Energy Policy 39 (2011) 5167–5181

Contents lists available at ScienceDirect

Energy Policy

0301-42

doi:10.1

n Corr

E-m

journal homepage: www.elsevier.com/locate/enpol

Examining the financial performance of micro-generation wind projects andthe subsidy effect of feed-in tariffs for urban locations in the United Kingdom

Ryan Walters, Philip R. Walsh n

Ted Rogers School of Management, Ryerson University, 350 Victoria Street, Toronto, Ontario, Canada M5B 2K3

a r t i c l e i n f o

Article history:

Received 18 May 2010

Accepted 26 May 2011Available online 22 June 2011

Keywords:

Wind

Micro-generation

Feed-in tariff

15/$ - see front matter & 2011 Elsevier Ltd. A

016/j.enpol.2011.05.047

esponding author. Tel.: þ1 416 979 5000; fax

ail address: prwalsh@ryerson.ca (P.R. Walsh).

a b s t r a c t

This paper seeks to evaluate the effect of the upcoming 2010 UK Feed-in Tariff (UK FIT) on decentralised

small wind-energy installations at the household and building level in urban locations. It is projected

that the UK FIT will stimulate an unprecedented surge in building-mounted turbine installation. The

tariff amount must stimulate incentive but mitigate the likelihood of distortions in the competitive

electricity market. To analyse these issues, measured energy output from sites in the Warwick Wind

Trials Project (WWTP) is converted into revenue in a net-present-value (NPV) framework for assessing

commercial purchases of small wind systems. Variances in project variables are examined through NPV

simulations using Monte Carlo analysis to capture permutations of small wind-project performance

in the UK—with and without the UK FIT. Our research concludes that the proposed tariff amount of

30.5 p/kWh will not significantly boost the economic attractiveness of mildly selective (WWTP-based)

sites in the UK. Furthermore, the fixed-tariff rate (£/kWh generated) could cause inefficiencies applied

across uneven wind-resource distribution. The results of this study suggest further examination of

policy related to micro-generation, in particular decentralised small wind projects.

& 2011 Elsevier Ltd. All rights reserved.

1. Introduction

In accordance with the EU renewable energy directive, the UKDepartment of Energy and Climate Change set out an aggressiveRenewable Energy Strategy in 2009 suggesting that over 30% ofelectricity should come from renewable sources as compared to5.5% in 2008 (Department of Energy and Climate Change, 2009).The UK government mandates a strong role not only for largerenewable projects, but also for small-scale renewable energy(otherwise known as ‘‘micro-generation’’) at the building andcommunity level. The burgeoning importance of small-scalerenewable energy was first recommended in 2006 in the UK’sMicro-generation Strategy. Testament to the government’s inter-est and support in this is its increasing offers of financialincentives to micro-generators, such as households and commer-cial building operators. According to the British Wind EnergyAssociation (BWEA) the number of building mounted installationsis forecasted (Fig. 1) to grow significantly as a percentage of smallwind systems in 2010 (when the UK FIT is to be implemented).

In the UK, the subsidy for small-scale renewable energygeneration has included capital grants and, most recently, Renew-able Obligation Certificates (ROCs). A key recommendation of the

ll rights reserved.

: þ1 416 979 5266.

Micro-generation Strategy was implementation of additionalpolicy mechanisms to increase deployment in the small-scalesector. After many years operating in Europe – and not withoutcontroversy – the feed-in tariff (FIT)1 is being implemented in theUK (UK FIT herein) to further promote and incentivise small-scalerenewable energy projects.

Within small renewable technology there is a special interestfor small wind applications. This is bolstered by claims thatpurport the UK to have ‘‘the best wind resources in Europe,’’holding as much as 40% of the EU wind capacity (British WindEnergy Association, 2009). Discussion and focus about harnessingthe UK’s wind power are on the rise, particularly involving thecommercial market for small wind. Potential energy yields ofsmall wind are being scrutinised along with the interaction ofprivate investment and government subsidy. On the policy side,there is controversy surrounding the implications of heavilysubsidising renewable energies in a competitive energy market.Experiences of the FIT in the EU demonstrate that, although theincentive shows success in stimulating renewable energy devel-opment, some countries have also experienced negative impacts,such as consumer backlash from higher wholesale electricitycosts (Lesser and Su, 2008).

Purchase behaviour toward renewable energy technology iscomplicated by evidence that rational purchase decisions invol-ving future savings or revenues are rarely made by consumers,although consumer surveys indicate that diffusion of urban small

Growth in Building Mounted Small Wind Systems

05000

1000015000200002500030000

2005Year

Num

ber o

f sm

all w

ind

syst

ems

2006 2007 2008 2009 2010

Freestanding Building Mounted

Fig. 1. Growth in building mounted small wind systems.

Source: BWEA 2009.

R. Walters, P.R. Walsh / Energy Policy 39 (2011) 5167–51815168

wind technology would be enhanced if positive financial benefitscould be observed. This raises an important question for policymakers, ‘‘If the tendency is for consumers to not fully evaluate theeconomic benefit of their purchase on an ex-ante basis then whatare the implications of policies that influence urban buildingowners to purchase small wind technology only to discover on anex-post basis that it was not financially beneficial?’’ One approachto answering that question, and the principal purpose of thispaper, is to undertake an ex-ante evaluation using metrics such asnet present value (NPV) through energy savings as testable waysto gauge the general economic attractiveness of urban small windsystems across the UK, given existing incentive policies.

Effective and efficient subsidy policies for renewable energywill be addressed through an examination of experiences of thefirst EU countries to use the FIT. This research adds to theknowledge base in its evaluation of the application of FIT policyto urban wind micro-generation. Discussion of controversiessurrounding energy policy mechanisms in Europe set the stagefor identifying subsidy inefficiencies that can be observed throughNPV testing of small wind projects under the UK FIT programme.

The NPV testing framework will be established using costs andrevenues of building-mounted small wind installations in urbansites in the UK. Measured data from real wind sites monitored inthe Warwick Wind Trials Project (WWTP) provides fundamentalenergy-generation information about small wind turbines that isthen analysed and prepared for use as revenue in the NPVframework. By converting a study of wind energy output of smallwind turbines from energy analysis into financial metrics, it ispredicted that the financial return will conform to the disappoint-ing results of the energy output measured in the WWTP and theimplications for policy development arising from these results areidentified.

2. Literature review

2.1. Liberalisation of electricity markets

In the context of electricity power networking, the literaturerefers to building micro-generation as distributed or decentra-lised energy. Bayod-Rujula (2009) notes the increasing liberal-isation of energy networks is a trend seen in many countries, andis characterized by an open access to transmission and distribu-tion grids to accommodate distributed energy sources. Hedescribes a shift in the control of energy flows from the tradi-tional energy systems of large central utilities to a more liberal-ised system where ‘‘much smaller amounts of energy areproduced by numerous small, modular energy conversion uni-tsyoften located close to the point of end use’’ (Bayod-Rujula,2009, p. 377). The principal drivers of a distributed energynetwork are technological developments in energy devices andnational commitments to renewable energy. Outlined benefits ofdistributed energy include: avoidance of transmission congestion,

the prospect of substituting expensive infrastructure capital todistributed energy resources (DERs), and the fact that on-siteproduction reduces transmission losses from a central utility.DERs provide new market opportunities for industrial competive-ness, are easier to source locations for (rather than large plants),and improve localisation of resources (Bayod-Rujula, 2009).

The liberalisation of the electricity market offers the contextwithin which individual electricity generation becomes an option.No longer constrained by the system control of the central electricutility, small-scale generators can take a proactive role in gen-erating their own energy supply and selling excess energy back tothe grid. The freedom to install renewable energy systems hasfurther commercialized small-scale generating technology asmanufacturers vie for prospective customers interested in newsources of energy.

2.2. Small wind purchase decisions in a liberalised electricity market

Small wind systems are typically purchased as commercialproducts, and understanding the relative weight of the variouspurchase-decision factors on the part of a prospective customer’s(building owner’s) mind is important. Unfortunately, there are anumber of factors that are involved in the purchasing decision;price, product features, brand, performance, aesthetics, etc. Own-ers of possible urban small wind sites are limited in terms ofinformation associated with their energy consumption patterns(typically monthly statements lacking in adequate load profiledata). While a FIT might provide surety of income, making anoptimal decision about product and price is difficult when thepurchaser is faced with imperfect information related to productperformance requirements (Friedman and Hausker, 1988). Com-plicating the matter is the risk that owners will tend to actirrationally when considering the purchase of renewable energytechnology. Studies have indicated that it is unlikely that mostpotential purchasers will behave rationally and utilize financialmethods to justify their purchase decision (Sauter and Watson,2007).

