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<= /span>

 

 

<= span lang=3DES>https://doi.org/10.37815/rte.v35n2.1061

<= span lang=3DES>Artículos originales

 

El modelo OSeMOSYS= y la descarbonización a largo plazo del sector eléctrico en Galápagos

OSeMOSYS model and the long-term decarbonizatio= n of the power sector in Galapagos=

 

Esteban Meneses1, 2 https://orcid.org/0000-0002-= 8578-956X, Rafael Soria2 https://orcid.org/0000-0003-

4836-1510=

 

 

1Escuela Politécnica del Litoral, Quito, Ecuador

estebanmenesesg@gmail= .com

 =

2Universidad= San Francisco de Quito, Quito, Ecuador

rsoriap@usfq.edu.ec


<= span lang=3DES style=3D'font-size:7.0pt;mso-bidi-font-size:11.0pt;mso-font-width= :102%'>.


Esta o= bra está bajo una licencia internacional Creative Commons Atribución-= NoComercial 4.0


Enviado: 2023/07/16 Aceptado: 2023= /08/22 Publicado: 2023/10/15


 


Resumen

OSeMOSYS (Open Source Modelling System) es = un modelo de equilibrio parcial que busca satisfacer la demanda energét= ica en el horizonte de tiempo planteado, que se optimiza por mínimo costo. El modelo ofrece diversas ventajas debido a su fácil uso y su condición de código abierto, lo que le permite estar en constante actualización = y evolución gracias a una comunidad creciente. Entre sus aplicaciones están los sistemas energéticos, los diferentes usos del suelo y la capacidad de plantear distintos escenarios de acuerdo con la información disponible. El trabajo presenta los principales comp= onentes del modelo, sus diversas opciones de uso y casos de uso en algunas regiones del mundo, incluido el caso de expansión eléctrica al año 2050 en las Islas Galápagos. Como resultado, no solo es posible contar con electricidad 100% renovable en las Islas, también es un escenario menos costoso en relación a mantener el sector eléctrico como se encuentra en = la actualidad. Dicha inversión sería menos de la mitad de lo que= se gastaría si se sigue con las políticas eléctricas actuales. Para alcanzar la descarbonización de las Islas, se tiene que empezar a invertir en energías renovables que, con el mejoramiento de su tecnología, son cada vez menos costosas y más eficientes. Es importante recalcar que, para cualquier plan a largo plazo, se debe contar con los estudios ambientales necesarios,<= span style=3D'letter-spacing:-.5pt'> más aun tratá= ndose de un lugar con tanta riqueza natural.



&nbs= p;

Palabras clave: = código abierto, planificación energética, energía renovable, capacidad, potencia,

gases de efecto invernadero (GEI).

 

 

Abstract

OSeMOSYS (Open Source Modeling System) is a partial<= span style=3D'letter-spacing:-.3pt'> equilibrium model that aims to meet energy demand over a specified time horizon,= optimizing for minimum<= span style=3D'letter-spacing:-.2pt'> cost. The model offers a variety of advantages due to its ease of use and open-source status, which allows it to be constantly updated and evolved= by a growing community. Among <= span class=3DSpellE>its applications are energy systems as well as different possible= land uses. In addition= , the user can establish a variety = of scenarios according to the available information. In this way, the work presents its main components as well as its various usage o= ptions, in addition<= span style=3D'letter-spacing:-.65pt'> to case s= tudies in some r= egions of the= world, including the case of electrical expansion to 2050<= span style=3D'letter-spacing:-.75pt'> in the Galapagos Islands. As a result, = it is possible to have 100= % renewable electricity in the islands, and it is a less costly<= span style=3D'letter-spacing:-.65pt'> scenario than maintaining the electricity sector as it is today.<= span style=3D'letter-spacing:-.7pt'> This investment would be less than half of what would be spent if current <= span class=3DSpellE>electricity policies = were continued. To achieve <= span class=3DSpellE>decarbonization = on the islands, it is necessary to start investing in renewable energies, which,<= span style=3D'letter-spacing:-.55pt'> with the improvement of their technology, are becoming increasingly less expensive and more <= span class=3DSpellE>efficient. It is important to note that any long-term plan must include = the necessary environmental <= /span>studies, especially in a place with so much = natural wealth.

 

Keywords: open source, energy planning, = renewable energy, = capacity, power, greenhouse gases (GHG).

 

Introducción

El archipiélago de las Galápagos se encuentra alrededor de 1.000 km de las costas de Ecuado= r. Estas islas son jóvenes en térm= inos geológicos, lo que las hace vulnerables a especies con mayor tiempo de evolución. Su historia de asentamientos humanos comenzó a mediados del milenio pasado, cuando eran visitadas por piratas y balleneros, y después fueron colonizadas por primera vez para realizar actividades agríc= olas por exiliados y presos políticos de los primeros gobiernos de la naciente República del Ecuador, en los años 30= del siglo XIX. Las islas fueron declaradas Patrimonio Natural de la Humanidad y Reserva de la Biósfera por la UNESCO1 en el año de 1978 y 1984 respectivamente, desde entonces= las islas han crecido demográficamente, al igual que el turismo que llega cada año a las islas del Parque Nacional Galápagos, PNG (2023).

