Assessing Uruguay’s green hydrogen

potential: A comprehensive analysis

of electricity and hydrogen sector

optimization until 2050

Evaluación del potencial de hidrógeno verde en Uruguay:

Un análisis integral de la optimización de los sectores de

electricidad e hidrógeno hasta el 2050

1.- Technical University of Munich , TUM School of Engineering and Design, Chair of Renewable and Sustainable Energy Systems

andrea.cadavid@tum.de https://orcid.org/0000-0001-8941-6528

2.- Sidnei Cardoso, School of Business IAG PUC-Rio, sidnei.cardoso@phd.iag.puc-rio.br, https://orcid.org/0000-0002-7212-6652

3.- Universidad de la República, Facultad de Ingeniería, Grupo Interdisciplinario Ingeniería Electroquímica verodiaz@fing.edu.uy

https://orcid.org/0000-0001-5335-6404

4.- Technical University of Munich , TUM School of Engineering and Design, Chair of Renewable and Sustainable Energy Systems

thomas.hamacher@tum.de https://orcid.org/0000-0002-0387-8199

Andrea Cadavid Isaza

, Thushara Addanki

, Verónica Díaz

Thomas Hamacher

Recibido: 15/11/2024 y Aceptado: 09/1/2025

Uruguay se posiciona como potencial exportador de hidrógeno verde y derivados, según lo descrito en

la hoja de ruta. El objetivo principal de este estudio es explorar cómo reacciona el sistema eléctrico del

país a los objetivos delineados en la hoja de ruta. Otro objetivo es analizar cómo podría desarrollarse el

sector del hidrógeno verde basado en el precio de mercado del hidrógeno. Se propone una metodología

para distribuir los costos entre ambos sectores. El análisis revela que cada escenario presenta desarrollos

muy diferentes de los sistemas energéticos en Uruguay. Son necesarias expansiones sustanciales en

las capacidades de energía renovable, particularmente fotovoltaica y eólica, para apoyar una economía

del hidrógeno. Los escenarios impulsados por el mercado, especialmente con precios más altos del

hidrógeno, muestran aumentos signicativos en las capacidades de los electrolizadores. La viabilidad

económica de la producción de hidrógeno a precios más altos sugiere que las exportaciones de

hidrógeno podrían convertirse en un negocio rentable para Uruguay.

Uruguay is setting out to become a leading exporter of green hydrogen and its derivatives, as described

by the hydrogen roadmap. The primary aim of this study is to explore how the country’s electricity

system reacts to the goals outlined there. Another aim is to analyze how the green hydrogen sector

could develop based on the market price for hydrogen. A methodology for distributing the costs among

both sectors is proposed. The analysis reveals that very dierent pictures are painted in each of the

scenarios, leading to completely dierent developments of the energy systems in Uruguay, substantial

expansions in renewable energy capacities, particularly photovoltaic and wind power, are necessary to

support a hydrogen economy. The market-driven scenarios, especially at higher hydrogen prices, show

signicant scale-ups in electrolyzer capacities. The economic viability of hydrogen production at higher

price points suggests that hydrogen exports could become a protable venture for Uruguay.

PALABRAS CLAVE: Hidrógeno, Modelo de sistema energético, Optimización, Coste nivelado de la

electricidad, Coste nivelado del hidrógeno, Uruguay.

KEYWORDS: Hydrogen, Energy system model, Optimization, Levelized cost of electricity, Levelized

cost of hydrogen, Uruguay.

Resumen

Abstract

1. INTRODUCTION

The increasing global focus on green hydrogen as

an essential energy carrier reects a widespread

commitment to decarbonizing energy systems,

particularly in sectors where direct electrication is

impractical (IRENA, 2022). To meet the temperature

goals set by the Paris Agreement (United Nations,

2015), achieving signicant emission reductions

across all economic sectors is essential. This

requires decarbonizing energy, advancing

electrication, increasing the share of renewable

energies, and improving energy eciency. Green

hydrogen, produced from renewable sources via

water electrolysis, stands out as a clean energy

vector (Kumar & Lim, 2022; Stolten & Emonts,

2016) with a high energy-to-weight ratio (Chi &

Yu, 2018). Its production process, which relies on

solar, wind, or hydroelectric power, positions it as

an environmentally friendly and sustainable option

(BP, 2022; Kumar & Lim, 2022; Sánchez Delgado,

2019). With zero greenhouse gas emissions, green

hydrogen holds signicant potential as a substitute

for fossil fuels (Kumar & Himabindu, 2019; Laguna-

Bercero, 2012), particularly in “hard-to-abate”