The irrational purchase behaviour on the part of buildingowners may be further enhanced by policy decisions such asthe UK FIT and capital grants. Surveys of UK households indicatethat an important factor influencing the purchase of renewableenergy technologies such as small wind devices is the initialinvestment cost (Sauter and Watson, 2007). Providing capitalgrants and tariffs for the generated power in excess of the marketprice can encourage the building owner to purchase the technol-ogy even though they may not be able to accurately determinewhether a positive financial benefit will accrue to them over thelife of the product. In addition to the financial incentive, the UKFIT and capital grant for urban small wind technology representquasi-accreditation schemes that can have a significant influenceon the acceptance of the technology and the positive investmentdecision of the UK building owner (Sauter and Watson, 2007).

In addition to energy cost savings, environmental concerns areoften cited as motivators for considering small-scale renewableenergy. Despite being praised for environmental-friendliness, acursory overview of the literature suggests that environmentalbenefits of renewable energies would not factor highly in thepurchase decision.

A report by EcoAlign in 2008, an environmental-attituderesearcher, concluded that citizen concern about the environmentgenerally does not extend where there is a monetary cost (Wimberly,2008). Although this study was conducted on US citizens, a Europeansurvey of financial incentives similarly found that people primarilyweighed their decisions to purchase DERs on economic merit, such aspayback times, despite a strong awareness of environmental issues(Hack, 2006). Financial incentives offered by Germany and Greece to

R. Walters, P.R. Walsh / Energy Policy 39 (2011) 5167–5181 5169

improve the financial returns for consumers were instrumental in thedevelopment of distributed solar energy markets. From an ethicalconsumer-behaviour perspective, researchers at Bournemouth Uni-versity noticed that there is a large gap between those claiming theywould pay a premium for environmentally friendly products andthose who actually do (Bray and Grevett, 2009). Relating specificallyto small wind energy, a recent study by the Energy Savings Trust,commissioned to monitor customer experiences with small windsystems, concluded that ‘‘customer feedback indicates that percep-tion of the technology corresponds closely with technical perfor-mance. Customers were more positive about their wind turbineswhen receiving demonstrable cost and energy savings from theirturbine’’ (Energy Saving Trust, 2009, p. 17).

The diffusion of urban small wind technology in the UK willrely on the positive appeal garnered through ‘‘demonstrable costand energy savings’’. There may be a number of ways in whichcost and energy savings may be demonstrated to the purchaser. Inits simplest form, consumers may see the financial benefit asreduced monthly electricity bills or through more complicatedcalculations of return on investment (NPV) or payback multiples.While the literature suggests that purchasers of small windtechnology would not employ more sophisticated economicevaluation methods such as an NPV calculation in making theirpurchase decision (Sauter and Watson, 2007), the acceptance ofurban small wind generation in the UK ultimately hinges on theproduct’s economic attractiveness to building owners.

2.3. Choice of method to determine economic attractiveness

In order to address the question, ‘‘If the tendency is for consumersto not fully evaluate the economic benefit of their purchase on an ex-ante basis then what are the implications of policies that influenceurban building owners to purchase small wind technology only todiscover on an ex-post basis that it was not financially beneficial?’’,the choice of an ex-ante approach to determining economic attrac-tiveness will provide insight into whether the risk exists that urbansmall wind projects may not provide a financial benefit to thepurchaser. Common methods for evaluating small wind projectsinclude NPV and Payback Period (Kelleher and Ringwood, 2009;Falconett and Nagasaka, 2010) but for the purpose of this paper theauthors have chosen to use the NPV method primarily because thePayback Period method provides no consideration for cash flowsbeyond the payback year and, as opposed to the NPV, provides noindication as to how much the project will improve the capitalposition of the purchaser or how much the project yields over thecost of capital (Brigham and Houston, 2007).

When employing the NPV method, establishing a base dis-count rate and purchase decision (project acceptance) rule forsmall wind projects is required for assessing their attractiveness.The purchase decision rule for the purpose of determining aneconomically attractive project in this paper is thus established asproject NPV40 at a minimum discount rate of 2% (a reasonableestimate of an alternative return on a standard savings accountfor a deposit equal to the investment cost of the small windproject). Greater discount rates were considered and could bejustified, for example relying on a discount rate equal to conven-tional householder loan rates, but a minimal rate was chosen as itprovided a greater likelihood of positive economic return andproject attractiveness while remaining justifiable in terms ofalternative investment returns available to the consumer. In otherwords, if the project is not economically attractive at the chosenminimum discount rate, it certainly will not be at a higherdiscount rate. If the project is economically attractive at thislower discount rate then the flexibility exists to consider moreexpensive forms of financing other than from household savings.

2.4. Financial incentives for generating renewable energy

Several jurisdictions have introduced financial incentive poli-cies that encourage development of renewable energy projects(Wang et al., 2006; Dinica, 2008; Mulder, 2008; Munksgaard andMorthorst, 2008; Couture and Gagnon, 2010). Financial incentivesstimulate demand for renewable energy where there wouldotherwise be none with existing electricity prices (Lesser andSu, 2008). In economic terms, the problem stems from conven-tional electricity wholesale costs being incomplete – maybe evendeceptive – in that they do not account for all external costs, suchas environmental damage. Without internalised costing of allexternalities, renewable energies cannot exploit their chief com-petitive advantage: fewer harmful emissions (Finon andMenanteau, 2003). As a more extreme voice, Herman Scheerstates, ‘‘The big challenge for the renewable energy industry hasbeen to make the cost of clean energy competitive with heavilysubsidised conventional energy’’ (Scheer, 2007, p. 2). By incenti-vising renewable energy deployment, technologies can experi-ence economies of scale and experience learning-curveadvantages (Soderholm and Klaassen, 2007) thereby reducingcosts and becoming competitively affordable, even without theenvironmental valuation benefit.

While a FIT to encourage renewable generation seems, at firstglance, a reasonable solution, it is not without its controversies. Itcan be difficult to attribute mass renewable energy deploymentsolely to the FIT system; there are many other factors influencingrenewable energy uptake within each national context, includingplanning permissions, energy prices, and social and political will(Dinica, 2008). While this clouds the ability to isolate FIT systemsfor empirical tests of effectiveness, potential market implicationscan still be gleaned and discussed.

2.5. The FIT system and related controversies

In the EU there are two ongoing debates about how tooptimally subsidise renewable energy and design a FIT system.One relates to the challenges of setting an efficient tariff amountthat encourages investment and development without causingexcess advantage over the existing market (Mulder, 2008). Theother questions the long-term conflicting goals of competition ina liberalised energy economy and the use of protective subsidiesfor renewable energy, in particular a fixed-rate FIT (Wang et al.,2006).

Germany provides a case where the FIT was successful inreaching high levels of renewable energy installation, but wherethe FIT also caused undercurrents of strife in the electricitymarket. Favourable FIT amounts created competitive distortionsin the electricity markets (Sijm, 2002) where electric utilitieswere forced to purchase energy at too high a premium overgeneration costs. The high costs borne by electric utilities createda cost-diffusion problem in the market. In theory, any cost hikesto support renewable energy can be absorbed by all energy users,reducing it to a negligible increase (Scheer, 2007). However, at theintroduction of the FIT in 1990, wind power in Germany wasstimulated only in certain regions having the highest wind-energy resource. This meant the obligation to purchase the windenergy at a high FIT was disproportionately passed on to utilitiesof the Northern Region in Germany (Wustenhagen and Bilharz,2004). Customers in this region then threatened to switchsuppliers if the utilities passed the cost hike down to end-users.This energy market turmoil culminated in claims that feed-insupport schemes were in conflict with EU rules for state-aid(Meyer, 2003), and has led to many judicial actions by electricutility companies in protest (Sijm, 2002). Subsequent legislation,the Renewable Energy Sources Act (RESA), was introduced in

R. Walters, P.R. Walsh / Energy Policy 39 (2011) 5167–51815170

Germany in 2000 that effectively dealt with these disproportion-ate costs by creating a framework of cost-sharing amongstutilities and a tariff structure that employs an algorithmicdetermination of site-specific tariffs. This latter element provideslower-wind resource projects a higher tariff (when compared to areference site) thereby allowing for a more reasonable projectreturn (Jacobsson and Lauber, 2006; Meyer, 2003). The Germanexample highlights the fact that FITs are subsidies that havedelicate, conflicting interests between those who support thedevelopment of renewable energy and those who ultimately paythe tariff and that FIT policies must be carefully designed to avoidthese conflicts. This problem is summarised as ‘‘too high a FIT willstimulate over-investment, causing too rapid development ofabove-market RETs [Renewable Energy Technologies] and trigger-ing adverse economic impacts owing to higher than expectedelectric rates, as well as customer backlash’’ (Lesser and Su, 2008,p. 984).

