VOL.VI, N.° 1, 2023 | eISSN: 2697-3243 | pISSN: 2588-0829
Autoridades
Dr. Fernando Sempérteguí Ontaneda, Ph. D.
Rector de la Universidad Central del Ecuador
Ing. Diego Paredes Méndez, M.Sc.
Decano, Facultad de Ingeniería y Ciencias Aplicadas
Ing. Flavio Arroyo Morocho, Ph. D.
Subdecano, Facultad de Ingeniería y Ciencias Aplicadas
Revista Ingenio es una revista semestral de la Facultad de Ingeniería y Ciencias Aplicadas de la Universidad Central del Ecua-
dor fundada en el año 2017 | Vol. 6, núm. 1 | enero-junio 2023 | p-ISSN 2588-0829 e-ISSN 2697-3243 |
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Consejo Editorial
Ing. Diego Paredes Méndez, M. Sc., Presidente, Universidad Central del Ecuador, ECUADOR
Ing. Flavio Arroyo Morocho, Ph.D., Editor, Universidad Central del Ecuador, ECUADOR
Ing. Abel Remache Coyago, M. Sc., Editor académico, Universidad Central del Ecuador, ECUADOR
Lic. Tatiana Freire Rosero, M. Sc., Editor de sección, Universidad Central del Ecuador, ECUADOR
Ing. Paulina Viera Arroba, M. Sc., Universidad Central del Ecuador, ECUADOR
Dr. Johannes Ritz, MA., MIB., Ph. D. (c ), EU Business School Munich, ALEMANIA
Dra. Teresa Magal-Royo, Ph. D, Universidad Politécnica de Valencia, ESPAÑA
Dr. Andrés Vivas Albán, Ph. D., Universidad del Cauca, COLOMBIA
Dr. Boris Heredia Rojas, Ph. D., Universidad del Norte, CHILE
Dr. Jaime Duque Domingo, Ph. D., Universidad de Valladolid, ESPAÑA
Dr. Giovanni Herrera Enríquez, Ph. D., Universidad de las Fuerzas Armadas-ESPE, ECUADOR
Dr. José Luis Paz, Ph. D., Universidad Nacional Mayor de San Marcos, PERÚ
Dr. Jesús López Villada, Ph. D., Universidad Internacional SEK, ECUADOR
Dr. Michel Vargas, Ph. D., Escuela Politécnica Nacional-EPN, ECUADOR
Dr. Andrés Robalino-López, Ph. D., Escuela Politécnica Nacional-EPN, ECUADOR
Dr. Ali Bagheri Fard, George Brown College, CANADÁ
Dr. Kiyanoosh Golchin Rad, Pukyong National University, SOUTH KOREA
Dr. Alberto Sánchez, Escuela de Ingenierías Industriales-UVA, ESPAÑA
Dra. Esther Campus Serrulla, Ph.D., Universidad Europea de Madrid, ESPAÑA
Ing. Hamid Aadal, M.Sc., Science & Technology Innovation-ADF, IRÁN
Dra. Diana Ayala, Universidad Santo Tomás-USANTOTO, COLOMBIA
Consejo Asesor y Evaluador
Ing. Atal Kumar Vivas, M.Sc., Universidad de las Fuerzas Armadas ,
Ing. Galo Flor Terán, MBA., Universidad Tecnológica Equinoccial ,
Ing. Carlos Córdova Santafé, M. Sc., Ingenieros Córdova y Morales,
Ing. Alex Junqui Cedeño, M. Sc., Universidad Laica Eloy Alfaro de Manabí-,
Ing. Eddy Sánchez, M. Sc., Ponticia Universidad Católica del Ecuador ,
Ing. Tania Crisanto, M. Sc., Universidad de las Fuerzas Armadas ,
Ing. Nelson Chávez, M. Sc., Holcim Ecuador . . ,
Ing. Hugo Juanny Latorre, M. Sc., Universidad Central del Ecuador,
Ing. Holger Santillán, M. Sc., Universidad Politécnica Salesiana,
Ing. Rogger Peña, M. Sc., Instituto Superior Tecnológico Simón Bolívar,
Ing. Hugo Mauricio Valladares, M. Sc., Universidad Central del Ecuador,
Ing. Majid Khorami, M. Sc., Ph. D. (c), Universidad Tecnológica Equinoccial ,
Ing. Lenin Villareal, M.Sc., Universidad de los Hemisferios,
Ing. Christian Chimbo, M. Sc. Universidad de las Américas ,
Ing. Sebastián Espinoza, M. Sc., Instituto de Investigación Geológico y Energético ,
Ing. Luis Xavier Orbea, M. Sc., Universidad Tecnológica Equinoccial ,
Este número estuvo bajo la coordinación editorial del Ing. Flavio Arroyo, Ph. D., Ing. Abel Remache, M. Sc., y Lic. Tatiana Freire, M. Sc.