sectors. For example, Hydrogen can be utilized in

fuel cells to regenerate electricity, power cellular

radio bases in remote locations, or drive fuel cell

electric vehicles, among other applications. It also

has the potential to replace natural gas in various

heat-dependent processes. Hydrogen can also

play a critical role in reducing iron oxide (iron ore)

to produce iron (Direct Reduction Iron, or DRI) and

steel, eliminating the need for fossil fuels in one

of the most challenging industrial processes to

decarbonize.

Uruguay, with its advantageous geographic

location and robust renewable energy

infrastructure, is well-positioned to leverage green

hydrogen production for export and to foster

the development of new industries (International

Energy Agency, 2019, 2022, Appendix A;

Ministerio de Industria, Energía y Minería,

2023a). The country has formulated its strategy,

embodied by the “Green Hydrogen Roadmap

in Uruguay”(Ministerio de Industria, Energía y

Minería, 2023b), to cultivate a domestic market for

green hydrogen and position itself as a prominent

exporter of this renewable energy resource. In the

Roadmap it is recognized that Uruguay’s potential

for renewable energy production far exceeds the

future needs of its electricity system. Uruguay’s

stability, transparent legal framework, and a

strong reputation for honoring contracts and

commitments make it an appealing destination for

large-scale projects in green hydrogen and related

elds. Uruguay is uniquely positioned to combine

hydrogen with biogenic carbon dioxide (CO2) to

produce green methanol. This methanol can be

converted into synthetic gasoline, gas, oil, or jet

fuel. Uruguay can create new energy sources that

fully replace conventional fossil fuels by harnessing

renewable resources to produce green hydrogen

and utilizing agro-industrial waste. In the short

term, Uruguay aims to develop a domestic market

for green hydrogen and its derivatives, focusing

on heavy and long-distance transportation and

green fertilizer production. The national hydrogen

roadmap projects that the costs of renewable

energy in Uruguay by 2030 would enable green

hydrogen production at values between 1.2

and 1.4 USD/kgH2 in the western region and

between 1.3 and 1.5 USD/kgH2 in the eastern

region. These competitive costs position Uruguay

as a strong contender in the export market for

hydrogen derivatives. In the long term, Uruguay

will explore the potential for oshore green

hydrogen production to further enhance its export

capabilities (Ministerio de Industria, Energía y

Minería, 2023b).

The roadmap is not the rst study that investigated

Uruguay’s hydrogen potential and costs for

producing hydrogen in the country.

(Corengia et al., 2020) present a case study

where they establish a simulation-based sizing

of grid-connected electrolyzer plants for the case

of Uruguay. Their limiting factor is the available

surplus electricity from the grid; the only service

that the electrolyzer would provide to the electricity

system is peak shaving. They concluded that the

produced hydrogen is too expensive compared

to traditional fuels and that the utilization of the

electrolyzer plants is too low.

(Corengia & Torres, 2022) propose a design that

involves selecting power sources, electrolyzer

types and sizes, and energy storage devices for

hydrogen production in Uruguay at various scales.

The study highlights solid oxide electrolyzers as

promising, with alkaline electrolysis preferred

over proton exchange membrane electrolysis

among current market options. It emphasizes the

importance of complementarity in energy sources

and challenges the idea of producing hydrogen

solely to use energy surplus and avoid curtailment.

(Ibagon et al., 2023) developed a model to optimize

the capacity of renewable energy facilities,

electrolyzers, storage systems, and hydrogen

transport methods to minimize hydrogen costs

in Uruguay. It analyzed the impact of hydrogen

demand scale and technological maturity (2022 vs.

2030) on production costs and the supply chain.