Another problem in setting the efficient FIT amount concernsthe rate of spurred development. If the rate is too low, the marketcannot reach production levels that foster economies of scale thatcan reduce costs and stimulate wide-spread diffusion of thetechnology. However, too high a rate reduces financial risk andcan encourage over-investment in, and development of, lessefficient projects. While it is intuitively logical that profit-max-imization tendencies on the part of developers should limitinefficiencies, other factors in the project development processsuch as urgency to market, vendor–client relationship effects andpoor management decisions can mitigate those tendencies. With-out customising the FIT amount to the range of ROIs earned onwind-energy projects, any amount is likely to cause inefficiencies.The problem arises from having a flat, fixed-tariff generation ratethat is indiscriminately applicable to different renewable energygenerators regardless of their productivity or site-specific windendowment. For example, subsidy surpluses can be awarded tothose projects experiencing technical progress (Menanteau et al.,2003) or securing the best wind sites. This problem was high-lighted in Denmark where windfall profits were generated bydevelopers who could secure the best wind locations (Meyer,2003). Certain European jurisdictions (the UK excluded) haverecently sought to remedy problems related to resource discri-mination based on site-specific advantages by introducing initia-tives that adjust the tariff based on performance. Germany’s RESAallows for reduced tariffs if turbines performed in excess of areference facility (Jacobsson and Lauber, 2006) while othercountries such as the Netherlands, Portugal, Denmark, France

Fig. 2. Model for determining tipping point for diffusion of renewable energy. Inde

distribution and transmission.

and Cyprus have implemented stepped tariff designs that providelevels of tariffs based on the local conditions at the plant site(Klein et al., 2008) with reduced tariff support for higher yieldinglocations.

There also exists an incongruity in goals sought by liberalisingelectricity markets and the offering of financial incentives forrenewable energy. Jensen and Skytte (2003) explore the conflictof electricity liberalisation goals in the EU as (a) seeking compe-titive efficiency in energy production to reduce consumer elec-tricity costs, while (b) using the FIT, which has been known toraise retail electricity prices. Liberalisation has opened up com-mercial competitiveness of renewable energy technologies in adecentralised energy network. This can improve electricity mar-kets because ‘‘DG can also stimulate competition in supply;adjusting price via market forces. In a free market environment,a DG operator can buy or sell power to the electricity grid,exporting only at peak demand and purchasing power at off-peakprices’’ (Bayod-Rujula, 2009, p. 380). However, distributed gen-eration technologies currently also require subsidies in order toexist in the market at all. In the short term, it is hoped thatincreased manufacturing levels will create economies of scale andlearning-curve improvements as to spur innovation and techno-logical improvement so that renewable energy technologies willeventually become independently competitive in the energymarket. But subsidies such as the FIT are argued as beingfundamentally at odds with free-market principles (Lesser andSu, 2008; Couture and Gagnon, 2010) because they typically act asa fixed rate relative to the market price. If renewable energyproduction is always protected against market forces it will neverbe forced to compete against lower cost alternatives, limiting thepotential for competition-driven efficiency and technologicaldevelopment. Influenced by subsidy rather than market drivers,renewable energy development runs the risk of eventuallystagnating.

In designing a customised FIT amount, the problem lies injustifying subsidies for technologies in various stages of maturitygiven that technological advancement can be unpredictable. Thatis not to suggest that in all cases FIT programs are, or will be, inexcess of market prices. Evidence from Denmark (Munksgaaardand Morthorst, 2008) highlights how market prices can rise abovethe fixed rate of a FIT in certain circumstances when, for example,more conventional sources of electrical generation, such asnatural gas or oil, have increased in price subsequent to theintroduction of the FIT. The concern lies with FIT policies that mayset a tariff at levels well in excess of market prices contrary to the

pendent of any influence from other factors such as increased costs related to

R. Walters, P.R. Walsh / Energy Policy 39 (2011) 5167–5181 5171

EU’s goal of seeking competitive efficiency in energy productionto reduce consumer electricity costs (Jensen and Skytte, 2003).

Conclusions from the literature on FITs suggest that they areparticularly successful when attempting to create a ‘‘tipping point’’of minimum incentive necessary to catalyse quick deployment ofrenewable energy (Munksgaard and Morthorst, 2008; Couture andGagnon, 2010). However, within this quick deployment, an ‘‘aggres-sively priced’’ tariff may unavoidably produce incentive inequalitiesdue to variation in developer returns arising from regional resourceavailability. In the long term, sustained inflation of renewablevaluation (outside of the free market) could prove to be problematicin a liberalised electricity market seeking to stimulate competitivecommodity pricing and spurring technological innovation in renew-able technologies. Renewable energy incentive policies that seek tointroduce degressive FITs can mitigate the impact of windfall profitsby producers through the reduction in financial support as thetechnology advances and better knowledge of the quality andquantity of the available renewable resource allows for reductionsin capital and operating costs (Gonzalez and Del, 2008; Snyder andKaiser, 2009; Couture and Gagnon, 2010; Valentine, 2010). Further-more, the integration of economic and institutional interests inpromoting wind energy through FIT programs by mutually workingon determining the appropriate ‘‘tipping point’’ (Fig. 2) defined bythe prevailing market electricity price, wind resources, projectcharacteristics, etc. (Han et al., 2009), would increase the prob-ability of successful diffusion of wind energy (Dinica, 2008; Perezand Ramos-Real, 2009).

However, the question remains as to what extent the experiencesderived from FIT policies in other jurisdictions can be applied tourban small wind generation in the UK. An examination of theeconomics of the potential application of this technology given theproposed UK FIT amount is warranted, and suggested refinements toexisting UK FIT policies may be required.

3. Evaluation methodology

A three-step process is undertaken as part of a framework forassessing the commercial attractiveness of urban small windsystems (Fig. 3). This framework is consistent with similar studiesundertaken by Kelleher and Ringwood (2009) in their study ofwind micro-generation in Ireland and Falconett and Nagasaka(2010) who employed this probabilistic methodology in theirreview of small wind projects.

The first step involves the determination of annual energycapacity for a typical small wind project in the United Kingdom.

AnalyzeWarwick WindEnergy Data;

Find average outputand wind variance

(kwhr)

NPVcalculation for

basing purchase decision

1. Data Analysis 2. Commercial A

Wind energyX

Price of electricity= Revenue input

for NPV

Fig. 3. Framework for assessing the economic a

Energy data from the Warwick Wind Trials Project (WWTP) isanalysed and interpreted in preparation for use in year-to-yearproject cash flow analysis. Monthly energy measurements fromsmall wind turbines across a variety of urban sites in the UK areconverted into average yearly output in kWh. Energy yields aregrouped into low, base and high in preparation for Monte Carlovariance simulations and sensitivity analysis (RiskSim). In thesecond step, the annual energy capacity is applied to a series ofprofit/cost variables for use in the NPV modelling. The modelframework calculates NPV using a set of cash inflows and out-flows discounted to the present for combinations of differentenergy output, turbine cost and lifespan. Using the range ofproject variables affecting the NPV of wind sites, individualpurchase decisions are extrapolated to assess the potential forwidespread commercial viability. A Monte Carlo analysis isemployed to run 1000 NPV simulations that capture the spectrumof NPV results arising from different combinations of variables.

The choice of the number of simulations to run was based onMonte Carlo methodologies employed in other scientific andengineering disciplines (Rushton and Lolonis, 1996; Hall andStrutt, 2003; Johnson and Gillingham, 2004; Fischer et al., 2005;Gibbons et al., 2006; Blijenberg, 2007; Damon, 2008; Demariaet al., 2009). The frequency of positive NPV simulations will bethe measure of viability. The same variances can also beexplored through a sensitivity analysis that illuminates theimportance of different project variables in relation to energyoutput (revenue).

