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A hierarchical methodology for the integral net energy designof small-scale hybrid renewable energy systems

David Hernaacutendez-Torres an Alberto J Urdaneta Urdaneta b Paulo De Oliveira-De Jesus c

a Laboratoire Plasma et Conversion d Energie (LAPLACE) UMR5213-CNRS-INPT-UPS Toulouse Franceb Fundacioacuten para el Desarrollo del Servicio Eleacutectrico Caracas Bolivarian Republic of Venezuelac Department of Conversion and Energy Delivery Universidad Simoacuten Boliacutevar Caracas Bolivarian Republic of Venezuela

a r t i c l e i n f o

Article historyReceived 16 March 2015

Received in revised form

20 May 2015

Accepted 6 July 2015

Keywords

Alternative renewable energy

Power generation

Levelized costs

Analytic hierarchy process

a b s t r a c t

In this paper a two level hierarchical methodology is presented for the integral design of hybrid powergeneration systems based on alternative renewable energy (ARE) systems using the net energy concept

and considering technical economical societal as well as environmental aspects Results are presented

for the design of a small-scale hybrid renewable energy system using the proposed methodology in

Margarita Island Venezuela The proposed methodology applies the integrated analytical hierarchy

process (AHP) methodology to the selection of the best hybrid renewable energy system using the

concept of net energy and is divided in two phases a classic optimization process using levelized costs

minimization and an AHP implementation for decision making problems Under this methodology

technicalndasheconomical aspects are considered as quantitative parameters while socialndashenvironmental

aspects depend largely on the criteria of the system planning engineers and future users of the system

Technicalndasheconomical aspects are considered using specialized software used to optimize and compute

the best con1047297guration of an ARE project Socialndasheconomical aspects are de1047297ned as a series of parameters

that should be considered by the planning team a meeting of experts or a community consensus on the

project site Several diesel price scenarios (low intermediate and high) are considered The results show

the importance of the proposed tools for decision making problems

amp 2015 Elsevier Ltd All rights reserved

Contents

1 Introduction 100

2 Proposed methodology 102

21 Analytic hierarchy process (AHP) 102

22 The AHP method and the ARE sources 103

23 Energy return on energy invested (EROEI) 103

3 Study case 104

31 Low diesel price scenario 105

32 High diesel price scenario 107

33 Intermediate diesel prices scenarios 108

4 Conclusion 109

Acknowledgements 109

Appendix A Pair-wise matrices for low diesel price case study scenario 109Appendix B Pair-wise matrices for high diesel price case study scenario 109

References 110

1 Introduction

The future of the energy exploitation in our planet is bounded

to many challenges such as the achievement of the diversi1047297cation

of the primary energy sources Although recently oil prices have

Contents lists available at ScienceDirect

jo ur nal ho me pag e wwwelseviercomlocaterser

Renewable and Sustainable Energy Reviews

httpdxdoiorg101016jrser201507008

1364-0321amp 2015 Elsevier Ltd All rights reserved

n Corresponding author

E-mail addresses dhernandezlaplaceuniv-tlsefr (D Hernaacutendez-Torres)

aurdanetafundelecgobve (AJ Urdaneta Urdaneta)

pdeoliveirausbve (P De Oliveira-De Jesus)

Renewable and Sustainable Energy Reviews 52 (2015) 100ndash110



had an important decrease the alternative renewable energy

(ARE) sources are signaled to play an increasingly more important

role in this diversi1047297cation process Renewable energy sources have

grown from 01 of total of primary energy in 1973 (6106 MToe) to

11 of 13 371 MToe in 2013 [1] This implies an increase of 2400

in 40 years according to International Energy Agency [1]

Several reasons can explain the emergence of renewable

energy as demography conventional energy scarcity equilibrium

of planetary resource use Climate change is a fact Traditionallythe role of ARE sources has been referred to as part of the solution

to global warming [2] Also 80 of global energy resources are

used by 20 of the population living in developed countries [3]

ARE sources are an instrument to address the development of

world population instead of using conventional energy resources

Some advantages of ARE source are their participation on

energy diversi1047297cation their modularity and the capability of

decentralized operation They may contribute to equitable geo-

graphical energy distribution in rural countries and of course ARE

source are environmental friendly when compared to some con-

ventional sources as fossil fuel In a regional context ARE sources

are expected to play a more important leading role since wind

energy sources show a signi1047297cant development potential [4] A

very interesting comparison includes the concept of net energy

where the energy ef 1047297ciency of the given sources is computed

namely a focus is given to ldquohow much energy is needed to produce

energyrdquo [5] In this net energy analysis ARE sources are well

placed when compared to hydrocarbon sources

The analysis of the important role that ARE sources may play

includes the following factors

Their participation in the energy diversi1047297cation process Inevitable participation in the national energy approach as

conventional source are becoming scarce (as big scale hydro-

power) oil pro1047297tability should be maximized through exports

or due to immediate unavailability of some resources at big

scale as in the case of natural gas Identi1047297cation of the politicalndashterritorial importance of ARE

installations on remote and isolated sites with special interestin strengthening the nationals developing axes

Active role of ARE source in network stability when considered

as distributed energy sources with migration towards smart

grids networks In1047298uence on the electricity balance and reduction of power

transmission limits with installation of ARE sources near

energy consumption centers

The development of ARE resources are strongly dependent on

energy decision makers preferences These decision makers are

generally energy stakeholders that participate in the energy

market as investors and operators In general ARE projects are

strongly encouraged by national or regional regulatory boards

with strong incentives in order to achieve some economy of scalein alternative generating technologies Within this context when a

energy decision maker prospects the implementation and use of

ARE sources an ef 1047297cient methodology for a technicalndasheconomical

selection is required

There exists in literature several contributions about how to select

an appropriate electrical generating scheme based upon ARE sources

A comprehensive review can be found in [6] with a survey of

methodologies and computational tools to address this problem We

will highlight two of them First the RETscreen platform [7] provides

an option for an initial analysis of technical and 1047297nancial viability of potential renewable energy Another well developed tool is HOMER

[8] which is able to simulate and 1047297nd the best selected option for

energy source con1047297gurations in a given project In this paper it is

considered that the HOMER tool is known by the reader however a

detailed user manual is given in [8]

However despite ongoing ARE evaluation methodologies have

strong strengths on system optimization of technical and eco-

nomic variables It is observed that social and environmental

aspects are hard to treat and valuate in these proposals Quantify-

ing and evaluating the social and environmental viability of a

project is a dif 1047297cult task The analytic hierarchy process (AHP) can

be applied as a complementary evaluation technique in order to

evaluate societal and environmental impact aspects of widespread

integration of ARE sources The methodology proposed and

developed in [9] gives the possibility of classifying the different

analysis criteria into a hierarchical model thus solving the

problem of selection between several solution alternatives The

use of a de1047297ned weighting system makes it easier to classify

alternatives an criteria entries by applying a pair-wise comparison

We can 1047297nd some background on AHP application for ARE

planning purposes Comprehensive reviews on AHP application

for ARE resource planning and project valuation can be found in

[10ndash12] In the following we discuss some of them An analytic

hierarchy process has been applied by [1314] for renewable

energy resources in Jordan The application of an AHP method

for Chinese ARE sources is found in [1516] In [1718] the use of the

AHP method is proposed for decision making and planning of ARE

generation projects in Canada and India respectively Similarly in

[1920] the AHP method is applied for renewable energy planningin Malaysia and Indonesia respectively An integrated VIKOR ndashAHP

methodology to the selection of the best energy policy and

production site is developed in [21] In the work presented in

[22] an integrated methodology is proposed combining linear

optimization and AHP The use of different advanced techniques

along with the AHP method has also been formulated in the

literature In [2324] fuzzy logic is used for the AHP approach for

an ARE application in Brazil and Korea respectively An analysis of

the energy return on energy invested impact of ARE sources to the

environment is presented in [25]

This paper presents an integral approach that considers a

technicalndasheconomical analysis for ARE sources technology selec-

tion but also societalndashenvironmental aspects are considered dur-

ing the analysis of the project considering the net energy or EROEIconcept [26] The use of a two level analytic hierarchy process

Fig 1 Proposed methodology

D Hernaacutendez-Torres et al Renewable and Sustainable Energy Reviews 52 (2015) 100ndash110 101



(AHP) is proposed for this purpose The goal is to de1047297ne a

parameter that quanti1047297es the energy ef 1047297ciency of energy sources

The results of the technicalndasheconomical optimization problem

obtained by linear levelized cost minimization are used as an

input to the decision making model based in AHP The methodol-

ogy has been coded using basic programming language and two

study cases are discussed

This paper is divided in three sections In a 1047297rst section the

proposed methodology is presented In the second section themethodology is presented and formulated for a study case Finally

in the last section several results are presented and discussed

2 Proposed methodology

The methodology proposed in this paper is divided in two

levels The 1047297rst part involves the technicalndasheconomic analysis of

the project The second part deals with the socialndashenvironmental

analysis The goal of the methodology is to 1047297nd the best adapted

ARE sources con1047297guration for a given project where both techni-

calndasheconomical and socialndashenvironmental aspect are integrally

considered

The experts of the system may initially de1047297ne the search matrix

for the optimization problem formulation as proposed in [27] Theproposed strategy is also based on the results obtained in a 1047297rst

preliminary analysis using the software in [8] and considering only

technicalndasheconomical aspects A second analysis includes the

socialndashenvironmental aspects using the AHP method As the AHP

is based on weighted classi1047297cation by pair-wise comparison it is

proposed that some of the weighting vectors should be directly

completed from the results obtained in the 1047297rst analysis using [8]