For medium and small demands, conversion,

processing, transport, and storage costs are

similar to energy costs. For larger demands, the

cost of renewable energy represents the most

relevant cost and pipelines are the most cost-

eective for transporting compressed gas, while

trucks are preferred for smaller demands. For

medium demand, longer distances favor liquid

organic hydrogen carriers by truck, and shorter

distances favor trucks for compressed gas. The

study predicts that advancements in technology

will reduce hydrogen production costs from 3.5

USD/kg in 2022 to 2.3USD/kg by 2030.

1.1. Literature review

The study from (Bouzas et al., 2024) examines

hydrogen production costs in Uruguay, focusing on

the impact of various techno-economic parameters.

It highlights that electricity costs are a major driver

of hydrogen production costs, especially when

low capacity factors make electrolyzer CAPEX

and OPEX more signicant. Water costs are found

to be negligible. The Weighted Average Cost of

Capital (WACC) also has a substantial inuence,

particularly in scenarios with lower full load hours

where electrolyzer investment costs dominate.

Overall, WACC signicantly impacts investment-

based costs.

Previous studies on hydrogen production in

Uruguay have focused on identifying optimal

renewable locations and estimating production and

transportation costs to centers like Montevideo.

However, they haven’t explored integration with

the existing electricity system, interactions with

current infrastructure, or the potential synergies of

an integrated hydrogen and electricity system. This

paper aims to address these gaps by assessing

how hydrogen production can be integrated with

the electricity system, evaluating infrastructure

interactions, and determining incentives for

expansion. It also provides the levelized costs of

electricity and hydrogen within such integrated

systems.

100

2. METHODOLOGY AND MODEL DESCRIPTION

This study employs a linear programming energy

system optimization model called urbs (Dorfner,

2016; Dorfner et al., 2019). The software allows

the optimization of multi-commodity energy

systems. It incorporates inter-temporal planning

to analyze development pathways, consisting of a

“perfect foresight” model, which means all future

variables are dened from the beginning. The

model minimizes the total costs of the system,

all while fullling the given commodity demands.

For further information about the mathematical

background or the tool in general, check the

documentation (Dorfner, 2023). The model in

this study encompasses the existing Uruguayan

electrical system alongside planned expansions,

optimizing the system expansion and operation

The creation of the energy model requires the

creation and denition of dierent input data and

parameters. All the interactions between dierent

technologies and commodities can be visualized

and understood through a reference energy

system. The reference system for this case can be

seen in Figure 1.

In the case of Uruguay, there are dierent

available technologies to generate electricity,

from intermittent or non-conventional renewable

energies, there are present solar and wind energy,

to be more specic, we can nd the following

technologies: Open eld PV, Rooftop PV, Wind

onshore Level I, Wind Onshore Level II, and Wind

Oshore. For each of them we have a determined

potential and generation time series, the specics

are discussed in below section 2.3. All of these

have a temporal generation time series as input,

and their output is electricity. More traditional

renewable energies such as biomass and hydro

have the advantage of being exible when they

generate electricity. For biomass, yearly energy

potential is the limit. For hydro, the dam can be

used to store the water coming from an inow time-

for electricity generation and hydrogen production.

The analysis is an inter-temporal approach

that spans multiple reference years, including

2021, 2025, 2030, 2040, and 2050, providing

a comprehensive outlook on the evolution of

Uruguay’s electricity and hydrogen landscape.

Uruguay is modeled as a single node in this

model, so the costs and respective energy losses

of any transmission or distribution lines within the

country are not considered. In this section, we will

examine the specic assumptions, models, and

data utilized throughout the study.

2.1. Reference Energy System

series. This stored water can either be directed

to its powerplant to generate electricity or be

spilled. The last technology available for electricity

production is the Oil plant, which consumes oil

for which there is a specic cost associated and

generates electricity but also direct CO2 emissions.

The domestic demand then consumes electricity;

this demand has to be fullled every single hour.

The electricity could then be stored in batteries,

so generation can be shifted in time. Electricity

is also an input for hydrogen production using

electrolyzers. This produced hydrogen to fulll the

specic demand or to sell hydrogen for a specic

price. The produced hydrogen can be stored in a

compressed hydrogen storage and then released

for use at another time. In addition to storing,

there is the possibility of curtailing electricity, which

means getting rid of overproduction when this is

the cost-optimal solution.

