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Economic Analysis for Management ofDistributed Generation Systems on Electrical

Networks

Breno Wottrich, Luciane Neves Canha, Ricardo Cezar do Amaral, GennAn Morales Espafia

Abstract--This paper presents a study, using the softwareHOMER. for the adequate electrical and economic dispatchanalysis of distributed generation systems with renewable energyresources. In this way, sensitive analyses can be worked out tosimulate the system performance and to examine theattractiveness of the different power dispatch according to themanagement needs. The general logic is always illustrated with acase-study based on the power flow of a Brazilian university. Forthis purpose, there are connected three different types ofelectricity generation to the university's grid, namely: windturbines, photovoltaic panels, and fuel cells fuelled by hydrogengas.

Index Terms-dstributed generation, economic methodology,electrical network systems, software HOMER.

I. INTRODUCTION

(1 LEAN energy technologies have been receiving... exponential attention by governments, industries and

consumers. The commitment assumed by the European Union[1] - [2] and its actions towards sustainable targets also makesevident the necessity of raising the promotion of thesetechnologies. Besides, strong incentives - attractive costsconcerning the selling price of energy to the local network -

suggest the regeneration of investment's interest [3]. Thiseffort increases the social, economic, and environmentalbenefits these technologies can provide [4].

In this framework, there are renewable resources withexcellent potential to enhance the development of distributedgeneration technologies: nowadays is possible to concretizeapplications in large scale of decentralized generation projects.These systems must try to enclose practical aspects of whatseems viable in an economic and management perspective.Connected to this perception, it is central to point out, toexplore the complete potential of renewable energy systems,

Manuscript received April 23, 2009.B. W. is with the Centre of Energy and Environmental Studies, Federal

University of Santa Maria, Santa Maria, RS 97105-900 Brazil; and with theEngineering and Policy Analysis Department, Delft University of Technology,Delft, 2628 KV Netherlands (e-mail: b.wottrich@)gmail.com).

L. N. C and R. C. A are with the Centre of Energy and EnvironmentalStudies, Federal University of Santa Maria, Santa Maria, RS 97 105-900 Brazil(e-mail: lncanha@ufsm.ct.br and amrcasm@hotmnail.com).

G. M. E is with the Engineering and Policy Analysis Department, DelftUniversity of Technology, Delft, 2628 CN Netherlands (c-mailgerman.morales.e@gmnail.com).

the technology must be demonstrated as commercially viable,low risky, and acceptable to the public [5]. Therefore, pilotprojects play a crucial role to the successful development,information diffusion, and public acceptance of these systems.

In that perspective, several studies have been alreadyfocused in the analysis of renewable energy systems using thesoftware HOMER. The emphasis of these investigationsappears to be economic [6] - [7] or technical [8] - [9].Independently of their main concerns, usually there is not agener-al trend. The structure for simulations and all the inputdata are project-specific, with very strict boundaries. They arenot global. Then, their proposed configuration normally doesnot help when trying to access a first, fast and simplefeasibility study with any combination of renewable sourcesconnected to a given electrical network and load.

Accordingly, this paper contributes to the development of amethodology to analyze the integrated economic managementof multiple distributed generation systems connected toelectrical networks. Along the study, all the steps are appliedto a particular scenario - a Brazilian University named FederalUniversity of Santa Maria (UFSM), with its load connected tothe local network and self-producing (part of) its energy. Weuse for this purpose three distributed generation sources. Theproject highlights, in this way, an efficient method to estimatetechnical parameters, to the formulation of feasibility studies,and consequently to support the decision-making processconcerning distributed generation systems. Interestingly, theconceptualizations proposed along this paper have a globalfocal point. Therefore, results in simple, robust, and directstructures to a first financial and technical visualizationenclosing projects of renewable generation of energy.

Il. HOMER LOGIC - NPC AND COST CURVES

HOMER [10] is a computer model proposed by theNational Renewable Energy Laboratory (NREL) forevaluating design options for both off-grid and grid-connectedpower systems. It can be employed for stand-alone, remote,and distributed generation applications. Its optimization andsensitivity analysis algorithms allow to evaluate the economicand technical feasibility of a large number of technologyoptions and to account for variation in technology costs andenergy resource availability. It performs the energy balancecalculations for each system configuration, determines

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whether a configuration is feasible, and then estimates the costof installing and operating the system over the lifetime of theproject. The system cost calculations account for costs such ascapital, replacement, operation and maintenance, fuel, andinterest. As the main economic output, the software displays alist of configurations, sorted by total net present cost (NPC),which are used to compare system design options. HOMERcalculates the total net present cost ($) with (1). Cann is thetotal annualized cost ($Iyr) and CRE is the capital recoveryfactor, which in turn is a function of the interest rate i (%/) andthe project lifetime Rproj (yr).

