e-ISSN: 1390-5902

CEDAMAZ, Vol. 15, No. 1, pp. 19–26, Enero-Junio 2025

DOI: 10.54753/cedamaz.v15i1.2494

Spatial Multi-Criteria Assessment for Optimal Biomass Power Plant Siting in

Ecuador Using GIS

Evaluación espacial multicriterio para la localización óptima de plantas de bioenergía

en Ecuador usando SIG

María Eliza Vega-Iñiguez 1, Yulissa del Cisne Marín-Apolo 1and Danny Ochoa-Correa 1,*

1Universidad de Cuenca, Cuenca, Ecuador, eliza.vegai@ucuenca.edu.ec, yulissa.marin@ucuenca.edu.ec, danny.ochoac@ucuenca.edu.ec

*Corresponding author: danny.ochoac@ucuenca.edu.ec

Reception date of the manuscript: 25/05/2025 Acceptance date of the manuscript: 30/06/2025 Publication date: 30/06/2025

Abstract— This study examines the energy and spatial potential of agricultural residues in Ecuador for biomass-based distributed electri-

city generation. Based on national crop production data, residues from sugarcane, rice, oil palm, and hard corn were quantiﬁed, yielding an

estimated effective electrical potential of 2407.68 GWh/year. A spatial multi-criteria analysis was conducted using Geographic Information

System (GIS) software, integrating road and substation proximity, terrain slope, ﬂood risk, and distance to populated areas. Exclusion layers

considered volcanic hazard zones, primary road corridors, and hydrographic basins. The resulting suitability map classiﬁed the territory into

ﬁve levels. Guayas, Los Ríos, and Esmeraldas were identiﬁed as provinces combining both resource availability and logistical accessibility.

Additionally, a levelized cost of electricity (LCOE) of USD 0.097/kWh was estimated for a 25 MW biomass plant. The methodology is

applicable to geographic prospecting and energy potential assessment of other non-conventional renewable energy sources.

Keywords—Bioenergy, Multi-criteria analysis, Agricultural biomass, SIG, Ecuador.

Resumen— Este estudio evalúa el potencial energético y territorial de los residuos agrícolas en Ecuador para la generación eléctrica

descentralizada a partir de biomasa. A partir de datos de producción nacional, se cuantiﬁcaron los residuos disponibles de caña de azúcar,

arroz, palma aceitera y maíz duro seco, estimando un potencial eléctrico efectivo de 2407.68 GWh/año. Se aplicó un análisis espacial

multicriterio en un sistema de información geográﬁca (SIG), integrando variables como proximidad a carreteras y subestaciones, pendiente

del terreno, riesgo de inundaciones y distancia a zonas pobladas. Se excluyeron áreas con restricciones como zonas de amenaza volcánica,

vías principales y cuencas hidrográﬁcas. El análisis produjo un mapa nacional de idoneidad, clasiﬁcando el territorio en cinco niveles. Las

provincias de Guayas, Los Ríos y Esmeraldas concentran condiciones favorables tanto en disponibilidad de biomasa como en accesibilidad

logística. Además, se estimó un costo nivelado de electricidad (LCOE) de 0.097 USD/kWh para una planta de 25 MW. La metodología

propuesta es aplicable a estudios orientados a la prospección territorial y a la evaluación del potencial energético de otras fuentes renovables

no convencionales.

Palabras clave—Bioenergía, Análisis multicriterio, Biomasa agrícola, GIS, Ecuador.

INTRODUCTION

Biomass energy has been explored globally as a way to di-

versify electricity generation sources, particularly in contexts

where agricultural residues are abundant. Unlike other rene-

wable sources that depend on intermittent natural phenome-

na, biomass allows for dispatchable generation through ther-

mochemical conversion of organic matter. In countries with

strong agricultural economies, this approach presents a tech-

nically viable method to utilize residual matter that would

otherwise be discarded or inefﬁciently managed (Demirbas,

2001; Zafar, 2022).

In Latin America, Brazil and Colombia have integrated

biomass into their energy planning, beneﬁting from sugar-

cane bagasse and palm residues, respectively (International

Renewable Energy Agency (IRENA), 2016; Marín-Apolo y

cols., 2025). However, Ecuador has yet to exploit this path-

way, despite possessing a similar agricultural structure and

residue availability. According to national energy statistics,

the country generated 63.45% of its electricity from hy-

dropower in 2024, while biomass contributed approximately

1.29% to the overall mix (Agencia de Regulación y Con-

trol de Energía y Recursos Naturales no Renovables (ARCO-

NEL), 2024). This reliance on hydroelectric generation intro-

duces seasonal vulnerabilities, particularly during prolonged

dry periods associated with climate variability, as evidenced

by energy shortages in 2022 and 2023.