 

Actualmente, el 97% del áre= a del archipiélago es parte del PNG y la Reserva Marina de Galápago= s. El 3% o 230 km^2 del área de las cuatro islas pobladas está destinada para asentamientos h= umanos; de esta área, el 48% está en la isla Santa Cruz, sobre todo en las zonas altas designadas para la agricultura (84%) (WWF, 2003). Demográficamente, Santa Cruz es= la más poblada, con alrededor de 18.000 habitantes, seguida de San Cristóbal (7.200), Isabela (2.400) y Floreana (160). Estas islas concentran sus poblaciones alrededor de los puertos, con pequeños asentamientos en zon= as agrícolas altas (INEC, 2015). La economía de las Islas Galápagos pasó de ser agrícola a una basada en el turi= smo, siendo este rubro el 88% de sus ingresos en 2006 (Eple= r 2007). Sin embargo, De Miras (1995) estimó que solo el 7% de la riqu= eza se queda en las islas, ya que los principales operadores tur&i= acute;sticos son extranjeros o del territorio continental ecuatoriano.

 

 


1&nb= sp;   Organización de las Naciones Unidas<= span style=3D'letter-spacing:-.15pt'> para la Educación, la Ciencia y la Cultura.


&nbs= p;

El número de visitantes a las Islas Galápagos aument= ó significativamente desde finales d= e la década del ochenta. Así, el número= de turistas que visitan las islas superó el límite= oficial recomendado de 12.000 habita= ntes. Para hacer frente a la demanda creciente de turistas, el límite se amplió a 25.000, pero incluso este nuevo límite fue superado. Esto plantea preguntas sobre cuánto tiempo más podrán las islas soportar el crecimiento vertiginoso del turismo. En el año 2022, el archipiélago<= span style=3D'letter-spacing:-.05pt'> recibió 267 mil turistas (MAATE, 2023). Asimismo, la población loc= al también experimentó un crecimiento exponencial, pasando de 1.= 346 habitantes en 1950 a 25.244 en 2015 (INEC, 2015). Este crecimiento de turis= tas y población local se reflejó en la economía, que creci= ó aproximadamente un 78% entre 1999 y 2005, o un 9,6 % anual, lo que llev&oac= ute; a Galápagos a ser una de l= as regiones de mayor crecimiento a nivel mundial en términos económicos (Taylor, Hardner, and Stewart, 2009). El valor agregado bruto (VAB) en las islas en = el año 2022 fue alrededor de USD 260 millones2 y su VAB per cápita fue alrededor<= span style=3D'letter-spacing:-.35pt'> de USD 9 mil (BCE 2023a).

 

Este crecimiento turístico = ha impulsado la inmigración, lo que provoca un riesgo para la seguridad alimentaria del lugar (Walsh and Mena, 2016). Según Sampedro et al. (2020), las islas importaron el= 75% de los productos agrícolas que consumieron en el año 2017. Es= tos volúmenes las hacen vulnerables a las especies invasoras. Como resultado, dos de las especies de plantas invasoras, la guayaba y la mora, se han apoderado de área= s de tierra que antes se cultivaban, incluso en áreas restringidas del pa= rque (Snell et al., 2002). Tanto el crecimiento demográfico como el turístico han generado preocupación en términ= os energéticos, por lo que resulta necesario considerar la expansión energética del archipiélago.

 

Actualmente, la electricidad<= span style=3D'letter-spacing:-.1pt'> en las islas Galápagos proviene en su mayoría d= e motores de combustión interna (84%), eólica (12%) y solar (4%). Las islas importan grandes volúmenes= de derivados de hidrocarburos para satisfacer las necesidades energéticas de la población local y el turismo (IIGE, 2018). En consecuencia, la enorme riqueza biológica de las islas resulta vulnerable a posibles derrames de combustible. Es así como, en 2008, el gobierno ecuatoriano lanzó la Iniciativa Cero Combustibles Fó= siles en Galápagos, con la finalidad de eliminar el uso de combustibles fósiles y disminuir la emisión de gases de efecto invernadero al ambiente (PNU= D, 2016).

 

Para la construcción<= span style=3D'letter-spacing:-.45pt'> de un modelo de expansión eléctrica a largo plazo, se tienen varias opciones de modelos y progra= mas, en su mayoría modelos pagados, lo que dificulta su uso masivo, principalmente en economías en desarrollo. En consecuencia, el model= o de código abierto OSeMOSYS3 representa una opción de fácil acceso y aprendizaje. Este modelo de equilibrio parcial, que optimiza al menor costo, considera: eficiencia tecnológica, infraestructura actual energética= , capacidad de almacenamiento actual, la vida operacional de las tecnologías, factores de capacidad de las plantas, perfiles de recursos disponibles (viento, sol,<= span style=3D'letter-spacing:-.45pt'> etc), perdidas por distribución y/o transmisión, variables exógenas al modelo como el PIB, crecim= iento poblacional, entre otras. Dicho modelo fue presentado por primera vez en el taller IEA en 2008, escrito en GNU MathProg y después desarrollado en GAMS4<= /sup> y Python (Niet et al., 2021). Esta herramienta fue= desarrollada para ayudar a los planificadores de energ&iacut= e;a, así como para la comunidad científica de energía (Howells et al., 2011).