(1)NPC = CrCRFO.,Rp,3)j

System Modeling NIDIVIDUAL CLETV(technical and n~e s itive Itgaeecounomic)I DispatchSestle

11

+ 4

Fig. 2. General structure for simulations.

Another important factor for simulations is the cost curve.For a desired number of discrete simulated power values, itmust be defined how the input costs will vary. These costs arerelated to capital, replacement and operation and maintenance(O&M). Therefore, for every renewable source, it must beselected the power range and its associated costs. Fig. 1illustrates an example of a cost curve.

The power range goes from zero until 3,250 kW (dispatchintervals of 250 kW). So, one discrete cost value forreplacement and initial capital is chosen for each possible(combination of) simulated power.

- Initial Capital Replacement

1600

1400

1200

-1000

06 600---- --

43400

200

0

0 250 500 750 1000 1250 1500 1750 2000 2250 2500 2750 0000 3250

Power (kW)

Fig. 1. Example of cost curve for HOMER.

I11. STRUCTURE FOR ANALYSIS

The general logic to simulate a distributed generationsystem is showed in Fig. 2. The first step is detail the technicaland economic data to be used as input to the software. It mustbe always in mind to take market information for all system'srequisites.

General equations are set for the renewable sources toaddress a realistic variation in their maintenance and installedcosts. Then, with all the necessary input data to be entered inthe software, the second part takes place. The individualdispatch has as consequence, for each source, the initialdefinition of their maximum installed power and sensitiveanalysis of their behavior in the system. Important to mentionthat, once there is an electrical load connected to a systemwith distributed generation, the optimization algorithm ofHOMER will always give priority for the load. In other words,the renewable sources first serve the load, selling to the

network, at a preset price, just what exceed its necessity. Weassumed also that there is no technical restriction whenselling/purchasing energy to/from the electrical network.Consequently, it is always able to deliver any quantity ofenergy to meet the remaining demand.

Afterwards, with all the results in hand, the collectiveintegrated dispatch is implemented. Due to processinglimitations in HOMER, the combination of the sources (in ourcase wind turbines, photovoltaic panels, and fuel cells fuelledby hydrogen gas) is not possible. The dispatch has therefore tobe implemented individually and two by two - wind turbines

-+photovoltaic panels, wind turbines +-+ fuel cells, andphotovoltaic panels +- fuel cells. With all simulations rankedaccording to their net present cost, a last run is thus executedin the structure with the lowest NPC, with all the optimalvalues for each form of generation. In this way, it is possibleto extract important results for analysis. The next itemsobjectify to clarify the general methodology used. Thespecific case-study is based on the power flow of the FederalUniversity of Santa Maria - Brazil. There were connectedthree different types of electricity generation to the grid,namely: wind turbines, photovoltaic panels, and fuel cellsfuelled by hydrogen gas.

IV. SYSTEM SPECIFICATION

A. Technical Data

The values to model the electrical network, as well as theload, both in the Federal University of Santa Maria, arereferred to real numbers presented during the year 2007. Thelocal utility tariff for the area was readjusted taking intoaccount tables homologated in the year 2008 by the Brazilianelectrical system regulator agent (ANEEL), obtaining is thisway a more actualized costs data.

To the insertion of the renewable sources of the energy, thestart point is the definition of their technical parameters. Theprimary sources of energy (wind speed and solar radiation)can be obtained by the database of NASA [11], while theenergetic values (MJ/kg and k g/in 3) of the hydrogen gas arebased in default values provided by the software. A resume ofthe technical input data is provided on the Table I.

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Crucial to mention that the values present on Table I, andthe logic to obtain them, are not only project-specific, but canalso serve as reference to other similar projects. The values of25 kW for wind turbines and 5 kW for fuel cells are related toindividual units. For the eolian energy, it is simulated severaltypes of turbines in the system with the load and electricalnetwork. The best turbine is then found looking for the highestcapacity factor. In our system, turbines PGE25, 25 kW, with acapacity factor of 21.7% (below the worldwide average of30%). Concerning the fuel cells, a commercial unit of 5 kW[12] can be selected. Finally, once solar panels can in theorybe infinitely connected in series and parallel to give adesirable output, it can be set a proper random variation for