The criteria evaluation of the AHP method may be performed by

the same initial experts of the system The optimization process is

given by exhaustive by time-domain simulations with varying

system components size The different component sizes consid-

ered during simulation may be resumed in a multivariable search

matrix This methodology structure is resumed in Fig 1

21 Analytic hierarchy process (AHP)

The AHP is a method based on the formulation of a hierarchy

structure that is used to deal with complex decisions The method

classi1047297es the choices or solutions to a given problem by evaluation

according to different criteria and a de1047297ned weighting system

Weights are de1047297ned by a pair-wise comparison simplifying the

selection process

In the AHP method problems are modeled as hierarchies This

process is shown in Fig 2 In many cases each criteria may be

divided in several sub-criteria to further analyze the details of the

studied problem

Pair-wise comparisons are used to establish the importance

weight 1047297xed to each criteria These comparisons are based on thescale system proposed by [9] This scale is shown in Table 1

Comparisons using this scale in each level of analysis are used

to build the pair-wise comparison matrices Using the procedure

detailed in [9] the weights associated to each comparison can be

obtained For each comparison matrix two elements of interest are

computed the associated weight vector and the matrix incon-

sistency ratio This last value is used to estimate the consistency of

pair-wise comparisons Consider for example that a problem or a

criterion that needs to be evaluated for three candidate options

Consider for illustration purposes that option 1 is slightly more

important (factor 3 in the pair-wise comparison scale) than option

2 and that option 1 is strongly more important than option 3

(factor 5 in the pair-wise comparison scale) The problem now is tode1047297ne the pair-wise comparison between options 2 and 3 Not a

trivial solution since a system of equations needs to be solved A

graphical solution to this problem is presented in Fig 3 It may be

considered as an approximate solution that the comparison can

take a value of 2 in the proposed scale With these values the pair-

wise comparison matrix for this example is given by

Criterion Op1 Op2 Op3 Weight

Op1 1 3 5 06483

Op2 13 1 2 02297

Op3 15 12 1 01220

with an inconsistency ratio of 00036

In [9] it is considered that an inconsistency ratio lower than10 means that decisions expressed in the pair-wise matrix are

consistent Simple programming language is used to compute

Fig 2 The AHP method

Table 1

Pair-wise comparison table

Numerical rating Verbal judgment of preference between alternatives i and j

1 i is equally important to j

3 i is slightly more important than j

5 i is strongly more important than j

7 i is very strongly more important than j

9 i is extremely more important than j

2468 I ntermediate values

1 15 2 25 3 35

0

001

002

003

004

005

006

Fig 3 Illustrative example for computing inconsistency ratio

D Hernaacutendez-Torres et al Renewable and Sustainable Energy Reviews 52 (2015) 100ndash110102



weights and inconsistency ratios The eigenvectors e of the pair-

wise comparison matrices are given by

e frac14 eigs M λmaxI n

eth1THORN

where M is the pair-wise decision matrix λmax is the maximum

eigenvalue and I n is the identity matrix of size n the size of M The

weight vector is obtained by computing the average values of each

row of the eigenvector

The inconsistency ratio is obtained by 1047297

rst computing theconsistency index

CI frac14 eth λmaxnTHORN

ethn1THORN eth2THORN

Then the inconsistency ratio is given by

CR frac14CI

RI eth3THORN

where RI is the random index given in Table 2

22 The AHP method and the ARE sources

The goal now is to de1047297ne a methodology based on the AHP

method for hybrid power generation projects using ARE sources A

1047297rst classi1047297cation of different criteria is proposed according toimportant variables considered as candidates to have signi1047297cant

weight on the decision making problem

Given the problem complexity and the quantity of variables to

be considered the classi1047297cation is simpli1047297ed to two sets of criteria

technicalndasheconomical aspects and socialndashenvironmental aspects A

similar classi1047297cation is proposed in [17] Each set of criteria is then

divided with the following structure

I ndash Technicalndasheconomical aspects (TecEconAsp)

A Resources availability (AvailRes) A1 Total energy produced (for network connected systems)

or capacity shortage (for stand-alone systems)

A2 Multiple equipment location possibilities

A3 Type of energy constant or intermittent B Technical feasibility (TecFeas)

B1 Technological maturity and local experience with the

technology

B2 Availability of the technology B3 Net energy associated

C Financial viability (FinVia)

C1 Total cost of the project in net present cost (NPC)

C2 Total levelized cost-of-energy (LCOE) in $kWh C3 Total operational and maintenance costs

II ndash Socialndashenvironmental aspects (SocEnvAsp)

D Ecological impact (EcolImp)

D1 Reduction or substitution of CO2 emissions

D2 Reduction of impact on surrounding ecosystem

degradation D3 Visual or sound impact on surrounding populated areas

E Educational potential (EduPot)

E1 The project is tangible and visible within the

community E2 Promotes awareness and public interest on energy

problems

E3 Community participation F Ecological and social impacts (EcoSocImp)

F1 The project promotes economical activity

F2 The project promotes social development

Using this classi1047297cation the hierarchy structure proposed is

given in Fig 4

23 Energy return on energy invested (EROEI)

One of the main contributions of the proposed methodology

consists in the incorporation of the use of the net energy concept

in the pair-wise comparison matrices at levels 2ndash4 of the

described methodology This concept and the energy return on

energy invested (EROEI) for each ARE source can be found in [5]

and [26] respectively

The goal is to include a parameter that quanti1047297es the energy

ef 1047297ciency of energy sources Instead of concentrating analysis

efforts on costs associated to energy production focus is given toldquohow much energy is needed to obtain energyrdquo

The EROEI is given by equation

EROEI frac14UAE =EE eth4THORN

where UAE is the usable acquired energy and EE is the energy

expended

In a 1047297rst level the EROEI variable is taken into account by

considering an important weight to variable B3 in the ldquotechnical

feasibilityrdquo pair-wise matrix On a second level the EROEI will play

an important role when comparing ARE sources technologies by

pair A classi1047297cation of energy sources technologies as a function of

their EROEI ratio is presented in [26] Accordingly wind turbines

have an EROEI of 181 while for solar photovoltaic this value is

around 681 This means that when evaluating the hybrid ARE

solution candidates from the HOMER optimization a higher scaled

weight will be granted to options with a greater nominal power

from wind turbines when contrasted to less favorable EROEI for

solar photovoltaic technologies For comparison purposes an

Table 2

Average random index matrix

Size of M 1 2 3 4 5

Random index 0 0 058 09 112

Size of M 6 7 8 9 10

Random index 124 132 141 145 149

Fig 4 Proposed hierarchy structure




example of a renewable energy with high EROEI is hydropower

with typical ratio higher than 1001 In the other hand biodiesel

and bitumen oil from tar sands (as in the Venezuelan Orinoco oil

belt) are among the energy sources with the lower EROEI ranging

from 13 to 41 The impacts of these assumptions are discussed

later in the study case section

The matrices for levels 0 1 and 2 are now presented they are

constructed under the assumptions described before They can

also contain the results of an expert meeting or a community pollevaluating the different pair-wise comparisons When selecting

the pair-wise comparison values in a three dimension matrix at

least one value is specially selected to minimize the inconsistency

ratio from Eq (3)