Given this context, agricultural biomass—especially from

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OPTIMAL BIOMASS POWER PLANT SITING IN ECUADOR VEGA-IÑIGUEZ et al.

high-yield crops such as oil palm (Elaeis guineensis), rice,

sugarcane, and hard corn—represents a strategic opportunity

for decentralized electricity generation. These crops produ-

ce residues with acceptable moisture and energy content for

direct combustion, with annual production distributed across

various provinces. Prior studies in Ecuador have conﬁrmed

the physicochemical potential of these materials, but have

not addressed their spatial distribution or siting feasibility

(Peláez-Samaniego y Espinoza Abad, 2015; Chamba Que-

zada y Gómez Live, 2020).

The effectiveness of biomass-based generation systems

depends on the availability of feedstock and on the siting

of processing facilities. Factors such as proximity to road

infrastructure, slope, access to substations, and exposure to

ﬂood-prone areas affect both construction costs and opera-

tional logistics (Kumar y cols., 2006; Morato y cols., 2019).

Spatially explicit planning tools, particularly geographic in-

formation systems (GIS), allow for the integration of mul-

tiple georeferenced variables to support decisions regarding

plant location.

In this context, spatial multi-criteria analysis (MCA) pro-

vides a systematic framework to incorporate diverse spatial

factors into a composite suitability index. MCA has been suc-

cessfully applied in various studies for energy resource allo-

cation, including wind, solar, and small hydro siting (Mal-

czewski, 1999; Aydin y cols., 2013). Its ﬂexibility lies in the

capacity to assign weights to criteria based on technical prio-

rities and local conditions. In previous research conducted

in Ecuador, the authors have demonstrated the applicability

of this methodology in evaluating locations for submerged

hydrokinetic generation, with results consistent with ﬁeld

measurements and hydrodynamic behavior (Salinas-León y

Ochoa-Correa, 2025). These precedents support the relevan-

ce of MCA as a decision-support tool for territorial planning

in renewable energy projects.

This study applies an MCA approach using GIS to iden-

tify the most suitable areas for the installation of biomass

power plants in Ecuador. The methodology combines resi-

due availability data with logistical, environmental, and in-

frastructural parameters to build a suitability model through

weighted overlay analysis. The approach supports regional

planning efforts by generating evidence-based maps that dis-

tinguish areas with higher feasibility for biomass energy de-

velopment. It also provides a technical foundation for future

public and private investment initiatives seeking to expand

the country’s distributed renewable energy capacity.

MATERIALS AND METHODS

General Approach

The methodological design combined quantitative energy

assessment with geospatial modeling in order to determi-

ne technically and geographically feasible locations for bio-

mass power generation in Ecuador. The procedure consis-

ted of three stages. First, agricultural residues were classiﬁed

and quantiﬁed based on crop-speciﬁc generation factors. Se-

cond, the lower heating value (LHV) of each biomass type

was used to estimate the gross energy yield and the effecti-

ve electrical output. Lastly, spatial suitability was analyzed

using GIS, incorporating infrastructure, environmental, and

topographical variables through a MCA.

Residue Identiﬁcation and Quantiﬁcation

The assessment focused on four crops: sugarcane, oil

palm, hard corn, and rice. These crops were selected based

on three criteria: availability of national production data, vo-

lume of residual biomass generated, and compatibility with

direct combustion without prior conversion processes.

Residue generation was estimated using residue-to-

product ratios (RPR) derived from regional literature and

ﬁeld studies. For sugarcane bagasse, the adopted RPR was

0.3, meaning that 300 kg of dry residue are generated per

tonne of harvested cane. For oil palm residues—particularly

empty fruit bunches and ﬁbers—the RPR used was 0.26.

Hard corn and rice had RPR values of 1.0 and 0.2, respec-

tively, considering stalks and husks as primary combustible

fractions (Serrano y cols., 2017; Peláez-Samaniego y Espi-

noza Abad, 2015).