Finally, commercial analysis of small wind turbines in thecurrent UK market (without FIT) provides the base-case scenariofrom which to analyse the effect of FIT. Resulting changes in NPV,and the related changes in economic attractiveness, are aggre-gated using another 1000-simulation NPV Monte Carlo.

The efficiency of the UK FIT on building-mounted small windprojects will be analysed three ways:

(1)

naly

ttrac

Observing frequency, distributions and range of NPVs achievedin the Monte Carlo analysis with and without the UK FIT willshow whether certain projects are over or under compensated.

(2)

Determining and comparing a generation tariff amount thatwould be necessary to produce 50% or more of NPV40 (majorityof economic attractive projects) of 1000 NPV simulations.

(3)

Evaluating the sensitivity analyses from the Monte Carlosimulations at different ranges of energy yield (low, baseand high). This will show the circumstances at which govern-ment financial incentive analysis becomes an importantproject variable and thus makes FIT warranted.

Building Owner does not purchase

Building Owner does

purchasesis

Negative

NPV

PositiveNPV

3. Financial IncentiveAnalysis

tiveness of urban small wind systems.

R. Walters, P.R. Walsh / Energy Policy 39 (2011) 5167–51815172

3.1. Variances for Monte Carlo and sensitivity NPV analysis

The Monte Carlo analysis is used to paint a picture of thelikelihood of ROI among a sample population. This investigationcaptures the permutations of variance in the factors affectingprofitability and intends to seek out the frequency of profitablecombinations. As this analysis demands a more accurate ‘‘poten-tial scenario’’ variation, it will account for the reality (as can beseen in the WWTP data) that two sites with identical wind speedsmay generate different energy. To do this, the power curves of thethree yield groupings (least, base and highest kWh) of the WWTPtrialled sites were determined. The trend line of each group of 36data points (monthly kWh versus wind speed across three siteswithin a set) represents the low-, base- and high-potential energyyield from a small wind turbine influenced by any given windspeed (as shown in Fig. 4).

These three power functions provide the base equations fromwhich to calculate Monte Carlo variance of expected annualenergy output (kWh/year) for a site with a certain affecting windspeed. The sensitivity analysis, in contrast to Monte Carlo,attempts to measure the impact of variables on profitability. Thisrequires ranges (rather than actual variance determinants) offactors affecting profitability.

For the energy-output range, results from the nine WWTPtrialled sites were grouped according to low, base and high andaveraged. Regardless of indirect factors (such as wind speed),

kWh/month = 1.7255x2 - 3.3436x + 2.3337

kWh/month = 8.0453x - 11.248

kWh/month = 9.7483x - 12.778

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

0.00Average Wind Speed m/s

Mon

thly

kW

h

1.00 2.00 3.00 4.00 5.00

Least kWhBase kWhMost kWh

Fig. 4. Power curves for monthly kWh generation (sites grouped by kWh

generation: least, base, most).

151.89

158.83

184.68

2.74

2.83

2.99

Deventry Civic Centre

Park Farm

Napier

High kWh

Generated

57.64

61.99

81.48

2.04

2.18

2.22

Daventry County Park

Leicester

Lillington Road

Base kWh

Generated

51.92

53.99

56.08

2.27

2.04

2.30

Birds Hill

Hill Close Gardens

Delta Court

Low kWh

Generated

AnnualEnergy(kWh)

WWTP Average

Wind Speed (m/s)

Site

Fig. 5. Chart of yield categor

electricity output ranges from an average of 54–165 kWh/year, asshown in Fig. 5.

3.2. Commercial analysis

In analysing the financial return of different sites, the NPVframework (Table 1) employs a traditional financial evaluation ofa proposed project with annual cash flows. Employing an Exceladd-in tool called RiskSim, a Monte Carlo analysis is conducted onthe NPV of wind energy sites. This tool runs a user-definednumber of simulations based on variances in the inputs used tocalculate the NPV. By running many simulations, RiskSim cap-tures thousands of what-ifs (combinations of variables) thatreflect the uncertainties surrounding the NPV of small, urbanwind energy projects. Results are presented in histogram chartsand cumulative distribution graphs.

Key wind-project input variances that affect NPV in the simula-tions include turbine operating life, turbine cost and energy yield.From a building owner’s purchase perspective, these variablesconstitute the largest ‘‘risk’’ and NPV variation of purchasing aturbine. The Monte Carlo analysis is employed by running 1000NPV simulations, where each simulation randomly selects energydata from within the WWTP-based spectrum of wind speed andenergy yields. Combined with variance in other project variables, thiscreates a ‘‘microcosm’’ of possible building-mounted small windenergy projects in the UK. Aggregating 1000 individual NPVs(commercial purchases) for different projects allows for the

165.13

67.03

53.99

Average Annual (kWh)

kWh = 9.7483x – 12.778

kWH = 8.0453x – 11.248

kWh = (1.7255x^2) – 3.3436x + 2.3337

Power Curves (kWh/m/s/Month)*

*Annual Energy Output (kWh/year) =

Wind Speed * Power Curve * 12 Months

ies (low, base and high).

Table 1Elements and calculations of the NPV framework.a

NPV project elements Calculation

Discount rate Rate at which cash flows are discounted to

present value (PV). Set at nominal 2%

Project length (turbine

operating life)

Sets the amount of years the small wind

system will generate energy (revenue) and

therefore equals the project life. Turbine

lifespan is 10, 15 or 20 years

Year 0 project cost (negative

cash flow)

Turbine cost installed� (1-capital grant %).

Capital grant is set at 30% as per low carbon

buildings programme

Project revenues [years 1–

(turbine operating life)]

Yearly revenue¼[(energy output

(kWh)� generation tariff)þenergy output

(kWh)]� [electricity cost� electricity cost

increase]

Net present value (NPV) ¼PV (turbine operating life� yearly revenue)–

turbine cost� (1-capital grant %)

a Sources and justifications for electricity cost, electricity cost increase and

turbine costs are included for reference in the appendix.

R. Walters, P.R. Walsh / Energy Policy 39 (2011) 5167–5181 5173

opportunity to comment on widespread commercial viability (i.e.frequency of projects with NPV40 at a 2% discount rate).

Although variances in average wind speed and energy output(captured from the WWTP trials) define the randomness ofannual energy output (and project revenue) that produce differ-ent NPVs for each simulation, this Monte Carlo analysis does notinclude fluctuation randomness in energy yields given that windenergy is not distributed equally throughout the month, day, oreven hour. Depending on when energy is generated, the valuecould change in accordance with tiered electricity pricing andsupply/demand fluctuations which this does not account for.Instead, it is assumed that building owners will use all energyon-site and can store unused electricity (via battery) thus equallymatching supply with demand within a building and ensuring thesame value for all energy generated. Using sample site data fromWWTP to extrapolate to 1000 simulations is justified because trialsites were specifically chosen by the WWTP as representative ofdiverse locations across the UK that hold potential for commercialsuccess of small wind systems (as per the Warwick Wind Trialmethodology). Wind speed and yield ranges captured from theWWTP give a good indication of variances other UK sites couldexperience. Turbine costs and operating life variances are alsoderived from research into turbines used in the WWTP.

3.3. How a simulation is conducted using variances

Site-specific inputs are a mix of fixed numbers and variables.For variables, data from the WWTP form the ranges of averagewind speed and energy yield selected from lower, base and upperbounds according to a random selection method within thatrange (discrete or triangular). The random amounts are carriedthrough the financial model to produce an NPV for that simula-tion. The NPV framework with example simulation numbers andcalculations is shown in the appendix.

3.4. Turbine cost installed and operating life

Based on the analysis of the WWTP results, differences inannual energy yield did not significantly vary between differentmodels of turbine. Cost and durability of turbines, however, dovary with significant impact to the NPV of a purchased small windsystem. Turbines used in the WWTP could be purchased(Appendix—Chart of Turbine Life and Cost) and installed for aslow as £1500, but were only estimated to last for 10 years. Unitscosting £2500 had longer expected operating times. To accountfor this correlation within the model, the cost of turbine waslocked to its related lifespan (project length). Each simulation inthe Monte Carlo analysis randomly selects a cost of turbine as£1500, £2000 or £2500 using a RANDDISCRETE Excel function. Therelated lifespan (project length) is then automatically selectedusing the corollary shown in Table 2.