Revista Ingenio
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ÍNDICE
Andaluz M.
.....................................................................................38
Sisa V., Quinatoa C.
Vinocunga D., Romero A., Sánchez C.,
Propuesta de gestión para la adopción de BIM en empresas fabricantes ………………...… ...................80
Guzmán A., Alvansazyazdi M.
Inuencia en el diseño estructural del acero de refuerzo grado 80 y hormigón
de alta resistencia (55MPA) frente al acero de refuerzo convencional grado 60
y hormigón f ’c 28MPA en un edicio de hormigón armado de 18 pisos ………………...… ..................94
Pineda S., Villafuerte S., Correa M., Machado L., Hernández L.
Normas para publicar en la revista INGENIO …………..… ...........................................................................107
Drones multirotor en levantamientos topográcos de zonas montañosas .................................................13
Erazo R.
Diseño del proceso de obtención de queso fresco en la provincia de Chimborazo en el soware
SuperPro Designer ........................................................ .....................................................................................60
Optimal georeferenced deployment of charging stations for electric vehicles in distribution networks
using a trajectory-based heuristic model ………………...… .........................................................................4
Análisis comparativo del ciclo de vida - huella de carbono de una edicación de
hormigón armado frente a una edicación de estructura metálica …………........ ....................................20
Erazo R., Pardo V.
Análisis de conabilidad usando el método de Monte Carlo en los alimentadores
principales de la subestación Cristianía perteneciente a la Empresa Eléctrica
Quito……………………………..………………..…
Auditoría energética en las instalaciones del centro de operaciones y mantenimiento
de transporte ()....................................................................................... .................................................. 70
Salazar D., Placencio J., Ortiz Y., Laverde C.
La Revista es el órgano de divulgación especializada de la Facultad de Ingeniería y Ciencias
Aplicadas de la Universidad Central del Ecuador; su objetivo es la difusión de investigaciones técnicas y
cientícas en los campos de Ingeniería Civil, Ingeniería Informática/Sistemas de Información, Ingeniería
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REVISTA INGENIO
Optimal georeferenced deployment of charging stations for electric vehicles in
distribution networks using a trajectory-based heuristic model
Despliegue óptimo georreferenciado de estaciones de carga para vehículos eléctricos en redes de
distribución usando un modelo heurístico basado en trayectoria
Miguel Andaluz | Universidad Politécnica Salesiana, Quito, Ecuador
https://doi.org/10.29166/ingenio.v6i1.4303 pISSN 2588-0829
2023 Universidad Central del Ecuador eISSN 2697-3243
CC BY-NC 4.0 —Licencia Creative Commons Reconocimiento-NoComercial 4.0 Internacional ng.revista.ingenio@uce.edu.ec
, (), -, . -
e progressive increase in the consumption of fossil fuels and the constant eorts to reduce CO2 emis-
sions bring together the search for alternatives and the transport sector being one of the most dependent
on fossil fuels and the cause of approximately 80% of the air pollution, the electric vehicle emerges as an
alternative in mobility. at is why this article proposes a methodology for the optimal location of elec-
tric vehicle charging stations, given in a georeferenced distribution network scenario using a heuristic
for the insertion of electric vehicles, taking into account energy consumption, travel and autonomy. de-
veloped based on real data, reducing the minimum location of charging stations. Evaluated in the distri-
bution network of Santo Domingo-Ecuador, in a way that guarantees a technical and economic balance.
El progresivo aumento del consumo de combustibles fósiles y el constante esfuerzo por reducir las emi-
siones de CO2 se unen para la búsqueda de alternativas, y siendo el sector del transporte uno de los más
dependientes de los combustibles fósiles y causante de aproximadamente el 80% de la contaminación
atmosférica, el vehículo eléctrico surge como una alternativa en movilidad. Es por ello por lo que este ar-
tículo propone una metodología para la ubicación óptima de estaciones de carga de vehículos eléctricos,
en el escenario de una red de distribución georreferenciada, utilizando una heurística para la inserción
de vehículos eléctricos, teniendo en cuenta el consumo de energía, los viajes y la autonomía, desarrollada
con base en datos reales, reduciendo la ubicación mínima de las estaciones de carga; considerando la red
de distribución de Santo Domingo-Ecuador, de manera que garantice un equilibrio técnico y económico.
1. introduction
e need for means of transportation for the develop-
ment of our occupations has been present throughout
the history of the human race. According to the latest
Ecuadorian Energy Balance of 2017, the transport sector
represented 52.29% (45,098 kBEP; 73,427.76 GWh) of
the total national energy consumption [1].