 

El trabajo tiene como objetivo mostrar el funcionamiento del modelo OSeMOSYS= y

 


2   Valores= provisionales.<= /span>

3&nb= sp;   OSeMOSYS= (Open Source Modelling System).

4&nb= sp;   General Algebraic Modelling System.


&nbs= p;

algunos casos de uso. Por tanto, el trabajo se estruc= tura de la siguiente manera: La sección de metodología describe el modelo, sus principales características y plataformas de uso, y datos del caso en las islas; la sección de resultados indica los principales hallaz= gos de los casos a nivel global, y el caso de uso en las islas Galápagos; finalmente se presentan algunas conclusiones.

 

 

Metodología

Modelo OSeMOSYS<= o:p>

OSeMOSYS es un modelo de equilibrio, es decir, la oferta de energía es igual a la demanda. El modelo, al ser de código abierto, es un modelo que permite mejoras. Por tanto, es posible acceder a las ecuaciones del modelo y modificarlas a conveniencia, lo cual requiere un poco de destreza en la escritura de código.

 

Actualmente, existe tambi&eac= ute;n la nube de OSeMOSYS, donde el usuario puede crear una cuenta y correr el modelo, y asi= mismo se puede encontrar la última versión de la aplicación = aquí. Tanto la aplicación como la nube de OSeMOSYS funcionan en conjunto; en la aplicación se ingresan los d= atos al modelo y en la nube se corre el modelo. El usuario tiene a disposición el programa clicSAND (Cannone et al. 2022), permite correr el modelo con un archivo Excel para mayor facilidad. Para más información, se pueden tomar los cursos en línea disponibles gratuitamente en Open <= span class=3DSpellE>University (University 20= 23).

 

El modelo está desarrollado= en bloques y cada uno en tres niveles de abstracción. Los parámetros, las variables, las restriccio= nes y las relaciones con sus objetivos están d= escritas en inglés, así como, la formulación algebraica y su implementación en un lenguaje de programación. Fue desarrollado en bloques para facilitar su independencia y mejoras. Existen siete = bloques:

(1) objetivo, (2) costos, (3) almacenamiento, (4) capacidad adecuada, (5) balance = energético,

(6) restricciones, (7) y emisiones (Howells et al., 2011). Ver Figura 15.

 

Figura 1

Bloques y niveles de abstracción del modelo OSeMOSYS=


 

Par= a la definición del modelo existe: Conjuntos (ej. <= span lang=3DES style=3D'letter-spacing:-.1pt'>rango temporal, intervalos de tiempo, etc.), paráme= tros globales, parámetros de demandas, parámetros de tecnología, parámetros de <= /span>desempeño, parámetros de costos, parámetros de capacidad, restricciones de capacidad,


5&nb= sp;   Figura 1, adaptada de (Howells et al., 2011).


&nbs= p;

restricciones de inversión, restricciones de actividad, márgenes de reserva, emisiones y penalidades. Para mayor detalle sobre los parámetros, variabl= es, conjuntos y ecuaciones, remítase al manual OSeMOSYS (2023).

 

Función Objetivo

La función objetivo que se encuentra en el primer bloque (ver Figura 1) calcula el mínimo valor presente neto del sistema energético para satisface= r la demanda dada, que puede ser solo del sector eléctrico o uno que incluya todos los sistemas energéticos y usos de suelo. Para alcanzar la función objetiva deben satisfacer las diferentes restricciones (Howells et al., 2011).

 

Costos

Se tienen los costos de cada tecnología, tanto los de capital de inversión, así como los

de operación y mantenimiento, fijos y variable= s. Además, se puede ingresar los provenientes de penalidades por emisiones de GEI. Los costos variables se calculan para cada intervalo de tiempo especificado por el usuario, modo de operación de la tecnología, año de operación y región modelada. Los costos fijos se calculan de acuerdo con la capacidad instalada de cada tecnolog&i= acute;a. Al final se obtiene el costo de operación y mantenimiento anual que es la suma de los costos fijos y variables, estos son llevados al primer año modelado con una tasa de descuento. Los costos anuales de inversión están det= erminados por el nivel de capacidad existente y nueva, e igualmente son llevados al = primer año modelado con una tasa de descuento global o por tecnologí= a (Howells et al., 2011).

 

Capacidad Adecuada

Deb= e existir una capacidad suficiente para cada<= span lang=3DES style=3D'letter-spacing:-.65pt'> tecnología que permita cubrir la demanda energética anual. Es necesario determinar la capacidad tot= al disponible previa al periodo del modelo, es decir, la capacidad actual al comienzo del periodo. Para determinar la capacidad suficiente anual, se calcula la tasa de actividad de cada tecnología, intervalo de tiempo y región modelad= a que es ingresada por el usuario en los factores de capacidad (Howells et al., 2011).