Goal TecEconAsp SocEnvAsp Weights

TecEconAsp 1 3 075

SocEnvAsp 13 1 025

TecEconAsp A AvailRes BTecFeas CFinVia Weights

A AvailRes 1 3 5 06370

BTecFeas 13 1 3 02583CFinVia 15 13 1 01047

SocEnvAsp DEcolImp EEduPot FEcoSocImp Weights

DEcolImp 1 3 13 02684

EEduPot 13 1 14 01172

FEcoSocImp 3 4 1 06144

A AvailRes A1 A2 A3 Weights

A1 1 7 1 04869

A2 17 1 15 00778

A3 1 5 1 04353

BTecFeas B1 B2 B3 Weights

B1 1 13 19 00704

B2 3 1 15 01782

B3 9 5 1 07514

CFinVia C1 C2 C3 Weights

C1 1 1 3 04286

C2 1 1 3 04286

C3 13 13 1 01429

DEcolImp D1 D2 D3 Weights

D1 1 5 7 07306D2 15 1 3 01884

D3 17 13 1 00810

EEduPot E1 E2 E3 Weights

E1 1 2 13 02493

E2 12 1 13 01571E3 3 3 1 05936

FEcoSocImp F1 F2 Weights

F1 1 15 01667

F2 5 1 08333

In the following sections the practical use of the AHP method is

given with some application examples For an evaluation of the

proposed methodology different study scenarios are presented

3 Study case

To illustrate the proposed planning methodology a study case

is presented in this section The principle is to design a hybrid

power generation system based on ARE sources in Margarita

Island in north-east Venezuela The application is based on an

isolated home without connection to the national electrical grid

For this study case solar wind and fuel resources are con-

sidered as available on the project site The diesel fuel is available

for portable electric power generation Data of wind resource in

Margarita Island are obtained from [28] Data for solar irradiance

was obtained from 10 min measurements in the central Venezue-

lan region (Caracas) These measures are available at httpcbm

usbveclima For illustration purposes of this example the statis-

tical data for solar irradiation in the central Venezuelan region is

used as the solar resource in the island Using an extrapolation to

the island geographical coordinates the HOMER is able to generate

stochastic time series hourly data for both wind and solar

resources Input data of equipment costs and electrical load pro1047297le

were obtained from typical HOMER data library The load pro1047297le

considered is assumed as a typical representation of small house

electrical energy consumption The availability curves of wind and

solar resources and the electrical load pro1047297le considered are

presented in Figs 5 and 6 respectively

Before de1047297ning the possible candidate options for a successful

decision making strategy comparisons associated with levels 0ndash2

(cf Fig 4) may be de1047297ned For this the assumptions considered for

the study are now de1047297ned These assumptions may change for

different project designs and as before in their de1047297nition may be

involved the planning team expert groups or even the organized

community

Some of the assumptions for the study and construction of thepair-wise comparison matrices are as follows

Fig 5 Solar and wind resources considered for Margarita Island




Within the technical and economic aspects the availability of

resources is considered as a key parameter with a higher

importance to the technical feasibility and economic viability In the social and environmental aspects it is given a slightly

higher importance to economic and social impact in the

community over the ecological impact and the educational

potential of the project A higher importance is considered for the following para-

meters the amount of energy produced the energy type

(continuous intermittent etc) the concept of net energy and

the importance in promoting social development in the com-

munity where the project is executed

Several scenarios for this study case are now presented for

different diesel prices with low intermediate and high price levels

31 Low diesel price scenario

In this 1047297rst case study the application example presented in

the previous section is considered under the assumption that

diesel fuel is available at current Venezuelan market prices ie

0029$L (price for July 2012)

The design of the hybrid system considers solar panels wind

turbines and diesel generators as energy sources The design

problem is reduced to 1047297nding the optimal size of each system

component For the speci1047297c hybrid generator system problem

search variables are the size (nominal power in W) of the powerconverter the battery stack the solar photovoltaic panels the

wind turbines and the diesel generator A backup battery system is

considered for increased system reliability The power converter

in this case an inverter is necessary to couple DC and AC buses

Additional converters may be considered for DCDC coupling

between ARE sources For example boost or buck converter may

be used to improve system controllability and operating perfor-

mance (improved ef 1047297ciency control to optimal operating point

MPPT maximum power point tracker algorithm etc) Restrictions

of 10 minimum capacity shortage and 25 minimum ARE sources

participation are considered With the given resource availability

data and with several proposed hybrid generation system con1047297g-

urations the HOMER is used for optimization and simulation of

results

With the system topology de1047297ned a search matrix is obtained

and it is possible to run the optimization algorithm using HOMER

to 1047297nd the optimal solution When the optimization process

completed the 1047297rst three solutions are presented in Table 3 They

are classi1047297ed in descending order by their total net presentcost (NPC)

The option 1 consists of 025 kW in solar panels 2 SW AIR X

400 W wind turbines a 500 W diesel generator 1 string of

6 batteries and a 1 kW power inverter Option 2 is composed by

4 AIR X 400 W wind turbines 500 W diesel generator 1 string of

6 batteries and a 1 kW power inverter Finally option 3 is given by

075 kW in solar panels 500 W diesel generator 1 string of

6 batteries and a 1 kW power inverter All batteries are model

S4KS25P 76 kWh (4 V 1900 Ah)

In Fig 7 the simulation results of the operating behavior of the

obtained system con1047297gurations are presented With these results

it is clear that the diesel generation participation is high This is

normal form of a NPC optimization since fuel prices are very low in

Venezuela These three options are 1047297xed as the candidate solutionsfor the second part of the methodology presented in the previous

section using the AHP method

The second part of the proposed methodology deals with the

evaluation of candidate options using the AHP method For this

each option is weighted by comparison with all the selected sub-

criteria (cf Fig 4) Previously described assumptions are used to

evaluate pair-wise comparisons between options and sub-criteria

Additionally as a reference an analysis of the negative impact of

ARE sources to the environment is presented in [25]

In this paper it is proposed that some evaluation weights

should be directly 1047297xed from results obtained with the optimiza-

tion performed using HOMER Then for example for sub-criterion

A1 ldquoElectrical energy capacity shortagerdquo the pair-wise matrix is

not evaluated and the vector weight is directly de1047297ned from

Fig 6 Electric load pro1047297le considered

Table 3

Results obtained with HOMER for the low diesel price case study

Structure NPC ($) OampM costs ($year) LCOE ($kWh)

Option 1 14 021 569 0301

Option 2 14 698 602 0315

Option 3 14 826 514 0318




optimization results with values 115 502 and 482 in kWhyear of

production capacity shortage for options 1 2 and 3 respectively

These values are normalized to the AHP method evaluation scale

(1ndash9) and the weight vector is respectively given by 023 038 and

039 It should be noted that in this case option 3 has the higher

weight because it represents the lower capacity shortage which is

a desired design requirement

The local weights matrix obtained is given in Table 4

The cells 1047297lled with light gray color denote the weights that

were 1047297xed directly from results of the optimization using HOMER

Pair-wise matrices used to obtain this weight matrix are presented

in this paper in Appendix A

The global weight vector is given in Table 5

With these results the global weights for each candidate

option are obtained as

Option 1 03413

Option 2 03247

Option 3 03340

Fig 7 Simulation results (electricity production) of hybrid sources for the three options considered

Table 4

Local weights matrix obtained for low diesel price case study

Option A1 A2 A3 B1 B2 B3 C1 C2 C3 D1 D2 D3 E1 E2 E3 F1 F2

Op1 023 014 055 025 025 033 034 034 033 033 033 010 060 033 020 033 033

Op2 038 033 021 059 059 031 033 033 032 034 033 026 020 033 020 033 033

Op3 039 053 024 016 016 036 033 033 035 033 033 064 020 033 060 033 033

Table 5

Global weight vector obtained for low diesel price case study

A1 A2 A3 B1 B2 B3 C1 C2 C3 D1 D2 D3 E1 E2 E3 F1 F2

023 004 021 001 003 015 003 003 001 005 001 001 001 000 002 003 013




With the results obtained after evaluation of each alternative

option 1 stands as still the best solution according to the evaluated

criteria An important aspect that may have an in1047298uence in this

result is the choice of option 1 as a hybrid energy source with a

high component of continuous energy capacity (the diesel gen-

erator represents the 74 of the energy produced) Using HOMER

a time response simulation is obtained for the selected solution

The simulation result is presented in Fig 8 and is representative of

the operation of the hybrid generator during a complete week The1047297gure shows the levels of energy production for each source the

electrical load demand and the batteries state-of-charge (SOC)

The results show the need of the diesel generator when the

batteries SOC falls beyond 50 during low wind energy

production

32 High diesel price scenario

The second case study scenario is given by the same example as

before but now considering international diesel fuel prices The

price taken as reference is at 161$L (average value in Europe

during July 2012) Using a similar model as in the previous case

the levelized cost optimization using HOMER is performed Results

are shown in Table 6 where the system characteristics arepresented The operating electricity production characteristic of

each con1047297guration is presented in Fig 9 In this case power

generation from solar panels dominates the 1047297rst and second

0 20 40 60 80 100 120 1400

05

1

15

0 20 40 60 80 100 120 14040

60

80

100

Fig 8 Time simulation results hybrid system operating during a week

Table 6

Results obtained with HOMER for the high diesel price case study


Option 1 25 896 383 0590

Option 2 28 408 384 0629

Option 3 29 469 878 0632

Fig 9 Simulation results (electricity production) of hybrid sources for the three options considered in the second case study problem




options The third system option is composed by a mixed solar

winddiesel power generator In this second case study option 1 is

composed by 25 kW in solar panels 1 SW AIR X 400 W wind

turbine 1 string of 6 batteries and a 1 kW power inverter Option

2 is a solution with 3 kW in solar panels 1 string of 6 batteries and

a 1 kW power inverter Option 3 is given with 175 kW in solar

panels 3 SW AIR X 400 W wind turbines 500 W diesel generator

1 string of 6 batteries and a 1 kW power inverter All batteries are

model S4KS25P 76 kWh (4 V 1900 Ah)