TABLE ITECHNICAL SYSTEM INPUTS

Photovoltaic Wind Turbine Fuel Cell

Lifetime Lifetime

(yrs) 30 Lifetime (yrs) 15 (operating 40,000hours)

Derating 95 Hub height so Heat recovery 5factor (%) (in) 50 ratio(%Groundreflectance 20 Turbine (kW) 25 Power (kW) 5C/o)Tracking TWO Altitude (in) 281 H2 - Density 0.09system axes (kg/in3 )Solar scaled Wind sealed H2 -Lowerannual 4.4 annual 3.22 heating value 120

averageId average (mis) (MJ/kg)

simulations - 10 kW in our example. At this point, we alsomust mention the power limit of each renewable source forsimulation was derived based in specific criterion. The nextitem will explain the principle used to place the individualpower limits, and consequently how the cost curves areconstructed based in these power ranges.

B. Economic Inputs

The cost curve for renewable sources of energy normally isnot a linear fuinction of power. In practice, there is a difficultyto establish their costs variation. Each project has uniquecharacteristics, and diverse factors can influence its final costsof installation, maintenance and operation, namely:governmental subsidies; maturity of the technology; proximitywith the load; labor costs; project complexity; and region ofimplementation. Because the objective is a relative analysis ofthe dispatch costs to classify the systems, and not an isolatestudy for each project, we looked for a trustworthy literature toaddress these expenditures. The Canadian library toolRETScrreno [13] gives average limits, in $CAD/kW for theyear 2005, for renewable energy projects inside Canada.Specifically concerning the sources we are interested in, thelibrary gives the following intervals for installed costs:

--Wind turbine: $ 1,000/kW to $3,OOO/kW.--Photovoltaic module: $ 8,000/kW to $1 2,000/kW.--Fuel cell: $4,OOO/kW to $7,700/kW.Based on these values, there is a large financial interval

where a source can operates, depending on the external andinternal factors of the project. Normally, the lowest value perinstalled power in the range is related to large projects, whilethe highest one is utilized for small scale generations. In fact,if a large scale project utilizes high technology, constructiveinnovations, and has excessive sophistication, there is atendency to increase its unitary costs of installation. However,the criterion used for these costs definition for the HOMER'ssimulations is that there is always scale and scope economieswhen increasing the project installed power.

For determine the cost curves per installed power (initialcapital and O&M), first the power limits for simulations ofeach source in the system must be considered. With thispurpose, a specific condition is used. A first electrical run isimplemented in HOMER for each renewable source, withoutany attention to economic factors, in intervals of individualpower. When the contribution of the renewable generation inthe system reaches the load factor, the maximum power for agiven source is then encountered. For instance, for windgeneration a first electrical run was executed in HOMER(intervals of 25 kW), until the contribution of the renewablegeneration in the system reached the load factor (3 5.7%). Atthis point, the quantity of turbines in the individual system was133, with total power of 3,325 kW. The same idea was henceutilized for the other sources. In the end, the photovoltaic andfutel cells achieved a maximum value of, restpectively, 3,250kW and 690 kW (13 8 units).

Wind Energy Cost EquationsFrom all types of 'new' alternative energies, the eolian one

has a more mature development cycle and a more considerablemarket penetration. There are substantial projects around theworld (Germany, Denmark, USA, and Spain) in a large rangeof power and for many different purposes. Therefore, thevalue of $1 ,000/kW probably is related to capacities ofhundred of megawatts. Because the superior range forsimulations is only 3,325 kW, we thought to be realisticenlarge the inferior cost value to $2,OO0lkW. In this way, thereference interval for this source in our case-study was set to$3,000/kW (25 kW) and $2,000/kW (3,325 kW).

With two extreme cost points already defined, it must bedecided how expenses vary for intermediate values ofsimulated power. Accordingly, we recommend to do notincrease in a constant rate the costs. This fact is mainlybecause, in our view, linear curves would not delineate in arealistic way the scale economies of a given project. With thispurpose (2) and (3) can be considered to find discrete costvalues associated with certain power in a range and,consequently, to build up the input cost curves for simulations.W(n) ca and W(n)o&m refers, respectively, to the installed cost($) and operation and maintenance cost ($Iyr) for 2 <ý n < Nnumber of turbines. N is related to the maximum number ofturbines in the system. P is the nominal power of an individualturbine (kW). Ls and Li reference, correspondingly, thesuperior and the inferior limits of installed costs per kilowatt($/kW). Es and Ei indicate, in that order, the superior and theinferior limits of energy generated (kWhlyr) by the wind

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source, when operating with N and I number of turbines. Toend with, k address the average maintenance cost per kilowatt-hour generated ($/kWh). When n equals to 1, the initial valuesto the series are set according to (4) and (5).