Looking for mechanisms that provide exibility in the
consumption of sources that come from fossil energy, thus
facilitating the migration to other primary energy sour
-
ces. ese would allow the development of the same ac-
tivities but with a minimum environmental impact and
reduced polluting emissions to the planet [2].
e agencies in charge of energy planning must con-
sider scenarios from the point of view of supply and de-
mand where electric mobility systems are representative,
as well as mechanisms and technical inputs not only at
Received: 26/10/2023
Accepted: 19/12/2023
Optimal deployment, heuristic model,
energy consumption, charging stations.
Despliegue óptimo, modelo heurístico,
consumo de energía, estaciones de carga.
5
Optimal georeferenced deployment of charging stations for electric vehicles in distribution networks using
a trajectory-based heuristic model
the engineering level but also in the regulatory framework
that allow the technological transition without producing
disadvantages in the operation of the electric power su-
pply system’s [3].
As electric vehicles increase their market share, it’s
going to get some attention from power companies. Its
inclusion in power systems represents a large increase in
load demand, causing many problems of power quality
degradation, increased energy losses. However, a problem
may occur between the network operators and the owners
of the charging stations since it may be the case that the-
re are dierences because the owners of the charging sta-
tions look for the commercial place where they can charge
the electric vehicles, but at lower cost. On the other hand,
the electricity network operators estimate that the char-
ging stations are located in such a way that they allow a
predetermined number of vehicles to be fed, impacting
the electricity network as little as possible [4].
Various solution methods worldwide have been pro-
posed to locate charging stations. For example, genetic
algorithms and voronoi diagrams have been incorpora-
ted. ese algorithms do not consider very important fac-
tors such as: load prole, consumption, autonomy, and
geographical considerations. at is why we start, for the
optimization process, from candidate sites which can be
conventional service stations, bus stops, shopping cen-
ters, parking lots, parks, etc. Consequently, the proposed
model does not start from scenarios where candidate si-
tes are considered, as would happen with voronoi when
segmenting the area of analysis but starts from a study
area. at is, he knows the study area based on its carto-
graphic reality [5].
Regarding the prole of charge and consumption of
Electric Vehicles (), the historical information of the re-
cords of electric taxis that operate in the city of Loja was
considered, as well as a model developed by the authors
that takes into account the process of charging of EV ba-
tteries modied in a novel model that represents the elec-
tric vehicle battery charging system based on its state of
charge and its current variability and charging time.
e general problem lies in optimally locating and si-
zing the charging stations along a georeferenced distribu-
tion network of 34 nodes, so that the proposed heuristic
starts from candidate sites in the network, of which they
can be public places, that is, it is an iterative method that
knows the study area since this information is extracted
from Open Street Maps (), as well as the use of -
soware to implement graph theory that will allow
nding the nodes and topology that is part of the solution
set. To later evaluate the voltage proles and load losses
simulated in Cymdyst [6].
2. method
2.1. ENERGY CONSUMPTION OF ELECTRIC VEHICLES
e prerequisite for the planning of charging stations is
to create the conditions for an adequate consumption
of electrical energy. On the other hand, electric vehicles
have zero emission characteristics; Low engine noise and
higher propulsion eciency [7], [8], [9], [10], [11].
From the point of view of transport systems, whether
public or conventional, a huge proportion of energy con-
sumption is due to the inecient movement of trac. e
exible energy consumption estimation model is based on
the evaluation of consumption based on data from other
vehicles on the road network, which have the possibility
of being accurate thanks to the dierent vehicle models
and energy eciency [12], [13].
e cost for energy consumption per 100 km of an
electric transport is up to three times less than the cost
of a conventional vehicle that uses fossil fuel, this taking
into account that in Ecuador there are lower rates, both
for gasoline and electricity [14].
When analyzing the real cost of electricity in the
country and the international price of gasoline, the EV is
still lower than that of a thermal combustion vehicle, the-
refore, the electric vehicle is more protable and ecient
even with the fuel subsidy that exists in the country. is
advantage is also visible in Europe [14].
2.2. ELECTRIC DISTRIBUTION NETWORK IN ELEC
TRIC VEHICLES
Within the exponential growth of EVs in moderate
portions, it should not cause too many inconveniences,
however, its wide adoption will probably create an im-
pact on the operation and management of electrical dis-
tribution networks, such as congestion, voltage problems
and load imbalances between phases [15].
Depending on the autonomy of the Electric Vehicle,
the excessive charge of the batteries of said cars will have
an impact on the distribution system, which would in-
crease the load demand, introducing disturbances in the
Interconnected Electric System, which imposes an in-
crease in the generation and make probable reinforce-
ments with the penetration of renewable energy in order
to maintain the balance between what is generated and
consumed [16] (see Table 1).
Approximately, the battery charging speed depends on
the output of the charging station and the technical speci-
cations of the electric car. e peak daily load curve during
a day in the worst case would have a higher consumption
6
Andaluz M.
in the midday and aernoon hours, and a lower consump-
tion in the early morning [17] (see Figure 1).