 

Balance Energ&eacu= te;tico

Los niveles de operación toman en cuenta la actividad, el uso de energía, la producción de energía y las emisiones en cada modo de operación y tecnología que son calculados por intervalos de tiempo. Por tanto, es importante que la producción energética cubra la demanda en c= ada intervalo. La tasa de producción de combustibles por tecnologí= ;a es determinada multiplicando la tasa de actividad de la relación de entrada/salida del combustible, que es la eficiencia de la tecnología (Howells et al., 2011).

 

Restricciones

Podemos limitar el modelo de varias formas, mediante capacidad con límites superiores y/o inferiores, por generación con límites superiores y/o inferiores, por cantidad de emisiones por el periodo total del modelo y mediante penalidades por emisiones por tecnología y/o por región. Finalmente, se lo puede limitar por máxima o mínima inversión en términ= os monetarios por unidad de capacidad. Dichos lí= mites se los puede hacer por tecnología, por región y por año (Howells et al., 2011).

 

Emisiones

Para las emisiones de GEI el usuario ingresa las relaciones de emisiones por unidad de actividad. Se puede considerar más de un GEI por cada tecnología y modo de operación; solo la actividad promedio de cada tecnología y su modo de operación se multiplican= por la relación emisión/acti= vidad. Las emisiones anuales se obtienen de la suma de las emisiones anuales por


&nbs= p;

modo de operación. Sin embargo, no todas las tecnologías son consideradas en las emisiones, por ejemplo, las tecnologías renovables como la solar y la eólica. (Howells et al., 2011).

 

OSeMOSY= S Galápagos

Área de Estudio

El archipiélago de Galápagos está formado por 22 islas, entre grandes y medianas= , y 107 islotes de origen volcánico con una inmensa riqueza bioló= gica (WWF, 2003). Estas islas enfrentan un permanente y vertiginoso crecimiento turístico que aumenta la demanda energética. En consecuencia, es necesario incrementar la oferta energética; en este sentido, el trabajo tiene cuatro regiones que son las c= uatro islas pobladas del archipiélago; Santa Cruz, San Cristóbal, Isabela y Floreana (ver Figura 2).

 

Figura 2

Infraestructura eléctrica por la Empresa Eléctrica de Galápagos


 

Datos

El trabajo usa diversas fuentes, la información sobre demandas eléctricas e infraestruct= ura

tecnológica existente proviene del SISDAT (ARCONER, 2023), que contiene datos históricos= ; las proyecciones de demanda del trabajo de la Fundación Bariloche (Fun= dación Bariloche FB, 2020). Los= precios de los combustibles s= e consideran subsidios. Las= proyecciones al año<= span style=3D'letter-spacing:-.15pt'> 2050 proceden del promedio histórico de subsidios del Banco Central del Ecuador del sector petrolero (BCE, 2023b).

 

Desarrollo del Mo= delo de Galápagos

Se definieron dos estaciones, la lluviosa y cálida que va desde diciembre hasta mayo, y la seca que va desde junio a novie= mbre. A continuación, se definieron las cuatro regiones; San Cristóbal (SC), Santa Cruz (STC), Isabela (IS), Floreana (FL). El horizonte de tiempo del modelo es de 2019 al 2050, y los primeros años se usan para calibrar al modelo. Los perfiles de los recursos provienen de las siguientes fuentes: El recurso viento obtiene dat= os de Ninja Wind, que se originan en el saté= ;lite de la NASA6, MERRA (Staffell and Pfenninger, 2016). Los datos de irradiancia global se adquieren del System = Advisor <= span class=3DSpellE>Model (SAM, 2023), de la

 


6   The National Aeronautics and Space Administration.


&nbs= p;

National Renewable Energy Laboratory. Para estos perfiles se toma el año meteorológico que comprende promedios quin= ce años o más.<= span style=3D'letter-spacing:-.25pt'> Las curvas de carga de cada isla forman parte de las demandas eléctricas. Finalmente, se tienen dos modos de operación debi= do a las baterías, uno que almacenan y en otro distribuyen energía= .

 

Infraestructura Energética de Galápagos<= /span>

La electricidad en las islas proviene de fuentes no renovables (84%), como las centrales térmicas de motores de combustión interna alimentadas con diésel (ver Tabla 1). Por tanto, para cambiar esta realidad se necesitan fuertes invers= iones en energías limpias que con su desarrollo experimenten costos decrecientes progresivamente más accesibles para economías en desarrollo. Actualmente, el archipiélago cuenta con energía solar y eólica. Existen estudios del potencial energético de energía geotérmica en la isla Isabela y de olas en el sur de las islas (Portilla, Sosa, and Cavaleri 2013). Sin embargo, son tecnologías más costosas, especialmente la de las olas que aún se encuent= ra en desarrollo.