101

Uruguay, like any other country, assumes an

increase in its economic growth and, therefore,

its electricity consumption. The Ministry of Energy

and Mining has scenarios and projections of the

electricity demand of the national interconnected

system (SIN) until 2040 (Ministerio de Industria,

Energía y Minería, 2018). ‘For this study the

Baseline scenario (“Tendencial”) was used, where

there are no signicant changes in the demand

distribution by sector from 2018 onwards.

The growth rate from the last years was then

2.2. Demands

Figure 1 Reference Energy system for urbs model for Uruguay

Table 1. Yearly electricity demand of the National Interconnected System (SIN)

extrapolated to calculate the expected demand

for 2050. The respective values can be seen in

Table 1.

Year

Yearly Electricity Demand [GWh]

2021

11,078

2025

12,190

2030

13,525

2040

16,747

2050

20,608

102

All these calculations refer to the total yearly

electricity demand. The hourly prole is taken from

the electricity market operator (ADME, 2024) for

the year 2021 is used as a base to disaggregate

future yearly demand into hourly values. In

the case of hydrogen demand, the roadmap

(Ministerio de Industria, Energía y Minería, 2023b)

gives information in terms of electrolyzer capacity,

market size, and one singular value for the yearly

production of 2040 of one million tons H2,

corresponding to 9 GW of electrolyzer capacity.

With this last value, we can derive that for each

GW of electrolyzer, they are assuming 111.111 kg

of hydrogen a year, and this ratio is used for all the

other years. Since the roadmap goes until 2040,

The renewable potential analysis is carried out

using the open-source tool pyGRETA (Kais Siala

et al., 2022). The tool performs customizable

land use eligibility analysis based on 38 dierent

criteria (Ryberg et al., 2018) at a high spatial

resolution of 250m x 250m to estimate the

available locations and total potential of solar

and onshore wind technologies of a given region.

The tool also analyses exclusive economic zones

up until a seabed depth of 50m to calculate

the xed oshore wind potential. In addition to

potential calculation, the tool also reads historical

weather data from MERRA-2 (Global Modeling

and Assimilation Oce (GMAO), 2015b, 2015a)

and Global wind atlas (Global Wind Atlas 3.0,

2022) to calculate the hourly capacity factors of

all possible locations. The detailed methodology

is described in sources (Kais Siala et al., 2022)

and (AUTHOR, 2024). Figure 2 shows the results

of this analysis. The map on the left shows the

locations of open eld and roof top PV potentials

and the map on the right shows the locations of

but the time scope of this study is until 2050,

some assumptions were required to calculate the

2050 value; we went for a conservative approach

of an electrolyzer capacity and demand increase

of 20%, which results in 1.2 million tons for 2050.

The respective original and calculated electrolyzer

capacities and demands can be seen in Table 2.

Table 2. Specied electrolyzer capacities and estimated hydrogen demands. Based on:

(Ministerio de Industria, Energía y Minería, 2023b)

2.3. Renewable Potentials

onshore and oshore wind potentials considered

in the energy model of Uruguay. The capacity

factors for solar PV technologies are very similar

across Uruguay as it is mostly latitude dependent.

So, the total potential of 410 GW for Open eld PV

and 22 GW of Rooftop PV is considered to have

the same hourly capacity factor time series. For

wind technologies, the capacity factors are highly

dependent on the location’s geography and are

dierent across the country. So even though the

total onshore wind potential of Uruguay is much

higher, only the highest two levels of locations

are considered with 49.3% and 46.5% capacity

factors, respectively. For simplicity within the

model, the capacity factor of 54.2% taken from

an average location is assumed for the oshore

region. It should be noted that the capacity factors

of this magnitude for wind technologies are one of

the highest in the whole world, which makes them

cost-competitive compared to PV technologies

despite drastic cost reductions projected in the

future for PV. See below section 2.4.