With a similar analysis as in the previous case the local

weights matrix is given in Table 7 Here light gray cells denote

the weights that were 1047297xed directly from results of the

optimization using HOMER Similarly pair-wise matrices used to

obtain this weight matrix are presented in this paper in Appendix B

The global weight vector is then given in Table 8

Finally global weights of the three options are obtained as the

product of the local weights matrix and the global weight vector

The result is given by

Option 1 02834

Option 2 03056

Option 3 04110

In this case a re-arrangement of 1047297nal solutions is obtained after

completion of the AHP method The 1047297nal problem solution

selection is given by option 3 where as in the previous case a

higher participation of a continuous energy source has an in1047298u-

ence in the 1047297nal result The third option gives the best balance in

terms of net energy as the other two options are primarily

composed by solar panels A 1047297nal critical parameter was the

capacity shortage which turned out to be relatively high for the

1047297rst two options with unmet supplied energy of 304 kWhyearand 173 kWhyear respectively Time response simulation results

for a complete week of operation are presented in Fig 10 A main

characteristic is the high solar power production with the exceed-

ing generated energy used to keep desired levels of battery stack

SOC A good energy balance is obtained with this option with

virtually no capacity shortage during a year round operation

33 Intermediate diesel prices scenarios

Two additional scenarios were considered with intermediate

diesel price values of 05 and 12$L Similar considerations to the

previous cases were used for pair-wise matrix comparisons

For a diesel price of 05$L the results of the HOMER optimiza-

tion are very similar to the low diesel price case All the possiblesolutions in the group of most interesting optimization results

include a 500 W diesel generator This fact re1047298ects that the diesel

prices are still very low and do not have a big in1047298uence in the

project 1047297nal NPC values After applying the described methodol-

ogy the selected optimal system is given by a mix of solar (250 W)

wind (800 W) and diesel generation (500 W)

For a diesel price of 12$L the optimization yields three main

options to be considered

1 The 1047297rst option is given by the installation of PV (2500 W) and

wind (400 W)

2 The second option consists of a mix of PV (1500 W) wind

(1200 W) and diesel (500 W)

3 The third option is given by a purely PV(3000 W) system

Table 7

Local weights matrix obtained for the high diesel price case study


Op1 018 026 023 030 030 034 035 034 038 047 043 026 043 033 033 033 033

Op2 032 064 012 016 016 036 033 033 038 047 043 064 014 033 053 033 033

Op3 050 010 065 054 054 030 032 033 023 005 014 010 043 033 014 033 033

Table 8Global weight vector obtained for the high diesel price case study


023 004 021 001 003 015 003 003 001 005 001 001 001 000 002 003 013

0 20 40 60 80 100 120 1400

05

1

15

2

0 20 40 60 80 100 120 14060

70

80

90

100

Fig 10 Time simulation results hybrid system operating during a week for the

second case study problem

02 04 06 08 1 12 14 16200

400

600

800

1000

1200

1400

1600

1800

Fig 11 PV and wind systems sizes vs diesel prices for each scenario




The 1047297nal weight vector gives a 02835 weight for the 1047297rst

04093 for the second and 03072 for the third option The selected

solution is the second option the mix with high PV and wind

participation The storage device obtained for all optimal solutions

is a string of 6 batteries (456 kWh) with a 1 kW power inverter

Fig 11 shows the evolution of PV and wind installation sizes for

the selected solution as a function of the diesel prices considered

in the different scenarios presented in this paperIt can be observed that for the different considerations used in

the pair-wise matrix comparisons at all levels obtained results for

higher diesel prices tend to show an overall increased PV penetra-

tion ratio while for the low diesel price scenarios the selection of

wind power is privileged over PV More importantly the results

show the importance of considering social and environmental

aspects This is shown in the case of a high penetration of PV or

wind when the 1047297nal selected solution is not necessarily the result

of the technical and economical optimization

4 Conclusion

In this paper a hierarchical planning methodology for the

integral net energy design of small-scale hybrid renewable energysystems using ARE sources is proposed The approach considers

technicalndasheconomical as well as societalndashenvironmental aspects in

two different decision layers One of the main features of the

methodology is to complement the classical methodology that

considers the optimization and levelized cost analysis by using the

EROEI or net energy concept and the AHP method for decision

making problems to ensure the consideration of complex and

unconventional aspects such as social and environmental para-

meters in the design of the hybrid ARE systems The methodology

is evaluated using an application example in Margarita Island

under several simulation scenarios low intermediate and high

diesel fuel price cases The obtained results show that the aspects

considered in the proposed methodology have an in1047298uence on the

initial results obtained by using the classical approach of levelizedcost optimization

Acknowledgements

The authors would like to acknowledge the 1047297nancial and

technical support of the Venezuelan Planning School (Escuela

Venezolana de Plani1047297cacioacuten)

Appendix A Pair-wise matrices for low diesel price case study

scenario

A2 Op1 Op2 Op3 Weights

Op1 1 13 13 01396

Op2 3 1 12 03325Op3 3 2 1 05278


Op1 1 3 2 05499

Op2 13 1 1 02098

Op3 12 1 1 02402

B1 Op1 Op2 Op3 Weights

Op1 1 13 2 02493

Op2 3 1 3 05936

Op3 12 13 1 01571


Op1 1 13 2 02493

Op2 3 1 3 05936

Op3 12 13 1 01571

D3 Op1 Op2 Op3 Weights

Op1 1 13 15 01047

Op2 3 1 13 02583Op3 5 3 1 06370

E1 Op1 Op2 Op3 Weights

Op1 1 3 3 06000

Op2 13 1 1 02000

Op3 13 1 1 02000


Op1 1 1 13 02000

Op2 1 1 13 02000Op3 3 3 1 06000

Matrices D2 E2 F1 and F2 are 3 3 all-ones matrices

Appendix B Pair-wise matrices for high diesel price case study

scenario


Op1 1 13 3 02583Op2 3 1 5 06370

Op3 13 15 1 01047


Op1 1 2 13 02297

Op2 12 1 15 01220

Op3 3 5 1 06483


Op1 1 2 12 02970

Op2 12 1 13 01634

Op3 2 3 1 05396


Op1 1 2 12 02970

Op2 12 1 13 01634Op3 2 3 1 05396


Op1 1 1 9 04737

Op2 1 1 9 04737

Op3 19 19 1 00526





Op1 1 1 3 04286Op2 1 1 3 04286

Op3 13 13 1 01429


Op1 1 13 3 02583

Op2 3 1 5 06370

Op3 13 15 1 01047


Op1 1 3 1 04286

Op2 13 1 13 01429Op3 1 3 1 04286


Op1 1 12 3 03325

Op2 2 1 3 05278

Op3 13 13 1 01396

Matrices E2 F1 and F2 are 3 3 all-ones matrices

References

[1] International Energy Agency and Organisation for Economic Co-operation andDevelopment Key world energy statistics 2014

[2] Howes T The EUs New Renewable Energy Directive (200928EC) The newclimate policies of the European Union internal legislation and climatediplomacy vol 5 2010 p 117

[3] Mudacumura GM Mebratu D Haque MS Sustainable development policy andadministration Public administration and public policy Boca Raton CRCTaylor and Francis 2006

[4] Gonzaacutelez-Longatt F Gonzaacutelez JS Payoacuten MB Santos JMR Wind-resource atlasof Venezuela based on on-site anemometry observation Renew SustainEnergy Rev 201439898ndash911

[5] Martenson C The crash course the unsustainable future of our economyenergy and environment John Wiley amp Sons Inc Hoboken NJ 2011

[6] Connolly D Lund H Mathiesen BV Leahy M A review of computer tools foranalysing the integration of renewable energy into various energy systemsAppl Energy 201087(4)1059ndash82

[7] Leng GJ Renewable energy technologies project assessment tool RETScreenVarennes Quebec CANMET Energy Diversi1047297cation Research Laboratory 1998

[8] Lilienthal P HOMER micropower optimization model Report National Renew-able Energy Laboratory (NREL) Golden CO 2005

[9] Saaty TL Fundamentals of decision making and priority theory with theanalytic hierarchy processmdashvol VI RWS Publications Pittsburgh PA 1994

[10] Pohekar SD Ramachandran M Application of multi-criteria decision makingto sustainable energy planningmdasha review Renew Sustain Energy Rev 20048(4)365ndash81

[11] Banos R Manzano-Agugliaro F Montoya FG Gil C Alcayde A Goacutemez JOptimization methods applied to renewable and sustainable energy a reviewRenew Sustain Energy Rev 201115(4)1753ndash66

[12] Taha RA Daim T Multi-criteria applications in renewable energy analysis aliterature review London Springer 2013 p 17ndash30

[13] Elkarmi F Mustafa I Increasing the utilization of solar-energy technologies

(set) in Jordanmdashanalytic hierarchy process Energy Policy 199321(9)978ndash82[14] Akash BA Mamlook R Mohsen MS Multi-criteria selection of electric power

plants using analytical hierarchy process Electr Power Syst Res 199952(1)29ndash35

[15] Wu ZX Wei ZH Mitigation assessment results and priorities for Chinasenergy sector Appl Energy 199756(3ndash4)237ndash51

[16] Wang YM Chin KS Fuzzy analytic hierarchy process a logarithmic fuzzypreference programming methodology Int J Approx Reason 201152(4)541ndash53

[17] Nigim K Munier N Green J Pre-feasibility MCDM tools to aid communities inprioritizing local viable renewable energy sources Renew Energy 200429(11)1775ndash91