P~ ) N(N -1) 1 (2)W (n)o&m W (n - )o&m + 2k(Es - EiXN + I- n)

N(N -1) (3)W(=1)P=PLs (4)

W(n = I)o&M kEi (5)

The O&M (operation and maintenance) cost for this sourcenormally is function of the amount of energy generated. In thisway, [14] gives a relation to address these variations -

$0.014/kWh. And, according to the first electrical run inHOMER, we had an eolian contribution of 47,628 k)Whyr (Ei)when dispatching 25 kW, and 6,334,471 kAWhyr (Es) utilizingthe maximum capacity - 133 turbines. To end with, it was setthat the replacement cost values correspond to 70% of theinitial capital values encountered in (2).

Solar Energy Cost EquationsWith two cost points already defined - $8,000/kW for

projects of 3,250 kW and $ 12,000/kW for projects of 10 kW -

(6) and (7) can be therefore used to define how costs vary fordiscrete values of dispatched power in HOMER. The logic issimilar from the wind energy source. S(n) ca and S(n)o&mrefers, respectively, to the installed cost ($) and operation andmaintenance cost ($/yr) for 2 x Pi !5: n< Ps power dispatched.Crucially, n varies in discrete intervals, always multiple of Pi.Pi and Ps, in turn, are related to the minimum and maximumsolar power (kW) simulated in the system. Ls and Li reference,correspondingly, the superior and the inferior limits ofinstalled costs per kilowatt ($/kW). To end with, Ks and Kiindicate, in that order, the superior and the inferior limits ofoperation and maintenance ($/yr) for the source. The initialvalues to the series are addressed utilizing (8) and (9).

FSO-Pi)= - 2Pi (Ps ±Pi-nXLs-Li)S~), In-iPs(PS -Pi) 1]n (6)

S~no~m= ~n P~o~ +2Pi (Ps + Pi -nXKs- Kt)S~~n~~o&M -SPn +~o& + P( Pi) (7)

S(n = P),, = PiLs (8)

S~n P0&U =2Pi(Ks - Ki)

PS +Pi (9)

We estimated are necessary, on average, 240 maintenancehours per year for a system composed by 3,250 kW. Thereference [15] indicates a rate of payment for system expertsto 100 Canadian dollars per hour. Therefore, the superior limitof O&M (Ks) was calculated to $24,000/yr. Ki is, in oursystem, approximately $0.00/yr. once small scale projectsoften have their O&M costs neglected. Again, the replacementcost values correspond to 70% of the initial capital.

Fuel Cell Cost EquationsTaking into account the superior and inferior limits of

power for the source -5 kW and 690 kW - and its installedcost range, $4,000/kW - large scale projects - and $7,700/kW -

small scale projects) - (10) and (11) are therefore employed toconstruct the cost curves. F(n)~,, and F(n)oSm refers to theinstalled cost ($) and O&M costs (M/) for 2 x Pi •5 n <ý Pspower dispatched. Remarkably, n varies in discrete intervals,always multiple of Pi. The latter and Ps are again related tothe minimum and maximum fuel cell power (kW) in thesystem. Ls and Li address, correspondingly, the superior andthe inferior limits of installed costs per kilowatt ($/kW). Theinitial values to the series were set using (12) and (13).

F Fn), F(n -Pi),, 2Pi(Ps +Pi -nXLs -Li)1nn - P-i PS (Ps -Pi)

F(n)o&m -5.71 X 1- 6 F(n),,p

F(n = Pi)_P = PiLs

F(n = Pi)o&m= 5.71 X 10- 6F(n =Pi)~,ý

(10)

(11)

(12)

(13)

Differently from the solar and the wind sources, the O&Mcosts for fuel cells are normnally associated with their installedcosts. Hence, the operation and maintenance costs can be setat 5% of the total installed costs. Once HOMER works withhourly data in the year, it is necessary to divide this percentageby 8,760 (hrs). Importantly, the 70% relation of the initialcapital for replacement cost values is also used for this source.