 

Tabla 1

Infraestructura eléctrica actual

 

 

Central

Subtipo central

 

Isla

Puesta en operación

V= ida operacio= nal

Potencia nominal (kW)

= Santa Cruz Solar Puerto Ayora

Solar

Santa Cruz/ Baltra

Diciembre de 2014

30 años

1.521

Baltra Solar

Solar

Santa Cruz/ Baltra

Enero de 2017

30 años

67

Santa Cruz Solar aislados

Solar

Santa Cruz/ Baltra

Diciembre de 2010

30 años

10

Baltra Eólico<= o:p>

Eólica

Santa Cruz/ Baltra

2014

25 años

2.250

Santa Cruz térmica

Térmica

(MCI)

Santa Cruz/ Baltra

1990

15 años

14.810

San Cristóbal= Solar Eolicsa<= /span>

Solar

San Cristóbal<= /p>

Julio de 2011

30 años

13

San Cristóbal= Eólico

Eólica

San Cristóbal<= /p>

2007

25 años

2.400

San Cristóbal t&eacut= e;rmica

Térmica

(MCI)

San Cristóbal<= o:p>

1991

15 años

7.100

Isabela Solar aislados

Solar

Isabela

Enero de 2011

30 años

8

Isabela Solar

Solar

Isabela

Noviembre de 2018

30 años

953

Isabela Térmica

Térmica

(MCI)

Isabela

2018

15 años

1.625

Floreana Perla Solar

Solar

Floreana

Septiembre de 2004

30 años

21

Floreana Solar aislados

Solar

Floreana

Septiembre de 2004

30 años

6

Floreana Térmica

Térmica

(MCI)

Floreana

2010

15 años

440

 

Perfiles de los Recursos

Actualmente, el modelo OSeMOSYS no soporta perfiles horarios de los recursos= ; en consecuencia, fue necesario cambiar de perfiles de 8.760 horas al año a 96 intervalos de tiempo al año, que es lo que soporta el modelo. Para esta transformación se agruparon mediante promedios, ocho grupos de tres horas cada uno, por cada día del año= , partiendo desde las cero horas; de esta forma se agregó = los perfiles de los recursos al modelo. La Figura 3 ilustra dicha transformación


&nbs= p;

en un día de temporada seca, donde se puede observar la diferencia entre los factores de planta7 d= el recurso eólico con una resolución de 8.760 horas y los factores de planta con 96 intervalos de = tiempo. Aunque se pierde algo de resolución, en general sigue la misma trayectoria del perfil del= recurso (velocidad del viento). Cabe mencionar que al tratarse de un día de<= span style=3D'letter-spacing:-.55pt'> temporada seca, donde el recurso está en sus registros m&aacut= e;s altos, los factores de planta registran sus máximos valores; por lo tanto, en meses como enero dichos factores de planta serán más bajos= ; también se puede observar el promedio anual de la capacidad de las plantas en cada isla.

 

Figura 3

Perfil del viento en un día de<= span style=3D'letter-spacing:-.05pt'> la temporada seca - 15 de agosto


 

Para los perfiles solares se realizó el mismo procedimiento. Como resultado= , se pueden observar factores de planta que llegan en promedio al 80% de la capacidad nominal en horas del mediod&ia= cute;a, con promedios anuales de alrededor de 20% en las cuatro islas. Aunque al igual que en los perfiles eólicos sigue la trayectoria del recurso, se pie= rde la resolución de los picos (irradiancia global). Ver Figura 4.

 

Figura 4

Perfil solar en un día de la temporada seca - 15 de agosto


 


7    Es la relación de uso de la planta a su capacidad nominal, es decir, el porcentaje de horas al año en que la planta funciona a máxima capacidad.


 

Costos Empleados en las Tecnologías

Los= costos = de las dos tecnologías renovables empleadas en el modelo = siguen decreciendo en el tiempo = debido a que son tecnolog&iacu= te;as maduras. Los costos= de las centrales termoeléctricas, así como su eficiencia, mejoran. Sin embargo, las termoel&eacut= e;ctricas son alimentadas por combustibles fósiles, específicamente por diésel, lo que las hace vulnerables al mercado de materias primas y a las reservas con las que cuenta cada país, mismas que están decreciendo en todo el mundo. Por tanto, se espera que los combustibles sigan al alza (Ver Tabla 2). Además, se debe considerar que el planeta está en transici&oa= cute;n a energías renovables para alcanzar la carbono-neutralidad en términos de emisiones de G= EI que permitan mitigar el cambio climático.

 

Tabla 2

Opciones de costos de electricidad


 

1<= /span>

Opciones <= /span>de planes


Costos de inversión (USD/kW)


Costos fijos de O&M

(USD/kW/yr)


Costos Variables de O&M

(USD/kW/yr)


de electricidad

2019

2050

 

2019

2050

 

2019

2050

Wind

2.114

1.158

 

35

17

 

0

0

Solar PV

1.740

960

 

17

10

 

0

0

Thermal (ICE)

1.200

1.200

 

20

20

 

1<= /p>

1<= /p>

1 Fuente: Fundación Bariloche.

 

 

Resultados y Discusión

OSeMOSYS es un modelo de código abierto que permite su uso en diferentes regiones del mundo, así= ; como también mejoras en el mismo, que cuenta con casos de estudio en más de cincuenta países alrededor= del mundo8 (Detailed Model Documentation, 2023). Se mostrarán algun= os casos de uso, incluido el caso de uso en las islas Galápagos con mayor detalle, esto con fines ilustrativos del alcance = del modelo.