Since not all the biomass is recoverable for energy use, a

40% availability factor was applied to each theoretical va-

lue to account for soil nutrient recycling needs and technical

losses during collection and handling (Chamba Quezada y

Gómez Live, 2020). The ﬁnal recoverable mass m(in ton-

nes/year) was computed as:

m=P×RPR ×α

where Pis the annual crop production (tonnes/year), RPR

is the residue-to-product ratio, and α=0,40 is the assumed

availability coefﬁcient.

Energy Yield Estimation

The energy content of each residue was estimated using

the LHV adjusted for moisture content. The values used we-

re based on national laboratory results and consistent with

international references. For instance, oil palm ﬁber (20%

moisture) was assumed to have an LHV of 15.4 MJ/kg, while

hard corn stalks (15%) were assigned 14.8 MJ/kg. Sugarca-

ne bagasse (50% moisture) and rice husk (10%) yielded 7.94

MJ/kg and 12.01 MJ/kg, respectively (Instituto Nacional de

Preinversión, 2014).

The gross caloriﬁc energy Q(in MJ/year) was then obtai-

ned as:

Q=m×LHV

To estimate the energy potentially available as electricity,

a conversion efﬁciency of 25% was used, consistent with ty-

pical biomass combustion systems of medium scale (15–30

MW) (Kumar y cols., 2006). The effective electrical potential

(EEP), expressed in kilowatt-hours per year, was calculated

as:

EEP =Q×η×277,778

where: - η=0,25 is the conversion efﬁciency, - 277.778

is the MJ-to-kWh factor (1 kWh = 3.6 MJ).

Spatial Suitability Analysis Using GIS

The third phase involved the identiﬁcation of optimal loca-

tions for biomass plant installation based on spatial criteria.

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DOI: 10.54753/cedamaz.v15i1.2494

A multi-criteria evaluation was conducted using ArcGIS, em-

ploying both exclusion and preference factors. Exclusion zo-

nes included protected areas, rivers, national roads, and vol-

canic hazard regions. These areas were masked out of the

analysis based on binary raster layers.

Six preference criteria were selected to reﬂect geographic,

logistic, and environmental conditions: (i) effective electri-

cal potential (from the energy model), (ii) distance to pri-

mary roadways, (iii) proximity to electrical substations, (iv)

slope (percent grade), (v) ﬂood exposure index, and (vi) dis-

tance from urban settlements. Each criterion was rasterized

and standardized on a scale from 1 (least suitable) to 5 (most

suitable).

Weights were assigned to each criterion through expert

judgment and reference to prior MCA studies (Morato y

cols., 2019; Kumar y cols., 2006). The ﬁnal suitability index

was calculated using weighted linear combination:

∑

i=1

wi·ri

where: - Sis the overall suitability score, - riis the norma-

lized raster value for criterion i,-wiis the assigned weight

(Table 1), - ∑wi=1.

Table 1 summarizes the weights used in the MCA.

Table 1: Weights assigned to spatial preference criteria

Criterion Weight

Effective electrical potential 0.40

Distance to primary roads 0.25

Proximity to substations 0.15

Slope 0.08

Flood exposure 0.07

Distance from urban settlements 0.05

The resulting suitability raster was classiﬁed into ﬁve ca-

tegories using natural breaks (Jenks), producing a ﬁnal map

used to identify the most appropriate locations for biomass

plant development.

RESULTS

Effective Electrical Potential of Agricultural Residues

The calculated EEP for each crop was derived from the

recoverable mass and the LHV, as detailed in the metho-

dology. The crop yielding the highest annual EEP was oil

palm, with 1273.55 GWh/year, followed by rice (733.81

GWh/year), sugarcane (220.68 GWh/year), and hard corn

(179.64 GWh/year). These estimates were based on an assu-

med conversion efﬁciency of 25% and reﬂect the moisture-

adjusted caloriﬁc value of each residue.

The predominance of oil palm is due to the high caloriﬁc

value of its ﬁbers and to the year-round availability of its

residues in the coastal regions of Ecuador. Rice husks also

exhibit considerable potential due to their moderate LHV and

substantial production volumes in lowland provinces. Table 2

summarizes the annual EEP estimates by crop.

Table 2: Effective Electrical Potential by Residue Type

Crop EEP (GWh/year)

Oil palm 1273.55

Rice 733.81

Sugarcane 220.68

Hard corn 179.64

Geographic Distribution of Biomass Potential

To assess the spatial distribution of biomass resources

across Ecuador, residue generation was ﬁrst estimated at

the crop level. Figure 1 illustrates the distribution of the

four selected crops—sugarcane, rice, oil palm, and hard

corn—expressed in tonnes per year per province. These maps

were developed using georeferenced agricultural production

data, which served as the basis for calculating the recoverable

biomass by crop.