In the simulation example below, the RANDDISCRETE Excelfunction chose a turbine cost of £1500 (£1050 after the 30%capital grant). For this simulation, the corresponding year

Table 2General range of UK turbine cost and hardware performance.a

Cost of turbine Lifespan (years) Cost used in model (with 30% grant)

£1500 10 £1050

£2000 15 £1400

£2500 20 £1750

a Information about turbine models can be found in the appendix.

0 negative cash flow would be £-1050 and project revenueswould continue until year 10.

Turbine cost installed w/grant

£1,050

Capital grant 30% Turbine operating life 10

Using the range of turbine costþoperating life sampled in theWWTP allows the simulations to capture the fact that purchaserscan choose between these available categories of turbines on themarket. Every turbine used in the WWTP was individuallyselected and commercially purchased by the owners of a site. Itis assumed in this paper that general selection of small windturbines for urban building application would reflect similarpatterns experienced in the WWTP.

3.5. Average wind speed and energy output function

Setting the variance of annual Energy Output (kWh) of smallwind systems in urban sites required two steps. Given that theenergy output of a turbine is a function of its efficiency (powercurve) and influencing wind speed (average annual), these twovariables were captured in the model. First, the complete range ofaverage measured wind speeds found from the WWTP analysiswas assembled: absolute minimum (low), average (base) andabsolute highest (high). In the model, a RANDTRIANGULAR func-tion selects a random number within that range for use as‘‘Average wind speed’’ in a given NPV simulation. The randomlygenerated ‘‘Average wind speed’’ number is then applied to powercurves experienced in the lowest, base and highest performingsites of the WWTP (power curves derived from data contained inthe appendix—Graphed Power Curves Extrapolated from WWTPData). This captures the variance in amount of energy (low, baseor high) a site might generate when influenced by the given windspeed. The ‘‘Energy Output (kWh/year)’’ figure of a simulation isdetermined by a random selection within this range (RAND-TRIANGULAR function). This annual energy-output figure is readyto be used in the yearly project revenue calculation (in combina-tion with the variance in operating life, cost of turbine, and otherfixed project inputs) to simulate the NPV of a small wind project.

3.6. Financial incentive analysis of FIT (generation tariff)

Three different scenarios are tested using the Monte Carloanalysis to assess commercial viability of small wind in urbanbuilding application. Commercial viability is measured as thefrequency of simulations where NPV40 exists for different fixedtariff amounts.

Tests conducted:

1.

1000 NPV simulations with capital grant, but without genera-tion tariff (base case).

2.

1000 NPV simulations where generation tariff¼30.5 p/kWhwith capital grant (proposed for UK).

3.

Level of FIT needed to produce, on average, a positive NPVvalue for an urban UK wind site (arithmetic mean of NPV¼0 of1000 simulations) (economically attractive project).

Following the results of the Monte Carlo analysis, the NPVinputs and variances are evaluated in a sensitivity analysis. As thesensitivity analysis does not attempt to recreate the permutationNPV probability (like Monte Carlo does via simulation), the inputsconcern the minimum–maximum ranges of financial-impactingvariables from the base case where NPV is calculated using

R. Walters, P.R. Walsh / Energy Policy 39 (2011) 5167–51815174

averages of each variance. For example, the wind speed crucial tothe Monte Carlo simulation becomes irrelevant as it is not a directfinancial indicator. The energy range used for sensitivity is theaverage actual kWh of the low, middle and high performing sitesas discussed and determined earlier. Other static variables in theMonte Carlo analysis are given lower and upper ranges thatsignify their actual volatility in the market. For example, thecapital grant in the UK is currently at 30%, which is useful as astatic figure for Monte Carlo simulation (testing scenarios ofcurrent turbine revenue). However, the sensitivity analysis strivesto measure its current impact on the NVP in relation to very-possible levels of grants at 20% or 40%.

Isolating energy yield distribution compares how financiallysensitive the tariff is across different small wind sites (low, baseand high performance). For example, an NPV of �£1021 in thesensitivity analysis model (Fig. 6) represents the expected valueof the base-case scenario of project variables. Sensitivities aremeasured based on the relative changes to the NPV resulting fromdifferent thresholds for each project variable. The results of the

-1021.14NPV

Maximum

5%

2500

20

165.13

0.15

2.72

0.405

40%

Base case

2%

2000

15

67.03

0.15

1.77%

0.305

30%

Minimum

1%

1500

10

53.99

0.15

0.51%

0.205

20%

Input Cells

2%

2000

15

67.03

0.15

1.77%

0.305

30%

Site-specific inputs

Discount rate

Cost of Turbine

Turbine operating life

Energy Output (kWh)

Electricity Costs

Electricity cost increase

Government Incentives

Generation tariff

Capital grant

Fig. 6. Sensitivity analysis model (adapted from Monte Carlo).

NPV Histogram 1000 Trials, Base

020406080

100120140160180200

-1700.00 -1600.00 -1500.00 -1400.00 -1300.00 -120

NP

Freq

uenc

y

Min. NPV = -1600

Fig. 7. Monte Carlo an

NPV Histogram 1000 Trials, Low-High Wind

0

20

40

60

80

100

120

-1500.00 -1400.00 -1300.00 -1200.00 -1100.00 -1000.00 -900.00

NP

Freq

uenc

y

Min. NPV = -1401

Fig. 8. Monte Carlo analys

sensitivity analysis are outputted as one- and two-factor tornadocharts that allow for an impact comparison of the project vari-ables. Energy yields, isolated for low and high performance, arealso outputted to allow for the comparison of project sensitivitiesbetween the performance ranges.

4. Results

Results of 1000 NPV simulations without the UK FIT are shownin Fig. 7. The histogram indicates the NPVs of a sample populationof 1000 small wind projects in current market conditions wherejust the capital grant is applied. The mean NPV is �£1.160, andthe maximum under the best circumstances of project variables is�£790. Given the fact that all projects would not generateenough revenue to cover costs, commercial viability is far fromlikely without further financial incentive. The three concentra-tions of frequency correlate with the three turbine operating life/cost settings. Isolating the turbine cost/life variable revealed thatthe best performing frequency concentration (�£1.000 to �£790range) was the lowest turbine operating life. Without the tariff, anexpensive but long-running turbine never makes up its cost.

Applying the proposed generation tariff of 30.5 p/kWhimproves the commercial prospects, but only slightly (Fig. 8).The mean NPV increases by 32% to �£793 and, interestingly, thedistribution of NPV simulations now follows a normal pattern incontrast to the market without the tariff. This would suggest thatsensitivity to cost and operating life have decreased in impor-tance as a contributing factor to the final NPV. Whereas withoutthe tariff it could be suggested that the highest cost/longestoperating turbine should be avoided, now it is less clear whichvariables hold the highest sensitivity to NPV under the UK FIT.NPV simulations range from �£298 to �£1401 (�£810) repre-senting an increase of 36% from the range without the tariff(�£1103) (Fig. 9). The maximum NPV achieved increases by 62%(to �£298), almost twice as much as the mean NPV increase(32%), showing a larger increase with the highest performing sites

case without Generation Tariff

0.00 -1100.00 -1000.00 -900.00 -800.00 -700.00

V

Mean NPV = -1160

Max. NPV = -790

alysis: base case.

Variance where Generation Tariff = 0.305

-800.00 -700.00 -600.00 -500.00 -400.00 -300.00 -200.00

V

Mean NPV = -793

Max. NPV = -298

is: base caseþUK FIT.

Cumulative Probability Chart

0.00.10.20.30.40.50.60.70.80.91.0

-1500.00 -1400.00 -1300.00 -1200.00 -1100.00 -1000.00 -900.00 -800.00 -700.00 -600.00 -500.00 -400.00 -300.00 -200.00

NPV

Cum

ulat

ive

Pro

babi

lity

Fig. 9. Probabilities of return for base caseþUK FIT.

NPV Histogram 1000 Trials, Low-High Wind Variance where Generation Tariff = 0.935

0

20

40

60

80

100

120

140

-1000.00 -800.00 -600.00 -400.00 -200.00 0.00 200.00 400.00 600.00 800.00 1000.00 1200.00 1400.00

NPV

Freq

uenc

y

Min. NPV =-890

Mean NPV = -10.88

Max. NPV = 1366

Fig. 10. Monte Carlo analysis: base caseþUK FIT of 93.5 p.