 

Casos de uso del Modelo OSeMOSYS en el Mundo

El trabajo de (De Moura, Legey, an= d Howells 2018) muestra las ventajas y desventajas en los procesos de interconexión energética con los países vecinos de Brasil (Argentina, Bolivia, Guayana, Paraguay y Perú). Sus escenarios fueron modelados con OSeMOSYS y comparados con los resultados del modelo OSeMOSYS SAMBA que incluye los países de Sudamérica. Como resultado, se<= span style=3D'letter-spacing:-.75pt'> obtuvo que Sudamérica tiene una alta dependencia de la generación hídrica. La comparación de escenarios SAMBA indica una reducción de hasta 23 GW en la expansión de la capacidad instalada brasileñ= ;a y una adición de 5 GW en la capacidad instalada de los demás países de Sudamérica. Además, = se mostró que paí= ses como Guyana y Bolivia, que tienen gran potencial hídrico pero = poco financiamiento, pueden mejorar su infraestructura eléctrica con acue= rdos energéticos con Brasil.

 

El trabajo de Quevedo and Moya (20= 22) presenta cuatro escenarios de expansión eléctrica con OSeMOSYS que han servido de insumo para los tomadores= de decisiones. Se detallan las características de vulnerabilidad geográfica y su alta dependencia a las importaciones de combustibles fósiles. En consecuencia,= establecieron como meta lo acordado por República

 


<= /o:p>

8    Las aplicaciones incluyen: EE. UU., México, Costa Rica, Nicaragua, Parag= uay, Brasil, Bolivia, Ecuador, y toda Sudamérica (modelo SAMBA, país por país). UE27+ Reino Unido, Suiza y Noruega (pa&iac= ute;s por país - modelo OSeMBE); todo el Continente africano (paí= ;s por país - modelo TEMBA), Mauricio, Azerbaiyán, Irán, Pakistán, India, Vietnam, Nepal, Bután, Sri Lank= a, Tailandia, Filipinas, Malasia, Mongolia, e Indonesia.


&nbs= p;

Dominicana en el Acuerdo de París, en donde, estimaron que para alcanzar dicho acuerdo sobre la reducción de un 25% = de GEI para el año 2030, en comparación con las emisiones del a&ntil= de;o 2010, se necesita un total de inversión de USD 11.404 millones a una tasa de descuento de 4%.

 

Finalmente, el trabajo de Tchandao = et al. (2023) modela la expansión eléctrica de Togo al año 2050. Se proponen tres escenarios; uno referencial que mantiene los niveles actuales de inversión en<= span style=3D'letter-spacing:-.75pt'> energía renovable, otr= o que propone cero emisiones de GEI al 2050 y el último que añade penalidades a las emisiones de GEI. Como resultado,<= span style=3D'letter-spacing:-.2pt'> el modelo propone energía solar e importaciones de combustibles<= span style=3D'letter-spacing:-.1pt'> como solución óptima a largo plazo, esto por encima del gas y de las hidroeléctricas, lo = que implica una inversión diez veces mayor a la actual.

 

Caso de Us= o en Galápagos

Escenarios de Expansión Eléctrica de Galápagos

Para el caso de las islas Galápagos se propusieron dos escenarios= ; el Referencial (REF) que sigue una trayectoria com= o la actual, con pequeñas inversiones en energías renovables y que= al final del periodo tiene una alta proporción de energía no renovable proveniente de las centrales térmicas; y el escenario de descarboniza= ción profunda llamado en este trabajo, Futuro Solar y Eólico (S&= ;E). Ver Tabla 3)9.

 

Tabla 3

Escenarios = a largo plazo de la expansión eléctrica en Galápagos


Escenarios        &= nbsp;           &nbs= p;             = Descripción<= /b>

<= span lang=3DES style=3D'font-size:1.0pt;mso-bidi-font-size:12.0pt'>

Referencial (REF)        &= nbsp;           &nbs= p; Energía térmica predominante (MCI) sin grandes inversiones en energías renovables (ER), con pequeñas adiciones en ER, alcanzando alrededor = de 50 % de energía limpia al 2050 en el sector eléctrico en el archipiélago.


Futuro solar y eólico (S&E) Fuerte inversión solar y eólica, alcanzando hasta un 75 % de energía limpia en 2030 y el 100 % hasta = 2040 en el sector eléctrico.