[18] Daniel J Vishal NV Albert B Selvarsan I Evaluation of the signi 1047297cant renew-able energy resources in India using analytical hierarchy process LondonSpringer 2010 p 13ndash26

[19] Ahmad S Tahar RM Selection of renewable energy sources for sustainabledevelopment of electricity generation system using analytic hierarchy processa case of Malaysia Renew Energy 201463458ndash66

[20] Tasri A Susilawati A Selection among renewable energy alternatives based ona fuzzy analytic hierarchy process in Indonesia Sustain Energy Technol Assess2014734ndash44

[21] Kaya T Kahraman C Multicriteria renewable energy planning using anintegrated fuzzy VIKOR amp AHP methodology the case of Istanbul Energy2010352517ndash27

[22] Zakerinia M Ghaderi F Piltan M Optimal portfolio selection between differentkinds of renewable energy sources 2010

[23] Barin A Canha LN Abaide A Magnago K Wottrich B Multicriteria analysis of the operation of renewable energy sources taking as basis the AHP methodand fuzzy logic concerning distributed generation systems Online J ElectronElectric Eng (OJEEE) 20091(1)52ndash7

[24] Heo E Kim J Boo KJ Analysis of the assessment factors for renewable energydissemination program evaluation using fuzzy AHP Renew Sustain EnergyRev 201014(8)2214ndash20

[25] Akhgari P Kamalan H Monavari M Utilizing AHP method in rating of highlighted environment parameters in most used renewable energies inelectricity production Eur J Sci Res 201160182ndash8

[26] Murphy DJ Hall CAS Year in reviewmdashEROI or energy return on (energy)

invested Ecol Econ Rev 20101185102ndash18[27] Urdaneta AJ Chankong V A multiobjective minimax approach to controller

settings for systems running under disturbances Control-Theory Adv Technol19895(4)391ndash411

[28] Gonzaacutelez-Longatt F Teraacuten R Meacutendez J Hernaacutendez A Guilleacuten F Evaluacioacuten delRecurso Eoacutelico en Venezuela Parte I Congreso Petrolero Energeacutetico ASME-UNEFA Puerto Cabello 2006 [in Spanish]




had an important decrease the alternative renewable energy

(ARE) sources are signaled to play an increasingly more important

role in this diversi1047297cation process Renewable energy sources have

grown from 01 of total of primary energy in 1973 (6106 MToe) to

11 of 13 371 MToe in 2013 [1] This implies an increase of 2400

in 40 years according to International Energy Agency [1]

Several reasons can explain the emergence of renewable

energy as demography conventional energy scarcity equilibrium

of planetary resource use Climate change is a fact Traditionallythe role of ARE sources has been referred to as part of the solution

to global warming [2] Also 80 of global energy resources are

used by 20 of the population living in developed countries [3]

ARE sources are an instrument to address the development of

world population instead of using conventional energy resources

Some advantages of ARE source are their participation on

energy diversi1047297cation their modularity and the capability of

decentralized operation They may contribute to equitable geo-

graphical energy distribution in rural countries and of course ARE

source are environmental friendly when compared to some con-

ventional sources as fossil fuel In a regional context ARE sources

are expected to play a more important leading role since wind

energy sources show a signi1047297cant development potential [4] A

very interesting comparison includes the concept of net energy

where the energy ef 1047297ciency of the given sources is computed

namely a focus is given to ldquohow much energy is needed to produce

energyrdquo [5] In this net energy analysis ARE sources are well

placed when compared to hydrocarbon sources

The analysis of the important role that ARE sources may play

includes the following factors

Their participation in the energy diversi1047297cation process Inevitable participation in the national energy approach as

conventional source are becoming scarce (as big scale hydro-

power) oil pro1047297tability should be maximized through exports

or due to immediate unavailability of some resources at big

scale as in the case of natural gas Identi1047297cation of the politicalndashterritorial importance of ARE

installations on remote and isolated sites with special interestin strengthening the nationals developing axes

Active role of ARE source in network stability when considered

as distributed energy sources with migration towards smart

grids networks In1047298uence on the electricity balance and reduction of power

transmission limits with installation of ARE sources near

energy consumption centers

The development of ARE resources are strongly dependent on

energy decision makers preferences These decision makers are

generally energy stakeholders that participate in the energy

market as investors and operators In general ARE projects are

strongly encouraged by national or regional regulatory boards

with strong incentives in order to achieve some economy of scalein alternative generating technologies Within this context when a

energy decision maker prospects the implementation and use of

ARE sources an ef 1047297cient methodology for a technicalndasheconomical

selection is required

There exists in literature several contributions about how to select

an appropriate electrical generating scheme based upon ARE sources

A comprehensive review can be found in [6] with a survey of

methodologies and computational tools to address this problem We

will highlight two of them First the RETscreen platform [7] provides

an option for an initial analysis of technical and 1047297nancial viability of potential renewable energy Another well developed tool is HOMER

[8] which is able to simulate and 1047297nd the best selected option for

energy source con1047297gurations in a given project In this paper it is

considered that the HOMER tool is known by the reader however a

detailed user manual is given in [8]

However despite ongoing ARE evaluation methodologies have

strong strengths on system optimization of technical and eco-

nomic variables It is observed that social and environmental

aspects are hard to treat and valuate in these proposals Quantify-

ing and evaluating the social and environmental viability of a

project is a dif 1047297cult task The analytic hierarchy process (AHP) can

be applied as a complementary evaluation technique in order to

evaluate societal and environmental impact aspects of widespread

integration of ARE sources The methodology proposed and

developed in [9] gives the possibility of classifying the different

analysis criteria into a hierarchical model thus solving the

problem of selection between several solution alternatives The

use of a de1047297ned weighting system makes it easier to classify

alternatives an criteria entries by applying a pair-wise comparison

We can 1047297nd some background on AHP application for ARE

planning purposes Comprehensive reviews on AHP application

for ARE resource planning and project valuation can be found in

[10ndash12] In the following we discuss some of them An analytic

hierarchy process has been applied by [1314] for renewable

energy resources in Jordan The application of an AHP method

for Chinese ARE sources is found in [1516] In [1718] the use of the

AHP method is proposed for decision making and planning of ARE

generation projects in Canada and India respectively Similarly in

[1920] the AHP method is applied for renewable energy planningin Malaysia and Indonesia respectively An integrated VIKOR ndashAHP

methodology to the selection of the best energy policy and

production site is developed in [21] In the work presented in

[22] an integrated methodology is proposed combining linear

optimization and AHP The use of different advanced techniques

along with the AHP method has also been formulated in the

literature In [2324] fuzzy logic is used for the AHP approach for

an ARE application in Brazil and Korea respectively An analysis of

the energy return on energy invested impact of ARE sources to the

environment is presented in [25]

This paper presents an integral approach that considers a

technicalndasheconomical analysis for ARE sources technology selec-

tion but also societalndashenvironmental aspects are considered dur-

ing the analysis of the project considering the net energy or EROEIconcept [26] The use of a two level analytic hierarchy process

Fig 1 Proposed methodology





















considered





















selection process




studied problem





















Op1 1 3 5 06483

Op2 13 1 2 02297

Op3 15 12 1 01220





Table 1









1 15 2 25 3 35

0

001

002

003

004

005

006








eth1THORN










CR frac14CI

RI eth3THORN


















technology













problems





given in Fig 4














expended













Table 2


Size of M 1 2 3 4 5


Size of M 6 7 8 9 10

Random index 124 132 141 145 149
















ratio from Eq (3)


TecEconAsp 1 3 075

SocEnvAsp 13 1 025





DEcolImp 1 3 13 02684

EEduPot 13 1 14 01172



A1 1 7 1 04869

A2 17 1 15 00778

A3 1 5 1 04353


B1 1 13 19 00704

B2 3 1 15 01782

B3 9 5 1 07514


C1 1 1 3 04286

C2 1 1 3 04286

C3 13 13 1 01429


D1 1 5 7 07306D2 15 1 3 01884

D3 17 13 1 00810


E1 1 2 13 02493

E2 12 1 13 01571E3 3 3 1 05936


F1 1 15 01667

F2 5 1 08333




3 Study case





























community








































results













S4KS25P 76 kWh (4 V 1900 Ah)




















Table 3



Option 1 14 021 569 0301

Option 2 14 698 602 0315

Option 3 14 826 514 0318



















Option 1 03413

Option 2 03247

Option 3 03340


Table 4



Op1 023 014 055 025 025 033 034 034 033 033 033 010 060 033 020 033 033

Op2 038 033 021 059 059 031 033 033 032 034 033 026 020 033 020 033 033

Op3 039 053 024 016 016 036 033 033 035 033 033 064 020 033 060 033 033

Table 5



023 004 021 001 003 015 003 003 001 005 001 001 001 000 002 003 013
















production










0 20 40 60 80 100 120 1400

05

1

15

0 20 40 60 80 100 120 14040

60

80

100


Table 6



Option 1 25 896 383 0590

Option 2 28 408 384 0629

Option 3 29 469 878 0632























Option 1 02834

Option 2 03056

Option 3 04110





























wind (400 W)