Cumulative Probability Chart

0.00.10.20.30.40.50.60.70.80.91.0

-1000.00 -800.00 -600.00 -400.00 -200.00 0.00 200.00 400.00 600.00 800.00 1000.00 1200.00 1400.00

NPV

Cum

ulat

ive

Pro

babi

lity

Fig. 11. Probabilities of return for base caseþUK FIT of 93.5 p.

R. Walters, P.R. Walsh / Energy Policy 39 (2011) 5167–5181 5175

than with the average performing sites. Project input combina-tions analysed for revenue sensitivity will determine if best-combination project inputs (top percentile of NPV simulations)are only accessible to some.

If a building owner had a lower personal discount rate, an NPV of�£400 and higher could possibly be perceived as motivation topurchase. However, even with a lower discount rate, there is only a2% chance of achieving an NPV of �£400 or greater (Fig. 10). Instead,the highest frequency (steepest slope) centres around the �£900 to�£600 range as the average NPV a building owner could expect frompurchasing a small wind system. Although the tariff improves theaverage range of NPVs, widespread commercial deployment wouldappear to be unlikely, given the current inputs.

The required generation tariff amount for achieving a meanNPV of around zero (using a discount rate of 2% p.a.) wouldrequire a generation tariff amount of 93.5 p/kWh, as shown inFig. 11. This tariff would be an unprecedented subsidy amountwell above the proposed FIT (30.5 p) and five times the retail priceof electricity (15 p). While about 50% of projects become econom-ically attractive to potential customers under this tariff (Fig. 11),the range of NPV now skyrockets to £2256, with some projects

remaining relatively costly (�£890) while others enjoy substan-tial profits (þ£1366).

Those at the highest end of profit reward would likely beregarded as reaping unfair compensation at the expense of utilitycustomers, who will bear the higher price of electricity that thehigh tariff amount would cause. This also shows that to stimulatewidespread deployment in this sector (where the average build-ing owner could invest in small wind) there is an inherentinefficiency of windfall profits reaped by the generators withthe best combinations of project inputs.

4.1. Sensitivity analysis results

The sensitivity analysis of the Monte Carlo testing produces aone-factor tornado chart (Fig. 12) where the average of windspeed, energy yield and cost of turbine are used to calculate thebase-case NPV. The cost of the turbine is the most sensitivevariable, which comes as no surprise given the front-loadednature of renewable energy technology costs. However, it doesshow that the generation tariff is overshadowed by other projectinputs (such as capital grants and turbine operating life).

0.15

0.51%

5%

0.205

10

20%

53.99

2500

0.15

2.72%

1%

0.405

20

40%

165.13

1500

-1500.00

-1400.00

-1300.00

-1200.00

-1100.00

-1000.00

-900.00

-800.00

-700.00

-600.00

-500.00

-400.00

-300.00

Cost of Turbine

Energy Output (kWh)

Capital grant

Turbine operating life

Generation tariff

Discount rate

Electricity cost increase

Electricity Costs

Government Incentives

NPV

Fig. 12. One factor sensitivity analysis.

0.15 & 00.51% & 0

0.15 & 0.51%5% & 0

5% & 0.155% & 0.51%

0 & 0.2050.15 & 0.205

0.51% & 0.20510 & 0

10 & 0.1510 & 0.51%5% & 0.205

5% & 1010 & 0.2050 & 20%

0.15 & 20%0.51% & 20%

5% & 20%0.205 & 20%

10 & 20%53.99 & 0

53.99 & 0.1553.99 & 0.51%

2500 & 02500 & 0.15

2500 & 0.51%5% & 2500

5% & 53.992500 & 0.205

53.99 & 0.2052500 & 10

10 & 53.9953.99 & 20%

2500 & 20%2500 & 53.99

0.15 & 02.72% & 00.15 & 2.72%

1% & 01% & 0.151% & 2.72%0 & 0.4050.15 & 0.4052.72% & 0.40520 & 020 & 0.1520 & 2.72%1% & 0.405

1% & 2020 & 0.4050 & 40%0.15 & 40%2.72% & 40%

1% & 40%0.405 & 40%20 & 40%

165.13 & 0165.13 & 0.15165.13 & 2.72%

1500 & 01500 & 0.151500 & 2.72%

1% & 15001% & 165.13

1500 & 0.405165.13 & 0.405

1500 & 2020 & 165.13

165.13 & 40%1500 & 40%

1500 & 165.13

-2200.00

-2000.00

-1800.00

-1600.00

-1400.00

-1200.00

-1000.00

-800.00

-600.00

-400.00

-200.00

0.00 200.00

400.00

Cost of Turbine & Energy Output (kWh)Cost of Turbine & Capital grant

Energy Output (kWh) & Capital grantTurbine operating life & Energy Output (kWh)

Cost of Turbine & Turbine operating lifeEnergy Output (kWh) & Generation tariff

Cost of Turbine & Generation tariff Discount rate & Energy Output (kWh)

Discount rate & Cost of TurbineCost of Turbine & Electricity cost increase

Cost of Turbine & Electricity CostsCost of Turbine & Government Incentives

Energy Output (kWh) & Electricity cost increaseEnergy Output (kWh) & Electricity Costs

Energy Output (kWh) & Government IncentivesTurbine operating life & Capital grant

Generation tariff & Capital grantDiscount rate & Capital grant

Electricity cost increase & Capital grantElectricity Costs & Capital grant

Government Incentives & Capital grantTurbine operating life & Generation tariff

Discount rate & Turbine operating lifeDiscount rate & Generation tariff

Turbine operating life & Electricity cost increaseTurbine operating life & Electricity Costs

Turbine operating life & Government IncentivesElectricity cost increase & Generation tariff

Electricity Costs & Generation tariff Government Incentives & Generation tariff

Discount rate & Electricity cost increaseDiscount rate & Electricity Costs

Discount rate & Government IncentivesElectricity Costs & Electricity cost increase

Electricity cost increase & Government IncentivesElectricity Costs & Government Incentives

NPV

Fig. 13. Two-factor sensitivity analysis.

R. Walters, P.R. Walsh / Energy Policy 39 (2011) 5167–51815176

This would suggest that other project variables would need toimprove before the generation tariff is effective (i.e. where thetariff becomes significant to a project’s commercial success). Thesensitivities can be explored further by employing a two-factortornado chart as shown in Fig. 13.

The results of the two-factor tornado chart present someinteresting findings, especially when separately categorized asfinancial incentive-based or technology/risk-based (Table 3).

The generation tariff appears as a sensitive variable in only the6th and 7th most sensitive combination. Indeed, of the twogovernment financial incentives, the capital grant is more crucialto commercial success although without the addition of thegeneration tariff the capital grant alone would be insufficient tostimulate small-wind development. In general, project variablesrelated to technology or operational performance rank higher

than government incentives, which may not be surprising as thesmall wind market is in its infancy. However, the results of thisanalysis suggest that tariffs could be more efficiently applied toother small-scale renewable energy technologies or differentapplications of small wind.

After the UK FIT was introduced into the Monte Carlo analysis,the maximum NPV increased more than the mean. This highlightsa potential inefficiency in the UK FIT programme if the emergenceof high NPVs encompassed project input combinations that wereonly accessible to some (e.g. those with good wind locations).Energy yields can be independently fixed (average low andaverage high) in order to determine which project inputs (otherthan yield) are now contributing to the highest performing sites.A fixed high-energy performance results in a change in the orderof sensitive variables (Fig. 14). Turbine operating life is now

R. Walters, P.R. Walsh / Energy Policy 39 (2011) 5167–5181 5177

highly sensitive, suggesting its role as a project input in the bestperforming NPV simulations. It can also be interpreted that thehigher cost of long-lasting turbines becomes justified under theUK FIT and that the highest small wind project NPVs arecontingent on spending more up-front capital, which not every-one has the disposable income to do.

5%

0.205

20%

10

2500

-1000.00

-900.00 -60

Cost of Turbine

Turbine operating life

Capital grant

Generation tariff

Discount rate

Electricity cost increase

Energy Output (kWh)

Electricity Costs

Government Incentives

-800.00 -700.00

Fig. 14. High energy yield one

0.2

1

20%

2500

-1600.00

-1500.00

-1400.00

-1300.00

-12

Cost of Turbine

Capital grant

Turbine operating life

Generation tariff

Discount rate

Electricity cost increase

Energy Output (kWh)

Electricity Costs

Government Incentives

Fig. 15. Low energy yield one

Table 3Grouping of 10 highest sensitivity combinations.