 

El escenario Referencial (REF) bus= ca seguir el mismo ritmo de inversión en energías renovables, es decir, sin mayor inversión en energías limpias; de esta forma= se mantiene un mínimo de generación termoeléctrica. Como consecuencia, la matriz eléctrica no cambia significativamente, y las centrales térmicas (MCI) permanecen como la tecnología predominante en las islas (ver Figura 5). En la misma figura se puede observar el almacenamiento de energía eléctrica en naranja y la capacidad necesa= ria de almacenamiento hasta el año 2050. A pesar de que las centrales t&ea= cute;rmicas siguen dominando hasta el final del periodo modelado, este escenario alcanza alrededor de un 50% de electricidad limpia de fuentes renovables. Tambi&eacut= e;n, se observa en el cuadrante de capacidad, que una vez cumplida la vida útil de l= as centrales térmicas, el modelo las reemplaza con centrales solares; lo mismo sucede con la tecnología eólica, que es reemplazada igualmente por la solar debido a que la tecnología eólica es más costosa. Al final del periodo, la mayor parte es energía térmica y solar. Con respecto a la capacidad de las baterías, es necesario alcan= zar hasta 6 MW hasta el final del periodo, lo que permite cubrir la curva de demanda eléctrica.=

 

En la Figura 6 se puede observar= con mayor detalle la generación y capacidad eléctrica por isla; en Santa Cruz y San Cristóbal en el escenario REF se observa el mismo patrón que en el agregado de las cuatro islas, con un aumento tanto en la generación como en la capacidad solar.


<= /o:p>

= 9    Es importante señalar que se pueden probar los escenarios que sean necesarios de acuerdo con la información que se tenga disponible, en este caso se han mostrado solo dos escenarios para efectos ilustrativos del modelo OSeMOSYS.


&nbs= p;

Además, se observa un reemplazo de las centrales eólicas por las solares y una disminución de la capacidad de las centrales térmicas (MCI).

 

Por otro lado, en la Figura 7 se observa que Isabela alcanza a cubrir la demanda eléctrica con alrededor del = 70% de energía solar y con mayor capacidad de baterías, de este modo disminuye la capacidad térmica en la isla. En lo que concierne a Floreana se observa que al final del periodo se tiene sobre el 50% de energía solar, así como una disminución importante de capacidad térmica en la isla.

 

Figura 5

Generación, capacidad y almacenamiento en el escenario referencial (REF)


 

Figura 6

Gener= ación, capacidad y almacenamien= to de Santa Cruz y San Cristóbal en el escenario referencial (REF)


 

En el escenario de descarboniz= ación profunda llamado Solar y Eólico (S&E), donde se asume= n políticas agresivas de inversión en energ&iacut= e;a renovable, se alcanza alrededor de un 75% de energía limpia para el año 2030 y el 100% en el año 2040. Se observa que al final del periodo todas las centrales<= span style=3D'letter-spacing:-.55pt'> eólicas son reemplazad= as por solares debido a su menor costo; por otro lado, la capacidad térmica se reduce casi en su totalidad y la capacidad de almacenamiento para cubrir


&nbs= p;

la demanda eléctrica sobrepasa los 10 MW (ver Figura 8). En consecuenci= a, las emisiones de GEI por generación eléctrica son eliminadas a partir del año 2040.

 

Figura 7

Gener= ación, capacidad y almacenamiento de Isabela y Floreana en el escenario referencial (REF)


 

Figura 8

Generación, capacidad y almacenamiento en el escenario referencial Sol= ar y Eólico


 

En la Figura 9 del escenario S&E se puede observar en detalle por isla; en San Cristóbal se alcanza el 100% = de electricidad renovable en el año 2038, mientras que en Santa Cruz llega en el año 2040; esto de= bido a que es la isla más poblada del archipiélago. Con respecto a la expansión de capacidad, = se tiene, al igual que en el escenario anterior, que ambas islas reemplazan ta= nto las centrales térmicas como las eólicas por solares.

 

Las islas con menor població= ;n Isabela y Floreana alcanzan más rápido el 100% de electricida= d limpia, Isabela en el año 2034 y Floreana en el año 2026.= Por otro lado, ambas islas llegan al final del periodo mo= delado con 100% de capacidad solar (ver Figura 10).


 

Figura 9

Gener= ación, capacidad y almacenamien= to de San Cristóbal y Santa Cruz en el escenario Solar y Eólico (S&E)


 

Figura 10

Gener= ación, capacidad y almacenamiento de Isabela y Floreana en el escenario Solar y Eólico (S&E).


 

Costos de Inversi&oacu= te;n y de Operación y Mantenimiento

Para los costos de inversió= n, de operación y mantenimiento, tanto los fijos como los variables, se us= a el valor presente neto en dólares del año 202110. La Figura 11 muestra los costos d= e inversión, la primera década del periodo modelado el escenario Referencial (REF) es alrede= dor de USD 25 millones menor. Sin embargo, en las siguientes dos décad= as del periodo, el escenario S&E tiene menos costos de inversión. En total, se t= iene que el escenario REF sin mayor<= span style=3D'letter-spacing:-.3pt'> inversión es apenas USD 5 millones menos costoso que el escenario de descarbonización profunda del sector eléctrico en términos de inversión.

 


10   Año que se usa frecuentemente para traer valor presente debido a la apreciación y estabilidad que tuvo el dólar estadounidense.


 

Figura 11

Inversión por periodos y escenarios


 

En lo referente a los costos fijos de operación y mantenimient= o, se tiene que el escenario REF en la primera década del modelo es menor que el escenario más ambicioso. En la segunda década es ligeramente menos costoso, y en la última es m&aacu= te;s costoso (ver Figura 12). Cabe recordar= que los costos fijos usados tanto en la tecnología solar como en la eólica son más altos (ver Tabla 2).