Electrolyzer Capacity [GW]

Hydrogen demand [kton H2/year]

2025

0.1

11.11

2030

0.6

66.66

2040

1000

2050

10.8

1200

103

Figure 2. Renewable Energy Potentials from pyGRETA for Uruguay. Legend: Technology Potential

Yearly Capacity Factor

2.4. Technoeconomic Data

The urbs model requires various techno-economic

data inputs, including CAPEX and OPEX for all

technologies, fuel costs, and broader economic

parameters such as the Weighted Average Cost

of Capital (WACC) and discount rates for long-

term investments. For the technology-specic

data, we intentionally minimized the number of

dierent sources used. By relying on a limited

set of sources, we ensured that the assumptions

and methodologies applied across technologies

are consistent, making comparisons between

them fairer and uniform. This approach reduces

the risk of discrepancies that could arise from

using data with varying underlying assumptions,

thereby enabling a more balanced evaluation

of the dierent technologies. Investment and

operational costs vary signicantly across regions,

particularly Latin America. To estimate the specic

costs for Uruguay, we employed the methodology

introduced by the Inter-American Development

Bank in their report on optimizing the Latin

American electrical system (Inter-American

Development Bank & Paredes, 2017).

104

Table 3. Sources for the Country and Temporal-specic Input Techno-economic data

This approach involves recalculating investment

and fuel costs for each country in the region,

by using specic factors per technology and

fuel. In our case we use Brazil as a baseline and

recalculated the factors. For all technologies,

we utilized the Net Zero 2050 scenario values,

using Brazil as the baseline. The only exception

was Rooftop PV, for which we selected techno-

economic data from Europe instead of Brazil, due

to the signicantly lower costs reported for Brazil.

According to market reports, such as the recent

ones from Wood Mackenzie (Mackenzie, 2023,

2024), the current range for rooftop PV in Brazil

is between 1200 to 1500 USD/kW, which aligns

more closely with the European starting point of

1120 USD/kW in 2021. The Table 3 summarizes

the matching of dierent data sources used to

create the country-specic and year-specic input

data.

As previously mentioned, key economic

parameters still need to be dened. Studies by

(Steinbach & Staniaszek, 2015), (García-Gusano

et al., 2016), and the (OECD, 2021),

have specically examined the role of discount

rates and the Weighted Average Cost of Capital

(WACC) in energy system models, highlighting

their inuence on long-term investment outcomes.

The WACC is crucial for assessing investments,

representing the cost of capital in a region and

sector, while the social discount rate reects

the time value of money and opportunity cost

of capital. Lower discount rates favor renewable

energy, while higher rates favor fossil fuels. Due to

economic uncertainty in Latin America, adopting

a default WACC is inappropriate. Therefore, a

region-specic WACC for Uruguay was dened

using an approach proposed in the PTX Business

Opportunity Analyser tool (Oeko-Institut, 2023),

where country-specic Equity Risk Premiums

(Damodaran, 2024) are used, resulting in a WACC

of 7.38%, compared to the 5% in Uruguay’s

Hydrogen Roadmap. The WACC will be applied

uniformly across all timeframes due to the lack of a

reliable projection method. The study also adopted

an average social discount rate of 3.894% for

South America, based on recommendations for

Latin American countries (Moore et al., 2020).

Technology

Power plants

Electrolyzers

Batteries

drogen Storage

105

Figure 3. Methodology used for the cost distribution among the products, commodities or sectors

To calculate the levelized cost of electricity and

hydrogen, we consider their interrelation, as the

electrical infrastructure is aected by hydrogen

production. Using the urbs framework, our

objective is to minimize total global costs for both

electricity generation and hydrogen production. To

be able to assign the costs between these two

products we will use a methodology and approach

commonly used in life cycle analysis called

subdivision and complemented by allocation, the

graphical description of the process can be seen

in Figure 3.

Subdivision tries to assign inputs, ows, or, in our

case, costs to the singular products. The second

approach, allocation, distributes the eects and

impacts of a system equitably based on specic

characteristics of the co-products. For the

subdivision step, the investment, x, and fuel costs

to produce electricity, as well as batteries and their

costs, are assigned to electricity generation, and

the costs only related to hydrogen such as costs

for electrolyzers and H2-Storage are assigned to

hydrogen production. For the allocation step, we

take into account that the total electricity that gets

produced is used as a direct electricity demand

This methodology creates a relevant and adaptable

database for the region.