Sensitivityranking

Technology-based/risk-based Government incentive-based

1 Cost of turbine, energy output

2 Cost of turbine Capital grant

3 Energy output Capital grant

4 Turbine operating life, energy

output

5 Cost of turbine, turbine operating

life

6 Energy output Generation tariff

7 Cost of turbine Generation tariff

8 Discount rate, energy output

9 Cost of turbine, discount rate

10 Cost of turbine, electricity cost

increase

At sites with low energy yield (Fig. 15), the 30.5 p tariff does littleto help projects become economically attractive while they struggleto produce enough revenue to cover the cost of the turbine regard-less of the turbine operating life. This result suggests that either aminimum energy yield or high tariff amount is necessary for thetariff to stimulate widespread commercial purchases. The bestcombination of project inputs – whether wind access or ability topurchase better turbines – will always produce inequitably highreturns under a fixed-tariff amount of this magnitude. Combinationsof average project inputs may become economically attractive at93.5 p/kWh but the best combinations would then benefit exces-sively. In addition, this level of tariff would be seen as unreasonablegiven the current tariff of 30.5 p/kWh and the presumption that thislatter rate has been determined based on a fundamental provision ofFIT policies; that the tariff is sufficient to cover generation costs plusa reasonable rate of return.

5. Conclusions

The proposed 2010 UK FIT small wind generation tariff amount(30.5 p/kWh) is unlikely to initially stimulate widespread commercialviability of small wind projects in urban locations. More than 95% oflocations (reflective of the WWTP-measured wind range) would

0.15

165.13

0.51%

0.15

165.13

2.72%

1%

0.405

40%

20

1500

0.00 -300.00NPV

-500.00 -400.00 -200.00 -100.00 0.00

factor sensitivity analysis.

0.15

53.99

0.51%

5%

05

0

0.15

53.99

2.72%

1%

0.405

20

40%

1500

00.00

-1100.00

-1000.00

-900.00

NPV

-800.00 -700.00 -600.00

factor sensitivity analysis.

Power Curves of WWTP Data Measured

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

1.00Average Wind Speed per Month

kWh

per m

onth

Lillington Road

Birds Hill

Leicester

Hill Close Gardens

Napier

Daventry Civic Centre

Daventry County Park

Park Farm

Delta Court

2.00 3.00 4.00 5.00

Fig. A2. Graphed Power Curves Extrapolated from WWTP Data.

R. Walters, P.R. Walsh / Energy Policy 39 (2011) 5167–51815178

produce less than desirable NPVs. What impact this may have onpurchasing behaviour as a whole, considering additional motivationssuch as energy independence and environmental benefit, remainsdifficult to quantify, but from a strictly investment perspective, theinstallation of small wind turbines in urban sites seems to be at a lossat this time.

Application of the proposed UK FIT for micro-generation windprojects across mildly selective, urban (building-mounted) sitesproduces a few attractive project NPVs over �£500 but less thanone-tenth of mildly discriminate projects reach this level, even withthe assistance of a subsidy. Because the top tier of project permuta-tions comes close to reaching a positive NPV (�£298) using the UKFIT, it is not a far stretch to assume technological developments(manufacturing cost decreases and efficiency increases) will improvein the near future to deployable levels of investment attractiveness.The caveat of this positive outlook is that it applies only to high-performing sites. Low-performing sites do not fare well even withthe tariff—low energy yield simply is not enough to cover installa-tion cost by any reasonable amount. Given that there is promise forhigh-performing sites, the problem thus becomes how to screen forthese projects. The top-performing sites require the best combinationof project inputs, including expensive, long-lasting turbines and goodwind resources. However, accurately predicting energy capacity fromspecific urban or semi-urban small wind sites may be problematic.Testing for wind performance adds costs and time that reduce theattractiveness of the project while relying on generous subsidy levelsalone in making the purchase decision may result in disappointingreturns on the part of the purchaser as a result of lower wind speeds.

Accordingly, reports indicating growth in small wind powergeneration are likely overestimated if they are relying on expansionof the urban market. Furthermore, forecasts of increasing contribu-tion of small wind energy to national energy targets by both thegovernment and small wind turbine manufacturers should beconsidered with caution. Given current market conditions, stimulat-ing significant purchase interest in urban small wind may require a

Site

WWTPAverage

(Measured)Oct-07

Nov-07

Dec-07

Jan-08

Lillington Road 2.22 1.73 2.31 3.04 Hill Close Gardens 2.04 1.51 2.44 3.50

Birds Hill* 2.27 1.71 2.74 3.78 Leicester 2.18 1.66 2.36 3.24 Napier 2.99 3.07 3.34 4.17

Daventry Civic Ctr. 2.74 3.82 Daventry County Pk. 2.04 2.51 3.01

Park Farm 2.83 3.22 3.22 Delta Court 2.30

*Birds Hill October 2007 measurement was not used from WWTP due to its p

Site

WWTPTotal

EnergyExported

(kWh)Oct-07

Nov-07

Dec-07

Jan-08

Lillington Road 53.67 129 319 461 Hill Close Gardens* 54.88 80 460 680

Birds Hill 47.39 27 47 236 352 Leicester 63.75 70 164 502 Napier 91.84 142

Daventry Civic Ctr. 109.13 919 Daventry County Pk. 55.79 239 452

Park Farm 178.62 916 177 Delta Court 19.44

*Hill Close Gardens values are lower than operating efficiency because a turb(SOURCE: Results PDF for Hill Close Gardens http://www.warwickwindtri

Fig. A1. Warwick Wind Trials

greater tariff than that proposed by the UK FIT. Higher levels of tariffsrelated to urban wind generation could have serious implications forthe general electricity market in terms of competitive distortions andresultant higher electricity prices from utilities mandated to paytariff-electricity-prices for renewable energy obligations.

To ensure tariff efficiency within the urban building sector, UKpolicy makers may need to withhold the FIT for this application ofsmall wind until more technical research projects – such as theWWTP – can provide better insight as to how to predict urban windperformance (and thus NPV). Without such a measure, the prob-ability is that projects will never reach their NPV expectationsbecause of lower-than-estimated energy yield, creating consumerdistrust and declining demand for installations. Small wind

Feb-08

Mar-08

Apr-08

May-08

Jun-08

Jul-08

Aug-08

Sep-08

2.21 3.12 2.32 2.10 1.93 2.04 1.98 1.54 2.27 3.23 2.14 1.41 1.68 1.54 1.79 1.13 2.36 3.36 2.37 1.97 2.05 2.68 1.87 2.29 2.87 2.06 2.07 1.77 1.81 2.20 1.69

4.10 2.81 2.70 2.33 2.31 2.05 2.01 2.66 3.66 2.84 2.50 2.46 2.39 2.36 1.79 2.15 3.00 2.26 1.94 1.23 1.50 1.37 2.75 3.80 2.70 2.01 2.49 2.63 2.78 1.97

3.85 2.49 2.46 2.34 2.58 2.31 1.80

erceived isolated discontinuity

Feb-08

Mar-08

Apr-08

May-08

Jun-08

Jul-08

Aug-08

Sep-08

218 150 154 105 91 119 103 52 66 0 11 51 42 173 323 115 54 289 313 127 102 101 77 105 129

588 355 492 312 327 335 305 940 512 390

640 119 117 21 51 30 652 955 479 325 367 266 329 215

66 204 129 160 84 44

ine switched off intermittently according to WWTP als.org.uk/8.html )

Results (Measured Data).