 

Figura 12

Costos fijos de operación y mantenimiento por periodos y escenarios


 

Las tecnologías renovables no presentan costos variables de operación y mantenimient= o al usar recursos de libre disponibilidad, mientras que las centrales termoeléctricas tienen costos de 1,04 USD/MWh, a los que se les agregan los costos de importación de diésel que son altos. Como resultado, los costos variables del escenario REF tienden al alza a pesar de mantener subsidios. En la primera década del modelo los costos de ambos escenarios parten relativamente iguales, sin embargo, en los siguientes periodos son mucho menores.<= span style=3D'letter-spacing:-.25pt'> Al final del periodo, se tiene que = en términos de costos variables el escenario REF es más del doble que el escenario


&nbs= p;

S&E, con una diferencia aproximada de USD 170 millones. La diferencia marginal en los costos de inversión y fijos entre el escenario REF y S&E no se compara con la diferencia en costos variables= . Por tanto, apostar por la descarbonización del sector eléctrico es una inversión menos costosa que mantener las políticas energéticas actuales (ver Figura 13).

 

Figura 13

Costos de operación y mantenimiento= variables por periodos y escenarios.


 

Conclusiones

El modelo OSe= MOSYS de código abierto es flexible y versátil; fácil de usar; y tiene una curva corta de aprendizaje, = lo que le permite desarrollarse rápidamente; con casos de uso en varias regiones del mundo y una importante comunidad internacional que hace posibl= e su evolución. Una de las debilidades del modelo es que no soporta perfiles horarios de los recursos renovables. Esto puede ser debido a la cantidad de parámetros y variables que toma en cuenta. En consecuencia, el modelo requiere hacer una abreviación de los dat= os y se pierde algo de resolución. Por el contrario, el modelo trata con mayor detalle los costos, lo que permite ingresar costos de inversión, costos de operación y mantenimien= to, fijos y variables, tasa de descuento de estos, costos de inversión de almacenamiento, costos de recuperación, entre otros.

 

Sobre los costos necesarios para la descarbonización del sector eléctrico, el modelo mostró= ; que en términos de inversi&oacut= e;n el escenario referencial es apenas USD 5 millones menos costoso que el escenario de descarbonización profunda (S&E). En términos de costos fijos de operación y mantenimiento, se observa que el escenario REF es ligeramente menos costoso<= span style=3D'letter-spacing:-.75pt'> que el escenario S&E (USD millo= nes).

 

Finalmente, los costos de O&M variables en el escenario S&E son considerablemente menores que los del escenario REF con un ahorro de USD 170 millones hasta el final del periodo<= span style=3D'letter-spacing:-.05pt'> en el 2050. En conclusión, no solo es posible la descarbonización del sector eléctrico, sino que tambi&ea= cute;n es una mejor inversión que continuar con las políticas actual= es en el sector. Además de considerar lo anterior, se podría agr= egar lo que genera el turismo de conservación que valora la biodiversidad de las islas y su proyección de desarrollo con energías renovables, sumados los beneficios incalculables que se obtendrían al eliminar la posibilidad de derrames de combustibles en las costas del archipiélago= . Por otro lado, es necesario recalcar que al tratarse


&nbs= p;

de un archipiélago megadiverso estas inversiones en energías alternativas se tienen que hacer con todas las precauciones debidas.=

 

Por tanto, es sumamente importante= que antes de comenzar cualquier proyecto de energías renovables se hagan= los estudios necesarios a detalle de todos los posibles impactos ambientales que prioricen el uso de zonas ocupadas con asentamientos humanos. Una de las fo= rmas podría ser la energía no distribuida solar mediante paneles y baterías para el uso en viviendas y locales comerciales, lo que resulta clave en hábitats limitados como los de las islas Galápagos. Considero que de esta manera se puede incidir en la soberanía energética de las islas.

 

Es importante recalcar que la transición de energías no renovables a renovables no se enfoq= ue enteramente en la reducción de emisiones de GEI, ya que por ejemplo las emisiones de GEI que producen las Galá= pagos son extremadamente marginales en la contribución global y debido a los permanentes vientos alisios= , estas emisiones no se quedan en las islas. Por lo tanto, corresponde a toda la comunidad atacar los problemas de raíz y a la comunidad científica crear el conocimiento necesario para avanzar de forma aceler= ada hacia un futuro sostenible.

 

Para un próximo trabajo, se pueden desarrollar múltiples escenarios, como un escenario de expansión eléctrica con energía de olas, uno con energía geotérmica y/o uno con energía solar distribuida. Ademá= ;s, se pueden incluir todos los escenarios con y sin subsidios a los combustibl= es, los cuales representan una gran problemática en el país. Asimismo, se pueden proponer escena= rios de expansión eléctrica que incluyan transporte terrestre eléctrico y/o marítimo, = en donde la información disponible y la calidad de esta son las únicas limitantes del alcance del modelo.

 

 

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WWF. 2003. “Migración y Ambiente En Las Islas Galápagos ISBN 978-9978-5= 8-129-2 - Libro. 2003. https://isbn= . cloud/978997= 8581292/migracion-y-ambiente-en-las-islas-galapagos/.

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