options. Finally, the price of natural gasand coal were assumed constant in both the short- andlong-term. While future work can illuminate the effectsof variable fuel prices on wind power’s social benefit, itis anticipated that an increase in the price of either

ARTICLE IN PRESSS. Kennedy / Energy Policy 33 (2005) 1661–1675 1675

natural gas or coal alone will cause a shift of capacityaway from the more expensive fuel, while an increase inthe cost for both fuels will result in direct increase inwind power’s social benefit.The peculiarities of any real world power system will

influence the magnitude of actual costs and benefits,however, a few important implications for wind powerplanning may still be drawn; particularly in small orconstrained grids where high penetration levels begin toimpact power system planning and operation. Windpower should not be compared with alternative powersources based solely on the private cost of generation.This approach ignores the impact of wind power on thecapacity requirements and dispatching of other plantson the system, which has been shown here to signifi-cantly influence avoided costs. In addition, with limitedaccounting of environmental externalities and at currentfuel prices, wind power does not provide a positivesocial benefit, even with significant reductions ininstalled capital costs (see Fig. 11). Tipping the balancein wind power’s favor depends on how broadly thescope of environmental benefits is defined.

Acknowledgements

The majority of the work presented here wascompleted during my graduate studies at the Divisionof Engineering and Applied Sciences at Harvard Uni-versity, Cambridge, MA. I would therefore like to thankPeter Rogers, Henry Ehrenreich, and John Holdren fortheir guidance and comments and the John and FannieHertz Foundation for their generous financial support.

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