R. Walters, P.R. Walsh / Energy Policy 39 (2011) 5167–5181 5179

manufacturers and eager government initiatives exploiting optimisticenergy yield predictions would, in the long run, erode consumerconfidence and damage real potential capacity increases in renewableenergy. If urban (building-mounted) small wind generation is tocontinue, future UK FIT policy must consider that these projects areeconomically more sensitive to factors other than a tariff incentive,

POWER CURVES PER SITE - WWTP and Estimated kWh/m/s/

SiteOct07

Nov07

Dec07

Lillington Road 1.69 1.73 2.31 kWh/m/s = -0.1817x² + 2.9241x - 2.5212 1.91 2.24 4.14 Hill Close Gardens 1.55 1.51 2.44 kWh/m/s = 0.346x² - 0.1459x + 0.5852 1.19 1.59 5.66 Birds Hill 1.83 1.71 2.74 kWh/m/s = -0.7146x² + 5.0975x - 6.2079 0.44 0.82 2.58 Leicester 1.66 1.66 2.36 kWh/m/s = 0.9018x² - 2.504x + 3.2452 1.57 1.27 2.08 Napier 2.28 3.07 3.34 kWh/m/s = 0.5311x + 3.8078 5.02 5.44 5.58Daventry Civic Centre 2.09 2.21 3.08 kWh/m/s = -0.3524x² + 4.9242x - 6.0749 2.67 3.08 5.76 Daventry County Park 1.55 1.64 2.51 kWh/m/s = 1.7028x² - 4.6045x + 3.8248 0.78 0.86 2.86 Park Farm 2.16 2.28 3.22 kWh/m/s = 0.4146x² + 0.0876x + 1.9043 4.02 4.26 8.53 Delta Court 1.75 1.85 2.59kWh/m/s = 0.1969x^2.2509 0.70 0.79 1.68

36 Power Est

20 Estimated

Fig. A3. Chart of Power Curves, Wind Spee

Turbine & WWTP Sites Ampair 600 230 Hill Close Gardens Lillington Road Park Farm Napier

600W

Eclectic StealthGen D400 Birds Hill Delta Court

400W

Windsave WS1200 Daventry County Park Daventry Civic Centre

1.2 kW

Zephyr AirDolphin Z1000 Leicester

1 kW

*AirDolphin costs £5,000 lasting a guaranteed 25 years to exp£3,000.**StealthGen lasts 25 years according to manufacturers

Sources:Eclectic StealthGen D400 http://www.energyenv.co.uk/D400Ampair 600 230 http://www.boost-energy.com(Ampair Inverter) http://www.bettergeneration.com/wind-turWindsave WS1200 http://www.bettergeneration.com/wind-t

Power

Fig. A4. Chart of Turb

such as capital cost reductions through incentive grants. Furthermore,as found with the German policy of higher tariffs for roof-mountedsolar photovoltaic (PV) systems versus ground-mounted PV projects,future UK FIT policy may need to consider micro-wind tariffs thatprovide additional incentives for costlier applications of the sametechnology.

Jan08

Feb08

Mar08

Apr08

May08

Jun08

Jul08

Aug08

Sep08

3.04 2.21 3.12 2.32 2.10 1.93 2.04 1.98 1.54

4.55 3.05 4.83 3.28 3.11 2.33 2.26 1.59 1.77

3.50 2.27 3.23 2.14 1.41 1.68 1.54 1.79 1.13

5.83 1.57 0.96 0.73 1.40 1.32 0.21 0.85 1.12

3.78 2.36 3.36 2.37 1.97 1.92 2.05 2.68 1.87

2.79 2.20 2.88 1.46 0.82 0.94 1.24 2.32 0.83

3.24 2.29 2.87 2.06 2.07 1.77 1.81 2.20 1.69

4.65 3.79 3.27 1.85 1.48 1.71 1.28 1.43 2.29

4.17 2.97 4.10 2.81 2.70 2.33 2.31 2.05 2.01

6.02 5.38 5.99 6.28 3.94 6.33 4.05 4.79 5.00

3.82 2.66 3.66 2.84 2.50 2.46 2.39 2.36 1.79

7.22 3.44 7.70 5.41 4.68 3.91 3.68 3.58 1.61

3.01 2.15 3.00 2.26 1.94 1.23 1.50 1.37 1.47

4.50 1.80 6.40 1.58 1.81 0.51 1.02 0.66 0.73

3.22 2.75 3.80 2.70 2.01 2.49 2.63 2.78 1.97

1.65 7.11 7.54 5.32 4.85 4.42 3.03 3.55 3.27

3.33 2.28 3.85 2.49 2.46 2.34 2.58 2.31 1.80

2.95 1.26 4.09 0.80 2.49 1.65 1.86 1.09 0.73

imates: 72 WWTP Data Points

Wind Speeds: 88 WWTP Data Points

d and Energy Output per WWTP Site.

ected 30 years, prorated for 20 year lifespan is about

WindTurbine.asp

bine-reviews/ampair-600.htmlurbine-review/windsave-ws1000.html

Cost (£) Lifespan (Years)

2,655 15

1,500 20**

1,498 10

3,000* 20*

ine Cost and Life.

R. Walters, P.R. Walsh / Energy Policy 39 (2011) 5167–51815180

If FIT policies are pursued, policy makers should consider theneed for degressive tariffs that are initially set at levels sufficientto reduce economic risk and promote the application of thistechnology and which, over time and with the mutual involve-ment of public and private institutions, reasonably sets staged-reductions in the tariffs that mirrors producer savings in terms oflower costs arising from economies of scale and innovationefficiencies. Understanding where that ‘‘tipping point’’ exists willbe crucial in order to encourage investment and development ofurban wind micro-generation without causing sustained excessadvantages over the existing energy market.

The conclusions of this investigation are limited by the sampleenergy yield being analysed as representative of the UK’s com-plex, urban wind resources and that annual energy data isassumed to be valued equally in the NPV calculations regardlessof when it is generated and consumed. The Monte Carlo analysiscaptures the range of urban wind speed and energy yieldevidenced across the UK, but simulates a random distribution

2008 Actual Cost (p/kWh)

14.93

2020 Predicted Cost (p/kWh)

Increase over 12 years

Increase pe(compound

15.87

18.42

20.61

6%

23%

38%

0.51%

1.77%

2.72%

Fig. A5. Electricity Price

Site-specific inputs Input Cells

LoBo

Discount Rate 2% Turbine Cost after Grant 1050 105

Capital Grant 30% Turbine Operating Life 10 10 Average Wind Speed 2.33 2.0Annual Energy Output (kWh) 110.69 47. Retail Electricity Price 0.15 Generation Tariff 0.305 Electricity price increase (Annual) 1.77%

NPV Analysis 0.15 0.1527 0.1554 Year 0 1 2 3 4 Cashflows -1050.0 50.36 50.66 50.96 51.26 PV -1050.0 49.38 48.69 48.02 47.36 0.1756585 0.1788 0.1819 0.1852 0.1884 Year 10 11 12 13 14 Cashflows 53.21 53.55 53.90 54.26 54.62 PV 43.65 43.07 42.50 41.94 41.39

CUM. NPV -585.60

NPV -585.60

Fig. A6. Sample NP

frequency when in reality a basic pre-screening of projects couldlimit uptake of intuitively poorer-performing sites. Empirical testsand country comparisons of FIT efficiency are inherently difficultas electricity markets, renewable energy resources and subsidypolicies typically exist within a specific national context. Further-more, our study did not explore the influence of building ownerdemographics and household categories in the adoption of urbansmall wind technology and future research into this area wouldidentify important contributions to policy development.

Appendix A

The NPV framework with example simulation numbers andcalculations is shown in Figs. A1–A4.

Element Energy Ltd (2009) published a quantitative analysisreport for the Department of Energy and Climate Change on themarket impact of increasing renewable electricity technologies

r yearannual)

Scenario description by Element Energy Ltd.

Moderate demand, timely investment

High demand, producers’ market power

High demand, significant supply

constraints

and Annual Increase.

wer und

Base Case

Upper bound Selection Method

0 1400 1750 Random Discrete

15 20 Random Discrete

4 2.4 2.99 Random Triangular 20 90.43 119.79 Random Triangular

0.1726 5 6 7 8 9 51.57 51.89 52.21 52.53 52.87 46.71 46.07 45.45 44.84 44.24 0.1918 0.195159 0.1986 0.2021 0.2057 0.2093 15 16 17 18 19 20 54.99 55.36 55.75 56.14 56.53 56.93 40.86 40.33 39.81 39.30 38.80 38.32

-375.84 -179.27

←

←←←

V Framework.

R. Walters, P.R. Walsh / Energy Policy 39 (2011) 5167–5181 5181

containing a key analysis of projected future electricity and gasprices in the UK. Several scenarios of changes to the price ofconsumer electricity were outlined as good projections for thefuture of electricity prices and then converted into a yearly %increase for use in the NPV model. This is shown in Fig. A5.

Sample NPV Framework is shown in Fig. A6.Institutional Barriers (UK) to Installation of Small Wind Systems:

Noise and Vibration Standards.Local Authority Planning Permission.Building Control Approvals – Structural Assessment and Elec-

trical Connection.

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