Table 7



Op1 018 026 023 030 030 034 035 034 038 047 043 026 043 033 033 033 033

Op2 032 064 012 016 016 036 033 033 038 047 043 064 014 033 053 033 033

Op3 050 010 065 054 054 030 032 033 023 005 014 010 043 033 014 033 033



023 004 021 001 003 015 003 003 001 005 001 001 001 000 002 003 013

0 20 40 60 80 100 120 1400

05

1

15

2

0 20 40 60 80 100 120 14060

70

80

90

100



02 04 06 08 1 12 14 16200

400

600

800

1000

1200

1400

1600

1800





















4 Conclusion
















Acknowledgements





scenario


Op1 1 13 13 01396

Op2 3 1 12 03325Op3 3 2 1 05278


Op1 1 3 2 05499

Op2 13 1 1 02098

Op3 12 1 1 02402


Op1 1 13 2 02493

Op2 3 1 3 05936

Op3 12 13 1 01571


Op1 1 13 2 02493

Op2 3 1 3 05936

Op3 12 13 1 01571


Op1 1 13 15 01047

Op2 3 1 13 02583Op3 5 3 1 06370


Op1 1 3 3 06000

Op2 13 1 1 02000

Op3 13 1 1 02000


Op1 1 1 13 02000

Op2 1 1 13 02000Op3 3 3 1 06000



scenario


Op1 1 13 3 02583Op2 3 1 5 06370

Op3 13 15 1 01047


Op1 1 2 13 02297

Op2 12 1 15 01220

Op3 3 5 1 06483


Op1 1 2 12 02970

Op2 12 1 13 01634

Op3 2 3 1 05396


Op1 1 2 12 02970

Op2 12 1 13 01634Op3 2 3 1 05396


Op1 1 1 9 04737

Op2 1 1 9 04737

Op3 19 19 1 00526





Op1 1 1 3 04286Op2 1 1 3 04286

Op3 13 13 1 01429


Op1 1 13 3 02583

Op2 3 1 5 06370

Op3 13 15 1 01047


Op1 1 3 1 04286

Op2 13 1 13 01429Op3 1 3 1 04286


Op1 1 12 3 03325

Op2 2 1 3 05278

Op3 13 13 1 01396


References



















































considered





















selection process




studied problem





















Op1 1 3 5 06483

Op2 13 1 2 02297

Op3 15 12 1 01220





Table 1









1 15 2 25 3 35

0

001

002

003

004

005

006








eth1THORN










CR frac14CI

RI eth3THORN


















technology













problems





given in Fig 4














expended













Table 2


Size of M 1 2 3 4 5


Size of M 6 7 8 9 10

Random index 124 132 141 145 149
















ratio from Eq (3)


TecEconAsp 1 3 075

SocEnvAsp 13 1 025





DEcolImp 1 3 13 02684

EEduPot 13 1 14 01172



A1 1 7 1 04869

A2 17 1 15 00778

A3 1 5 1 04353


B1 1 13 19 00704

B2 3 1 15 01782

B3 9 5 1 07514


C1 1 1 3 04286

C2 1 1 3 04286

C3 13 13 1 01429


D1 1 5 7 07306D2 15 1 3 01884

D3 17 13 1 00810


E1 1 2 13 02493

E2 12 1 13 01571E3 3 3 1 05936


F1 1 15 01667

F2 5 1 08333




3 Study case





























community








































results













S4KS25P 76 kWh (4 V 1900 Ah)




















Table 3



Option 1 14 021 569 0301

Option 2 14 698 602 0315

Option 3 14 826 514 0318



















Option 1 03413

Option 2 03247

Option 3 03340


Table 4



Op1 023 014 055 025 025 033 034 034 033 033 033 010 060 033 020 033 033

Op2 038 033 021 059 059 031 033 033 032 034 033 026 020 033 020 033 033

Op3 039 053 024 016 016 036 033 033 035 033 033 064 020 033 060 033 033

Table 5



023 004 021 001 003 015 003 003 001 005 001 001 001 000 002 003 013
















production










0 20 40 60 80 100 120 1400

05

1

15

0 20 40 60 80 100 120 14040

60

80

100


Table 6



Option 1 25 896 383 0590

Option 2 28 408 384 0629

Option 3 29 469 878 0632























Option 1 02834

Option 2 03056

Option 3 04110





























wind (400 W)




Table 7



Op1 018 026 023 030 030 034 035 034 038 047 043 026 043 033 033 033 033

Op2 032 064 012 016 016 036 033 033 038 047 043 064 014 033 053 033 033

Op3 050 010 065 054 054 030 032 033 023 005 014 010 043 033 014 033 033



023 004 021 001 003 015 003 003 001 005 001 001 001 000 002 003 013

0 20 40 60 80 100 120 1400

05

1

15

2

0 20 40 60 80 100 120 14060

70

80

90

100



02 04 06 08 1 12 14 16200

400

600

800

1000

1200

1400

1600

1800





















4 Conclusion
















Acknowledgements





scenario


Op1 1 13 13 01396

Op2 3 1 12 03325Op3 3 2 1 05278


Op1 1 3 2 05499

Op2 13 1 1 02098

Op3 12 1 1 02402


Op1 1 13 2 02493

Op2 3 1 3 05936

Op3 12 13 1 01571


Op1 1 13 2 02493

Op2 3 1 3 05936

Op3 12 13 1 01571


Op1 1 13 15 01047

Op2 3 1 13 02583Op3 5 3 1 06370


Op1 1 3 3 06000

Op2 13 1 1 02000

Op3 13 1 1 02000


Op1 1 1 13 02000

Op2 1 1 13 02000Op3 3 3 1 06000



scenario


Op1 1 13 3 02583Op2 3 1 5 06370

Op3 13 15 1 01047


Op1 1 2 13 02297

Op2 12 1 15 01220

Op3 3 5 1 06483


Op1 1 2 12 02970

Op2 12 1 13 01634

Op3 2 3 1 05396


Op1 1 2 12 02970

Op2 12 1 13 01634Op3 2 3 1 05396


Op1 1 1 9 04737

Op2 1 1 9 04737

Op3 19 19 1 00526





Op1 1 1 3 04286Op2 1 1 3 04286

Op3 13 13 1 01429


Op1 1 13 3 02583

Op2 3 1 5 06370

Op3 13 15 1 01047


Op1 1 3 1 04286

Op2 13 1 13 01429Op3 1 3 1 04286


Op1 1 12 3 03325

Op2 2 1 3 05278

Op3 13 13 1 01396


References





































eth1THORN










CR frac14CI

RI eth3THORN


















technology













problems





given in Fig 4














expended













Table 2


Size of M 1 2 3 4 5


Size of M 6 7 8 9 10

Random index 124 132 141 145 149
















ratio from Eq (3)


TecEconAsp 1 3 075

SocEnvAsp 13 1 025





DEcolImp 1 3 13 02684

EEduPot 13 1 14 01172



A1 1 7 1 04869

A2 17 1 15 00778

A3 1 5 1 04353


B1 1 13 19 00704

B2 3 1 15 01782

B3 9 5 1 07514


C1 1 1 3 04286

C2 1 1 3 04286

C3 13 13 1 01429


D1 1 5 7 07306D2 15 1 3 01884

D3 17 13 1 00810


E1 1 2 13 02493

E2 12 1 13 01571E3 3 3 1 05936


F1 1 15 01667

F2 5 1 08333




3 Study case





























community








































results













S4KS25P 76 kWh (4 V 1900 Ah)




















Table 3



Option 1 14 021 569 0301

Option 2 14 698 602 0315

Option 3 14 826 514 0318



















Option 1 03413

Option 2 03247

Option 3 03340


Table 4



Op1 023 014 055 025 025 033 034 034 033 033 033 010 060 033 020 033 033

Op2 038 033 021 059 059 031 033 033 032 034 033 026 020 033 020 033 033

Op3 039 053 024 016 016 036 033 033 035 033 033 064 020 033 060 033 033

Table 5



023 004 021 001 003 015 003 003 001 005 001 001 001 000 002 003 013
















production










0 20 40 60 80 100 120 1400

05

1

15

0 20 40 60 80 100 120 14040

60

80

100


Table 6



Option 1 25 896 383 0590

Option 2 28 408 384 0629

Option 3 29 469 878 0632























Option 1 02834

Option 2 03056

Option 3 04110





























wind (400 W)