To guarantee the continuity of the electricity supply and
stabilize the demand curve, which in fact changes accor-
ding to the time and type of daily charge of an electric
vehicle, strategies and procedures are considered where it
does not aect the electrical system and carry out a mas-
sive integration of electric vehicles in a planned way [18].
2.3. MOST REPRESENTATIVE CHARACTERISTICS OF
THE FLEET SYSTEM TO DETERMINE CONSUMPTION
To carry out the cost comparison, the most used com-
bustion vehicle in Ecuador was taken into account, the
model is the Chevy Aveo, and the Nissan Leaf model as
an electric vehicle, for which the initial cost of the elec-
tric vehicle vs. the combustion vehicle, the electric vehicle
has an increase in cost with 85% compared to the cost of
the conventional vehicle.
e costs for energy consumption were determined
based on the technical specications provided by the ma-
nufacturer of the electric vehicle. Several brands and mo-
dels of electric vehicles are expected to soon circulate on
the roads of Ecuador.
It is established that vehicle users generally log less
than 50 km per day, with a performance index for elec-
tric vehicles of 8 km/kWh (0.122 kWh/km) under ideal
conditions of trac and geography, it is concluded that
the energy demanded by the EV of the network would be
0.144 kWh for each kilometer traveled.
2.4. ALGORITHM
e algorithm will be responsible for determining the
optimal location of charging stations by extracting the
characteristics of electric vehicles, through the network
of 34 georeferenced nodes. is route may be useful for
the study of any real scenario of an electrical system de-
pending on the demand scenarios determined by Cymd-
yst (see Table 3).
Consequently, in [33] the heuristic model is explai-
ned in a standard way to solve the programming problem
Table 1
Charge mode data
Charging mode
Mode1 Mode2 Mode3 Mode4
Corrent (A) 16A 32A 64A Hasta 400A
Type of load slow slow Accelerated charging Fast charging
Power (kw) 3,8-11 7,7-22 14,8-43 40-120
Specic Connector No No Yes Yes
Figure 1
Model of charging stations. Electric vehicles in distribution networks
To determine the characteristics of the vehicle eet, a
comparison was made of both the conventional and elec-
tric vehicles, taking into account the route, autonomy
and consumption. For this, the costs of various models
of electric vehicles that are used in the United States wi-
thout taxes and without subsidies are shown, but these
low-end vehicles have already been inserted in Ecuador
(see Table 2).
7
Optimal georeferenced deployment of charging stations for electric vehicles in distribution networks using
a trajectory-based heuristic model
for which the kmeans algorithm will be used to generate
cluster, through the distribution network model of 34 no-
des generated in Cymdyst will be distributed in scenario
where you will get the power, voltage and consumption
at which the charging stations act taking into considera-
tion, public places [19].
3. Results and discussion
Once the model to be used is proposed, a result will be
obtained, which is developed in two scenarios that are
based on a base case study where it will be the starting
point to analyze the dierent behaviors of the network
when the charging stations come into operation. and the
impact on the elements to future case studies of load to
the distribution network.
3.1. ANALYSIS OF OPTIMAL LOCATION OF CHAR
GING STATIONS IN THE DISTRIBUTION NETWORK
One of the objectives of this article is to nd the opti-
mal location for charging stations, to evaluate in a geo-
referenced distribution network taking into account the
characteristics of electric vehicles, inserted in Ecuador
both in their consumption and autonomy compared to
conventional vehicles. based on satisfying user demand.
In the rst instance, it is necessary to extract the coordi-
nates of the area to be studied, through the Open Street
Maps that helps the georeferencing of the scenario, the-
refore the longitude and latitude given below were obtai-
ned as data (see Table 4).
Next, the results obtained from the optimization are
presented to nd the strategic points of charging stations
for the correct functioning of the network, since strate-
gic points of access to the public in the georeferenced ne-
twork were taken into account, such as parks, centres
Table 2
sales prices in Ecuador
Vehicle type Model Sales price in the usa without tax [usd]
Chevy Bolt 37.495
Ford Focus Electric 29.120
Nissan Leaf 30.680
EV Fiat 500e 31.800
BYD e5 34.990
Volkswagen e- Golf 28.995
Table 3
Pseudocode of the solution algorithm
Algorithm placement of charging stations
Step 1: Georeferencing and scenario generation
Step 2: Get the coordinates of the area.
Step 3: Declaration of variables
Xij, Zij, λ
Step 4: Read OSM le
Openstreetmap.
Step 5: Minimum enabling distance.
For k longitud (Xij)
[v]=BVE (λ, Xij)
end for
Step 6: Writing Purpose Function.
Step 7: Candidate sites for the study area
Step 8 End