Using the crop-speciﬁc residue-to-product ratios and the

assumed availability coefﬁcient, the estimated biomass va-

lues were aggregated to construct a composite raster layer

representing the total annual biomass availability per provin-

ce. This aggregation resulted in the total recoverable biomass

map shown in Figure 2.

The combined biomass layer was then used to estimate the

provincial EEP, which reﬂects the energy content of the re-

sidues potentially convertible into electricity. The EEP map

(Figure 3) classiﬁes provinces into ﬁve categories using na-

tural breaks (Jenks), allowing the differentiation of provinces

based on their energy generation potential. Provinces with

extensive oil palm and rice cultivation—particularly Guayas

and Los Ríos—stood out due to their concentration of high-

yield residues and favorable logistics.

The classiﬁcation was derived from the natural breaks

(Jenks) algorithm, which partitions the data into groups that

maximize intra-class similarity and inter-class contrast. Class

1 includes provinces with minimal generation potential, whi-

le Class 5 corresponds to territories with the highest con-

centration of energy-relevant residues. These include Guayas

and Los Ríos, where oil palm and rice dominate production

patterns and logistical infrastructure is already in place.

The ﬁve provinces with the highest EEP concentrate more

than 80% of the national recoverable biomass. Table 3 pre-

sents these provinces along with their estimated annual bio-

mass availability.

Table 3: Top Five Provinces by Effective Electrical Potential

Province EEP (GWh/year) Available biomass (t/year)

Guayas 891.09 1,263,517

Los Ríos 702.83 1,066,434

Esmeraldas 438.58 720,426

Sucumbíos 133.98 220,417

Manabí 124.81 174,583

Spatial Suitability Analysis

To determine locations that meet the spatial requirements

for biomass plant deployment, a MCA was conducted using

GIS-based raster overlay. This process integrated six prefe-

OPTIMAL BIOMASS POWER PLANT SITING IN ECUADOR VEGA-IÑIGUEZ et al.

a) sugarcane b) rice c) oil palm d) hard corn

Fig. 1: Geographic distribution of main crops in Ecuador by production volume (t/year): a) sugarcane, b) rice, c) oil palm, d) hard corn.

Fig. 2: Total estimated recoverable agricultural residues by

province (t/year).

rence criteria and three geographic exclusions. Each layer

was processed and standardized before being combined in-

to a composite suitability index.

Figure 4 presents the preference layers used in the weigh-

ted overlay. These include:

Road accessibility: Euclidean distance to primary and

secondary roads.

Proximity to settlements: Calculated as distance from

urban areas, favoring intermediate locations for distri-

bution efﬁciency.

Distance to substations: Derived from geolocated elec-

trical infrastructure datasets.

Slope: Extracted from digital elevation models, with lo-

Fig. 3: Provincial classiﬁcation of effective electrical potential

(EEP). The scale includes ﬁve classes based on annual generation

potential: Class 1 (0–4.42 GWh), Class 2 (4.42–21.02 GWh), Class

3 (21.02–133.98 GWh), Class 4 (133.98–438.57 GWh), and Class

5 (438.57–891.09 GWh).

wer gradients preferred to reduce construction comple-

xity.

In parallel, exclusion zones were deﬁned to mask out areas

incompatible with biomass infrastructure. These included:

Volcanic hazard zones: Based on ofﬁcial risk zoning

maps.

Hydrographic basins: Protected areas surrounding ri-

ver headwaters.

Transport corridors: Buffers applied to ﬁrst- and

second-order roads to maintain safety distances and pre-

serve transit zones.

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DOI: 10.54753/cedamaz.v15i1.2494

(a) Road accessibility (b) Proximity to settlements

Fig. 4: Spatial preference criteria used in the multi-criteria analysis.

Figure 5 shows the geospatial representation of these ex-

clusion factors.

Once standardized and reclassiﬁed, the preference layers

were weighted according to the scheme in Table 1. The ras-

ter overlay was computed through weighted linear combina-

tion, and the output classiﬁed into ﬁve levels using the Jenks

natural breaks method.