Table 7



Op1 018 026 023 030 030 034 035 034 038 047 043 026 043 033 033 033 033

Op2 032 064 012 016 016 036 033 033 038 047 043 064 014 033 053 033 033

Op3 050 010 065 054 054 030 032 033 023 005 014 010 043 033 014 033 033



023 004 021 001 003 015 003 003 001 005 001 001 001 000 002 003 013

0 20 40 60 80 100 120 1400

05

1

15

2

0 20 40 60 80 100 120 14060

70

80

90

100



02 04 06 08 1 12 14 16200

400

600

800

1000

1200

1400

1600

1800





















4 Conclusion
















Acknowledgements





scenario


Op1 1 13 13 01396

Op2 3 1 12 03325Op3 3 2 1 05278


Op1 1 3 2 05499

Op2 13 1 1 02098

Op3 12 1 1 02402


Op1 1 13 2 02493

Op2 3 1 3 05936

Op3 12 13 1 01571


Op1 1 13 2 02493

Op2 3 1 3 05936

Op3 12 13 1 01571


Op1 1 13 15 01047

Op2 3 1 13 02583Op3 5 3 1 06370


Op1 1 3 3 06000

Op2 13 1 1 02000

Op3 13 1 1 02000


Op1 1 1 13 02000

Op2 1 1 13 02000Op3 3 3 1 06000



scenario


Op1 1 13 3 02583Op2 3 1 5 06370

Op3 13 15 1 01047


Op1 1 2 13 02297

Op2 12 1 15 01220

Op3 3 5 1 06483


Op1 1 2 12 02970

Op2 12 1 13 01634

Op3 2 3 1 05396


Op1 1 2 12 02970

Op2 12 1 13 01634Op3 2 3 1 05396


Op1 1 1 9 04737

Op2 1 1 9 04737

Op3 19 19 1 00526





Op1 1 1 3 04286Op2 1 1 3 04286

Op3 13 13 1 01429


Op1 1 13 3 02583

Op2 3 1 5 06370

Op3 13 15 1 01047


Op1 1 3 1 04286

Op2 13 1 13 01429Op3 1 3 1 04286


Op1 1 12 3 03325

Op2 2 1 3 05278

Op3 13 13 1 01396


References













































ratio from Eq (3)


TecEconAsp 1 3 075

SocEnvAsp 13 1 025





DEcolImp 1 3 13 02684

EEduPot 13 1 14 01172



A1 1 7 1 04869

A2 17 1 15 00778

A3 1 5 1 04353


B1 1 13 19 00704

B2 3 1 15 01782

B3 9 5 1 07514


C1 1 1 3 04286

C2 1 1 3 04286

C3 13 13 1 01429


D1 1 5 7 07306D2 15 1 3 01884

D3 17 13 1 00810


E1 1 2 13 02493

E2 12 1 13 01571E3 3 3 1 05936


F1 1 15 01667

F2 5 1 08333




3 Study case





























community








































results













S4KS25P 76 kWh (4 V 1900 Ah)




















Table 3



Option 1 14 021 569 0301

Option 2 14 698 602 0315

Option 3 14 826 514 0318



















Option 1 03413

Option 2 03247

Option 3 03340


Table 4



Op1 023 014 055 025 025 033 034 034 033 033 033 010 060 033 020 033 033

Op2 038 033 021 059 059 031 033 033 032 034 033 026 020 033 020 033 033

Op3 039 053 024 016 016 036 033 033 035 033 033 064 020 033 060 033 033

Table 5



023 004 021 001 003 015 003 003 001 005 001 001 001 000 002 003 013
















production










0 20 40 60 80 100 120 1400

05

1

15

0 20 40 60 80 100 120 14040

60

80

100


Table 6



Option 1 25 896 383 0590

Option 2 28 408 384 0629

Option 3 29 469 878 0632























Option 1 02834

Option 2 03056

Option 3 04110





























wind (400 W)




Table 7



Op1 018 026 023 030 030 034 035 034 038 047 043 026 043 033 033 033 033

Op2 032 064 012 016 016 036 033 033 038 047 043 064 014 033 053 033 033

Op3 050 010 065 054 054 030 032 033 023 005 014 010 043 033 014 033 033



023 004 021 001 003 015 003 003 001 005 001 001 001 000 002 003 013

0 20 40 60 80 100 120 1400

05

1

15

2

0 20 40 60 80 100 120 14060

70

80

90

100



02 04 06 08 1 12 14 16200

400

600

800

1000

1200

1400

1600

1800





















4 Conclusion
















Acknowledgements





scenario


Op1 1 13 13 01396

Op2 3 1 12 03325Op3 3 2 1 05278


Op1 1 3 2 05499

Op2 13 1 1 02098

Op3 12 1 1 02402


Op1 1 13 2 02493

Op2 3 1 3 05936

Op3 12 13 1 01571


Op1 1 13 2 02493

Op2 3 1 3 05936

Op3 12 13 1 01571


Op1 1 13 15 01047

Op2 3 1 13 02583Op3 5 3 1 06370


Op1 1 3 3 06000

Op2 13 1 1 02000

Op3 13 1 1 02000


Op1 1 1 13 02000

Op2 1 1 13 02000Op3 3 3 1 06000



scenario


Op1 1 13 3 02583Op2 3 1 5 06370

Op3 13 15 1 01047


Op1 1 2 13 02297

Op2 12 1 15 01220

Op3 3 5 1 06483


Op1 1 2 12 02970

Op2 12 1 13 01634

Op3 2 3 1 05396


Op1 1 2 12 02970

Op2 12 1 13 01634Op3 2 3 1 05396


Op1 1 1 9 04737

Op2 1 1 9 04737

Op3 19 19 1 00526





Op1 1 1 3 04286Op2 1 1 3 04286

Op3 13 13 1 01429


Op1 1 13 3 02583

Op2 3 1 5 06370

Op3 13 15 1 01047


Op1 1 3 1 04286

Op2 13 1 13 01429Op3 1 3 1 04286


Op1 1 12 3 03325

Op2 2 1 3 05278

Op3 13 13 1 01396


References




































































results













S4KS25P 76 kWh (4 V 1900 Ah)




















Table 3



Option 1 14 021 569 0301

Option 2 14 698 602 0315

Option 3 14 826 514 0318



















Option 1 03413

Option 2 03247

Option 3 03340


Table 4



Op1 023 014 055 025 025 033 034 034 033 033 033 010 060 033 020 033 033

Op2 038 033 021 059 059 031 033 033 032 034 033 026 020 033 020 033 033

Op3 039 053 024 016 016 036 033 033 035 033 033 064 020 033 060 033 033

Table 5



023 004 021 001 003 015 003 003 001 005 001 001 001 000 002 003 013
















production










0 20 40 60 80 100 120 1400

05

1

15

0 20 40 60 80 100 120 14040

60

80

100


Table 6



Option 1 25 896 383 0590

Option 2 28 408 384 0629

Option 3 29 469 878 0632























Option 1 02834

Option 2 03056

Option 3 04110





























wind (400 W)




Table 7



Op1 018 026 023 030 030 034 035 034 038 047 043 026 043 033 033 033 033

Op2 032 064 012 016 016 036 033 033 038 047 043 064 014 033 053 033 033

Op3 050 010 065 054 054 030 032 033 023 005 014 010 043 033 014 033 033



023 004 021 001 003 015 003 003 001 005 001 001 001 000 002 003 013

0 20 40 60 80 100 120 1400

05

1

15

2

0 20 40 60 80 100 120 14060

70

80

90

100



02 04 06 08 1 12 14 16200

400

600

800

1000

1200

1400

1600

1800





















4 Conclusion
















Acknowledgements





scenario


Op1 1 13 13 01396

Op2 3 1 12 03325Op3 3 2 1 05278


Op1 1 3 2 05499

Op2 13 1 1 02098

Op3 12 1 1 02402


Op1 1 13 2 02493

Op2 3 1 3 05936

Op3 12 13 1 01571


Op1 1 13 2 02493

Op2 3 1 3 05936

Op3 12 13 1 01571


Op1 1 13 15 01047

Op2 3 1 13 02583Op3 5 3 1 06370


Op1 1 3 3 06000

Op2 13 1 1 02000

Op3 13 1 1 02000


Op1 1 1 13 02000

Op2 1 1 13 02000Op3 3 3 1 06000



scenario


Op1 1 13 3 02583Op2 3 1 5 06370

Op3 13 15 1 01047


Op1 1 2 13 02297

Op2 12 1 15 01220

Op3 3 5 1 06483


Op1 1 2 12 02970

Op2 12 1 13 01634

Op3 2 3 1 05396


Op1 1 2 12 02970

Op2 12 1 13 01634Op3 2 3 1 05396


Op1 1 1 9 04737

Op2 1 1 9 04737

Op3 19 19 1 00526





Op1 1 1 3 04286Op2 1 1 3 04286

Op3 13 13 1 01429


Op1 1 13 3 02583

Op2 3 1 5 06370

Op3 13 15 1 01047


Op1 1 3 1 04286

Op2 13 1 13 01429Op3 1 3 1 04286


Op1 1 12 3 03325

Op2 2 1 3 05278

Op3 13 13 1 01396


References

















































Option 1 03413

Option 2 03247

Option 3 03340


Table 4



Op1 023 014 055 025 025 033 034 034 033 033 033 010 060 033 020 033 033

Op2 038 033 021 059 059 031 033 033 032 034 033 026 020 033 020 033 033

Op3 039 053 024 016 016 036 033 033 035 033 033 064 020 033 060 033 033

Table 5



023 004 021 001 003 015 003 003 001 005 001 001 001 000 002 003 013
















production










0 20 40 60 80 100 120 1400

05

1

15

0 20 40 60 80 100 120 14040

60

80

100


Table 6



Option 1 25 896 383 0590

Option 2 28 408 384 0629

Option 3 29 469 878 0632























Option 1 02834

Option 2 03056

Option 3 04110





























wind (400 W)