V. SIMULATIONS AND RESULTS

With all the system technical information at hand and thespecific economic curves already calculated based in thegathered data, the final step is therefore implemented: theindividual and collective dispatch of the renewable sourcestogether with the electrical network and the load. So, a run isexecuted for all possible configurations, since only the loadconnected to the network to all the renewable generationworking together. Table 11 gives some economic outputs andthe renewable sources power for the UFSM optimal systems -

with the lowest NPC. Table III tabulates the financial valueswhen utilizing all the distributed generation. Crucially, oncethe case-study is related to a project in Brazil, it was necessaryto change the currency, from Canadian dollars to Brazilianreals - 1 CAD equals to 1.68 BRL. All the values on Table 11and Table III are related to a fix annual interest rate of 6% andproject lifetime of 25 years.

We must state that the management concerns whenanalyzing the economic outputs shall vary, depending on theirneeds. For the UFSM case, it could be interesting toimplement all the renewable sources together, even thisconfiguration does not representing the lowest NPC betweenthe options. There are several benefits for the institution, liketechnology development and know-how acquired.Furthermore, if is taken a long-term view, the costs are highly

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TABLE 11_______________________________ OPTIMAL DISPATCH STRATEGIES _______

Optimal Power (kW)IntaCaia R) OeainCs R/r Toa P(R) EegCstR$khWind Turbine Photovoltaic Fuel Cell,. nta aia R) OeainCs R/r oa P R) Eeg ot($kh

--- 0 5,538,486 70,800,440 0.3281,900 - -6,985,091 4,971,865 70,542,216 0.327

- 10 - 201,600 5,533,772 70,941,784 0.329- - 5 64,680 5,544,193 70,938,072 0.329

1,875 10 - 7,115,595 4,962,954 70,558,800 0.3271,900 - 5 7,049,771 4,997,601 70,680,224 0.327

- 10 5 342,100 5,538,979 71,148,848 0.3301,875 10 5 7,256,095 4,978,562 70,898,832 0.328

TABLE IllFINANCIAL OUTPUTS FOR TILE. COLLECTIVE RUN

Cornponent Capital (R$/yr Replacement (R$/yr O&M (RS/y H2 (R$/yr Salvage R$/y Total (RS/yPhotovoltaic 21,702 0 0 0 -843 20,859Wind Turbine 540,859 180,545 120,165 0 -33,605 807,964Fuel Cell 5,060 0 289 10,329 -482 15,196Electrical Network 0 0 4,702,166 0 0 4,702,166System 567,621 180,545 1 4,822,620 10,329 -34,930 1 5,546,186

diluted. The NPC is R$ 70,898,832, just R$ 8,832 above thesystem without any source. Also, the final energy cost(R$/kWh) remains unchangeable - 0,328. From Table III, theUFSM average annual expenditure would be R$ 5,546,186. Itrefers to the annual value that UFSM would pay, during 25years and with a fix interest rate of 6%, to generate and buy itsenergy. The item salvage is related to the equipments value inthe end of the project lifetime (R$ 34.930/yr).

Fig. 3 shows the layout in HOMER for the same collectivesystem. For the fuel cell, we decided to turn it on only duringpeak hours, due to the difficult to have a reliable source for thehydrogen gas in the region. Concerning the converter,practically there was no need to distinguish if a source isoperating in AC or DC mode, due to the installation and O&Mcosts of all required equipments have been already included inthe renewable sources curves. In energetic terms, almost allthe renewable energy was generated by wind turbines - 2 1%.For photovoltaic panels, the average power output wassituated in 2.2 kW and capacity factor of 22.3 %. The windgeneration had an average of 408 kW and a capacity factor of21.7 %. Lastly, the fuel cell operated always at full capacity (5kW), with the lowest capacity factor (8.94 %). It offered athermal generation of 3.53 kW and 3,149 mn3 for the annualconsume of hydrogen gas (approximately 384 cylinders/yr,2,400 psi, 49.6 L). To conclude, as the considerations alongthis paper have the purpose of a first feasibility study, fturtherdetailed economic and technical analysis is required forpractical implementation.

PGE 20/2 UFSM_0746 MWh/d

5.4MW peak

Converter Fuel Cell

AC DC

Fig. 3. Layout in HOMER for the collective run.

VI. CONCLUSION

This paper has presented a useful structure to analyze theeconomic feasibility of renewable energy projects. The bestsystem can therefore be evaluated simply by defining itstechnical constants and by using the general economic curvesand intervals recommended. The proposed methodology canassume hence a fundamental role to add up financial andtechnical concepts when structuring dynamic systems togetherwith renewable generation. In this perspective, the boundariescould be expanded, aggregating uncertainty and macro-economic, demographic and social variables.

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[11) Home page of NASA Surface Meteorology and Solar Energy Data Set:http://eosweb.larc .nasa.gov/sse/RETScreen.

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