Figure 6 shows the ﬁnal suitability map. Areas in Class

OPTIMAL BIOMASS POWER PLANT SITING IN ECUADOR VEGA-IÑIGUEZ et al.

a) Volcanic hazard zones b) First and second-order road corridors c) Hydrographic basins

Fig. 5: Geographic exclusion layers. a) Volcanic hazard zones, b) First and second-order road corridors, c) Hydrographic basins.

5 combine high biomass availability with favorable topo-

graphy, short distances to key infrastructure, and low expo-

sure to environmental constraints. These locations are predo-

minantly found in coastal provinces, particularly in Guayas

and Los Ríos, but also appear in portions of Esmeraldas and

Manabí. In contrast, high-residue regions with severe slopes

or restricted access ranked lower in the composite index.

Fig. 6: Final suitability map for biomass plant siting in Ecuador.

Classiﬁed into ﬁve levels: Class 1 (least suitable) to Class 5 (most

suitable).

Economic Evaluation: LCOE Estimation

To assess the economic feasibility of biomass power ge-

neration, the levelized cost of electricity (LCOE) was esti-

mated for a hypothetical 25 MW combustion-based facility.

The LCOE represents the average cost per unit of electricity

generated over the plant’s lifetime and was calculated using

the following equation (International Energy Agency (IEA) y

OECD Nuclear Energy Agency (NEA), 2020; Short y cols.,

2005):

LCOE =∑n

t=1(It+Ot+F

t)/(1+r)t

∑n

t=1Et/(1+r)t

where:

It= investment expenditures in year t,

Ot= operation and maintenance costs in year t,

t= fuel costs in year t,

Et= electricity generated in year t,

r= discount rate,

n= project lifetime in years.

The analysis used the following assumptions:

Installed capacity: 25 MW

Capacity factor: 70%

Project lifetime: 20 years

Discount rate: 10%

Capital cost: USD 2,200 per kW installed

Annual O&M costs: 4% of capital cost

Fuel cost: USD 8.5 per tonne (agricultural residues)

Based on these parameters, the annual energy generation

was estimated at 153.3 GWh/year, and the resulting LCOE

was calculated as USD 0.097/kWh. This estimate reﬂects

conservative assumptions adapted to Ecuadorian market con-

ditions. All monetary values are expressed in 2024 USD.

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DOI: 10.54753/cedamaz.v15i1.2494

DISCUSSION

The technical and spatial analysis conﬁrms that residues

from Ecuador’s agricultural sector have the capacity to sup-

port distributed electricity generation through biomass com-

bustion systems. Among the residues evaluated, oil palm ﬁ-

ber stands out due to its combination of thermal properties

and continuous generation throughout the year. Rice husks,

while seasonal, exhibit sufﬁcient volume and energy content

to sustain regional-scale applications, especially when inte-

grated with complementary feedstocks. Although sugarcane

bagasse is already used in industrial cogeneration, particu-

larly in sugar mills, its spatial availability is more concen-

trated and its use for independent biomass facilities may be

constrained by industrial self-consumption. Hard corn stalks

present lower energy yields per hectare but could support

small-scale systems in zones with limited alternative resi-

dues.

Geospatial analysis demonstrates that residue density alo-

ne is insufﬁcient to guide infrastructure placement. The

highest scoring provinces in terms of suitability—Guayas,

Los Ríos, and Esmeraldas—combine favorable logistical

conditions (access to highways and substations) with topo-

graphic stability and minimal environmental exclusion zo-

nes. Conversely, provinces with biomass availability but un-

favorable terrain or ﬂood exposure (e.g., certain Andean

zones) may require additional infrastructure investment or

adaptation in plant design.

Crop calendars also inﬂuence plant viability. Rice and su-

garcane residues are bound to harvesting cycles, resulting

in seasonal surpluses. These patterns introduce limitations

on year-round operation for plants depending exclusively on

these inputs. Oil palm cultivation, largely located in the coas-

tal region, yields residues more steadily across the calen-

dar year, which allows for a more predictable supply stream.

Combining residues with differing seasonal proﬁles may re-

duce storage requirements and increase operational stability,

as previously observed in mixed-feed biomass systems in Co-

lombia and Southeast Asia (Morato y cols., 2019).

Economically, the estimated LCOE for a biomass faci-

lity of 25 MW capacity was USD 0.097/kWh. This value

reﬂects the localized cost structure, including fuel collec-

tion, labor, transportation, and interconnection. While higher

than LCOE benchmarks in countries with large-scale supply

chains—such as Brazil (USD 0.06–0.08/kWh)—the estima-

ted value remains competitive in Ecuador’s context, where

fossil-based electricity still forms part of the dispatch matrix

and faces volatility in fuel supply costs (International Rene-

wable Energy Agency (IRENA), 2023).