Table 7



Op1 018 026 023 030 030 034 035 034 038 047 043 026 043 033 033 033 033

Op2 032 064 012 016 016 036 033 033 038 047 043 064 014 033 053 033 033

Op3 050 010 065 054 054 030 032 033 023 005 014 010 043 033 014 033 033



023 004 021 001 003 015 003 003 001 005 001 001 001 000 002 003 013

0 20 40 60 80 100 120 1400

05

1

15

2

0 20 40 60 80 100 120 14060

70

80

90

100



02 04 06 08 1 12 14 16200

400

600

800

1000

1200

1400

1600

1800





















4 Conclusion
















Acknowledgements





scenario


Op1 1 13 13 01396

Op2 3 1 12 03325Op3 3 2 1 05278


Op1 1 3 2 05499

Op2 13 1 1 02098

Op3 12 1 1 02402


Op1 1 13 2 02493

Op2 3 1 3 05936

Op3 12 13 1 01571


Op1 1 13 2 02493

Op2 3 1 3 05936

Op3 12 13 1 01571


Op1 1 13 15 01047

Op2 3 1 13 02583Op3 5 3 1 06370


Op1 1 3 3 06000

Op2 13 1 1 02000

Op3 13 1 1 02000


Op1 1 1 13 02000

Op2 1 1 13 02000Op3 3 3 1 06000



scenario


Op1 1 13 3 02583Op2 3 1 5 06370

Op3 13 15 1 01047


Op1 1 2 13 02297

Op2 12 1 15 01220

Op3 3 5 1 06483


Op1 1 2 12 02970

Op2 12 1 13 01634

Op3 2 3 1 05396


Op1 1 2 12 02970

Op2 12 1 13 01634Op3 2 3 1 05396


Op1 1 1 9 04737

Op2 1 1 9 04737

Op3 19 19 1 00526





Op1 1 1 3 04286Op2 1 1 3 04286

Op3 13 13 1 01429


Op1 1 13 3 02583

Op2 3 1 5 06370

Op3 13 15 1 01047


Op1 1 3 1 04286

Op2 13 1 13 01429Op3 1 3 1 04286


Op1 1 12 3 03325

Op2 2 1 3 05278

Op3 13 13 1 01396


References














































production










0 20 40 60 80 100 120 1400

05

1

15

0 20 40 60 80 100 120 14040

60

80

100


Table 6



Option 1 25 896 383 0590

Option 2 28 408 384 0629

Option 3 29 469 878 0632























Option 1 02834

Option 2 03056

Option 3 04110





























wind (400 W)




Table 7



Op1 018 026 023 030 030 034 035 034 038 047 043 026 043 033 033 033 033

Op2 032 064 012 016 016 036 033 033 038 047 043 064 014 033 053 033 033

Op3 050 010 065 054 054 030 032 033 023 005 014 010 043 033 014 033 033



023 004 021 001 003 015 003 003 001 005 001 001 001 000 002 003 013

0 20 40 60 80 100 120 1400

05

1

15

2

0 20 40 60 80 100 120 14060

70

80

90

100



02 04 06 08 1 12 14 16200

400

600

800

1000

1200

1400

1600

1800





















4 Conclusion
















Acknowledgements





scenario


Op1 1 13 13 01396

Op2 3 1 12 03325Op3 3 2 1 05278


Op1 1 3 2 05499

Op2 13 1 1 02098

Op3 12 1 1 02402


Op1 1 13 2 02493

Op2 3 1 3 05936

Op3 12 13 1 01571


Op1 1 13 2 02493

Op2 3 1 3 05936

Op3 12 13 1 01571


Op1 1 13 15 01047

Op2 3 1 13 02583Op3 5 3 1 06370


Op1 1 3 3 06000

Op2 13 1 1 02000

Op3 13 1 1 02000


Op1 1 1 13 02000

Op2 1 1 13 02000Op3 3 3 1 06000



scenario


Op1 1 13 3 02583Op2 3 1 5 06370

Op3 13 15 1 01047


Op1 1 2 13 02297

Op2 12 1 15 01220

Op3 3 5 1 06483


Op1 1 2 12 02970

Op2 12 1 13 01634

Op3 2 3 1 05396


Op1 1 2 12 02970

Op2 12 1 13 01634Op3 2 3 1 05396


Op1 1 1 9 04737

Op2 1 1 9 04737

Op3 19 19 1 00526





Op1 1 1 3 04286Op2 1 1 3 04286

Op3 13 13 1 01429


Op1 1 13 3 02583

Op2 3 1 5 06370

Op3 13 15 1 01047


Op1 1 3 1 04286

Op2 13 1 13 01429Op3 1 3 1 04286


Op1 1 12 3 03325

Op2 2 1 3 05278

Op3 13 13 1 01396


References




















































Option 1 02834

Option 2 03056

Option 3 04110





























wind (400 W)




Table 7



Op1 018 026 023 030 030 034 035 034 038 047 043 026 043 033 033 033 033

Op2 032 064 012 016 016 036 033 033 038 047 043 064 014 033 053 033 033

Op3 050 010 065 054 054 030 032 033 023 005 014 010 043 033 014 033 033



023 004 021 001 003 015 003 003 001 005 001 001 001 000 002 003 013

0 20 40 60 80 100 120 1400

05

1

15

2

0 20 40 60 80 100 120 14060

70

80

90

100



02 04 06 08 1 12 14 16200

400

600

800

1000

1200

1400

1600

1800





















4 Conclusion
















Acknowledgements





scenario


Op1 1 13 13 01396

Op2 3 1 12 03325Op3 3 2 1 05278


Op1 1 3 2 05499

Op2 13 1 1 02098

Op3 12 1 1 02402


Op1 1 13 2 02493

Op2 3 1 3 05936

Op3 12 13 1 01571


Op1 1 13 2 02493

Op2 3 1 3 05936

Op3 12 13 1 01571


Op1 1 13 15 01047

Op2 3 1 13 02583Op3 5 3 1 06370


Op1 1 3 3 06000

Op2 13 1 1 02000

Op3 13 1 1 02000


Op1 1 1 13 02000

Op2 1 1 13 02000Op3 3 3 1 06000



scenario


Op1 1 13 3 02583Op2 3 1 5 06370

Op3 13 15 1 01047


Op1 1 2 13 02297

Op2 12 1 15 01220

Op3 3 5 1 06483


Op1 1 2 12 02970

Op2 12 1 13 01634

Op3 2 3 1 05396


Op1 1 2 12 02970

Op2 12 1 13 01634Op3 2 3 1 05396


Op1 1 1 9 04737

Op2 1 1 9 04737

Op3 19 19 1 00526





Op1 1 1 3 04286Op2 1 1 3 04286

Op3 13 13 1 01429


Op1 1 13 3 02583

Op2 3 1 5 06370

Op3 13 15 1 01047


Op1 1 3 1 04286

Op2 13 1 13 01429Op3 1 3 1 04286


Op1 1 12 3 03325

Op2 2 1 3 05278

Op3 13 13 1 01396


References


















































4 Conclusion
















Acknowledgements





scenario


Op1 1 13 13 01396

Op2 3 1 12 03325Op3 3 2 1 05278


Op1 1 3 2 05499

Op2 13 1 1 02098

Op3 12 1 1 02402


Op1 1 13 2 02493

Op2 3 1 3 05936

Op3 12 13 1 01571


Op1 1 13 2 02493

Op2 3 1 3 05936

Op3 12 13 1 01571


Op1 1 13 15 01047

Op2 3 1 13 02583Op3 5 3 1 06370


Op1 1 3 3 06000

Op2 13 1 1 02000

Op3 13 1 1 02000


Op1 1 1 13 02000

Op2 1 1 13 02000Op3 3 3 1 06000



scenario


Op1 1 13 3 02583Op2 3 1 5 06370

Op3 13 15 1 01047


Op1 1 2 13 02297

Op2 12 1 15 01220

Op3 3 5 1 06483


Op1 1 2 12 02970

Op2 12 1 13 01634

Op3 2 3 1 05396


Op1 1 2 12 02970

Op2 12 1 13 01634Op3 2 3 1 05396


Op1 1 1 9 04737

Op2 1 1 9 04737

Op3 19 19 1 00526





Op1 1 1 3 04286Op2 1 1 3 04286

Op3 13 13 1 01429


Op1 1 13 3 02583

Op2 3 1 5 06370

Op3 13 15 1 01047


Op1 1 3 1 04286

Op2 13 1 13 01429Op3 1 3 1 04286


Op1 1 12 3 03325

Op2 2 1 3 05278

Op3 13 13 1 01396


References


































Op1 1 1 3 04286Op2 1 1 3 04286

Op3 13 13 1 01429


Op1 1 13 3 02583

Op2 3 1 5 06370

Op3 13 15 1 01047


Op1 1 3 1 04286

Op2 13 1 13 01429Op3 1 3 1 04286


Op1 1 12 3 03325

Op2 2 1 3 05278

Op3 13 13 1 01396


References
































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