The development of distributed biomass energy systems

in Ecuador may beneﬁt from further regulatory adjustments.

Although current legislation recognizes renewable genera-

tion under the distributed model, speciﬁc incentives for bio-

mass—such as feed-in tariffs, tax exemptions, or concessio-

nal ﬁnancing—remain limited. Encouraging cooperative mo-

dels involving small producers could increase supply chain

reliability and promote inclusive rural development. Mo-

reover, integrating biomass facilities into local development

plans would help align infrastructure investment with natio-

nal electriﬁcation and sustainability goals.

In summary, aligning biomass availability with spatial and

technical criteria leads to a more precise identiﬁcation of via-

ble plant locations. The ﬁndings provide a basis for pilot-

scale implementation and suggest that regional development

strategies can beneﬁt from incorporating biomass energy,

particularly in zones with abundant crop residues and logis-

tical connectivity.

CONCLUSIONS

This study combined energy estimation with spatial mo-

deling to evaluate the suitability of agricultural residues for

electricity generation through biomass combustion in Ecua-

dor. The analysis focused on four crop types with established

energy potential: oil palm, rice, sugarcane, and hard corn. To-

gether, these residues could yield an estimated annual output

of 2407.68 GWh, based on conservative assumptions regar-

ding recoverable biomass and thermal conversion efﬁciency.

Spatial analysis revealed that provinces such as Guayas,

Los Ríos, and Esmeraldas not only concentrate the highest

volumes of biomass but also offer geographic and infras-

tructural conditions compatible with biomass facility deploy-

ment. These include relatively ﬂat terrain, access to primary

roadways, and proximity to substations. The use of a weigh-

ted overlay method within ArcGIS allowed for the integra-

tion of energy potential with spatial preference and exclusion

layers, resulting in a suitability map that can guide infrastruc-

ture planning at the regional scale.

In parallel, a cost analysis estimated the levelized cost

of electricity for a 25 MW biomass plant at USD 0.097

per kWh. This value remains within a competitive range for

Ecuador’s generation mix, especially for decentralized appli-

cations where transmission costs and grid extension are li-

miting factors. The estimate reﬂects current local conditions,

including transportation logistics and fuel availability, and is

consistent with values reported in comparable Latin Ameri-

can contexts.

For biomass projects to scale, future planning efforts

should focus on regions with predictable feedstock availa-

bility and suitable siting conditions. Technical design must

account for crop seasonality, which can be mitigated by com-

bining residues with complementary harvest cycles. Beyond

technical considerations, institutional frameworks will play a

decisive role in advancing project implementation. Supporti-

ve measures—such as local incentives, risk-sharing mecha-

nisms, and standardized permitting processes—could encou-

rage private participation and improve deployment timelines.

The methodological framework applied in this study may

also be adapted to other regions in Ecuador or countries with

similar agricultural proﬁles, provided that geospatial and pro-

duction data are available. As part of a broader energy di-

versiﬁcation strategy, bioenergy holds promise as a locally

sourced and grid-compatible complement to intermittent re-

newables.

ACKNOWLEDGEMENTS

The authors thank the Universidad de Cuenca for provi-

ding access to the Microgrid Laboratory at the Faculty of

Engineering, where the present research was conducted.

OPTIMAL BIOMASS POWER PLANT SITING IN ECUADOR VEGA-IÑIGUEZ et al.

AUTHOR CONTRIBUTIONS

Conceptualization: M.E.V.-I., Y.M.-A. and D.O.-C.;

methodology: M.E.V.-I., Y.M.-A. and D.O.-C.; formal analy-

sis: M.E.V.-I., Y.M.-A. and D.O.-C.; investigation: M.E.V.-

I., Y.M.-A. and D.O.-C.; resources: D.O.-C.; data curation:

Y.M.-A. and M.E.V.-I.; writing — original draft: Y.M.-A.

and M.E.V.-I.; writing — review and editing: D.O.-C.; visua-

lization: Y.M.-A. and M.E.V.-I.; supervision: D.O.-C.; pro-

ject administration: D.O.-C. All authors have read and ap-

proved the ﬁnal version of the manuscript.

FUNDING

This research received no external funding.

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