Based on the techno-economic data discussed

in this section and the estimated capacity factors

for dierent renewables from above section 2.3,

the Levelized cost of Electricity from Open-eld PV

will decrease considerably from 48 USD/MWh in

2020 to 22 USD/MWh in 2050. For onshore wind,

the decrease is from 29-31 USD/MWh in 2020 to

only 25-27 USD/MWh in 2050. For oshore wind,

LCOE decreases from 101 USD/MWh in 2020 to

41 USD/MWh.

2.5. Levelized cost of Electricity and Hydrogen

and also used in the electrolyzer, so the total

electricity generation costs, are allocated between

the electricity demand and hydrogen production,

this costs of the electricity used for H2 production

get summed to the costs which were only related

to H2 and this constitutes our total hydrogen

costs.

This method ensures a fair distribution of

investment and operational costs, recognizing

that higher hydrogen demand requires additional

investment in the electrical system but may also

enable greater integration of low-cost renewable

energies. This approach is suitable because

our model optimizes the overall system costs,

ensuring fair cost and benet assignment given

the interrelated nature of electricity and hydrogen

production.

Elec Only Cost:

Power plants, Storage

& Transmission

H2 Only costs:

Electrolyzers, Storage

& Transport

Total

System

Costs

Subdivision

Elec Costs for

Allocation

by energy

Total

Electricity

Costs

Elec Demand

Elec for H2

Total

Hydrogen

Costs

106

To analyze the energy model and system

optimization for hydrogen production and

electricity generation in Uruguay, which aims

to produce and export hydrogen, three distinct

scenarios were developed:

Baseline Scenario (Only Electricity): This scenario

represents the expected evolution of the energy

system without implementing a hydrogen

economy. It serves as a reference point, illustrating

the system’s behavior under current policies and

technologies focused solely on electricity supply.

The model simulates the current trajectory of the

energy system, highlighting potential challenges

and limitations in meeting future electricity

demands without hydrogen integration.

National Hydrogen Roadmap Implementation

(H2 Roadmap): This scenario models the

implementation of the country’s national hydrogen

roadmap. Specic goals for hydrogen production

and utilization are set for each year, reecting the

2.6. Scenario denition

government’s strategic plan to integrate hydrogen

into the national energy mix. The roadmap includes

targets for hydrogen production capacities, and

infrastructure development. The model evaluates

the roadmap’s targets, assessing the required

capacities, investments, and resulting costs.

Market-Driven Hydrogen Production (1 to 3

USD/kg H2): In this set of scenarios, a price

signal for hydrogen is introduced, allowing the

model to determine the optimal production

and export quantities based on protability. The

model assesses whether hydrogen production is

economically viable and adjusts the production

levels accordingly. Various price points are

considered, which are kept constant throughout

the analysis period to evaluate their impact on

the global energy system. The model explores

the economic dynamics of hydrogen production,

considering various price signals and their inuence

on production decisions and export potential.

3. RESULTS AND DISCUSSION

The results show notable expansion within

the electricity sector, driven by the increased

demand necessitated by hydrogen production.

The study thoroughly evaluates the generation

matrix, Hydrogen production quantities, and their

corresponding levelized costs. The information

from all gures can also be found in the

supplementary material.

Figure 4. Installed Capacities for electricity generation per scenario and year.

107

Figure 4 illustrates the installed capacities,

and Figure 5 the electricity generation and

consumption per scenario and year. In the only

electricity scenario, there is minimal expansion up

to 2025 and 2030, with the only changes being the

addition of an already planned biomass plant and

the decommissioning of the oil plant. Hydropower

plants provide enough exibility to meet electricity

needs despite lower capacity. By 2040, signicant

changes occur as existing renewable energy

plants end their life. Photovoltaic (PV) capacity

increases signicantly by 2040 and 2050. On the

contrary, onshore wind capacity will decrease,

while oshore wind will see new installations by

2050.

Regarding electricity generation and consumption,

in all scenarios, the year 2021 shows minimal

In the hydrogen roadmap implementation

scenario, the installed capacity for 2021, 2025,

and 2030 mirrors the electricity-only scenario,

with existing renewable energies and planned

expansions being sucient for the early stages.

By 2040, signicant hydrogen demand and

depreciated renewables necessitate substantial

Figure 5. Electricity generation and consumption per scenario and year.

dierences in technology and curtailment, with

the electricity mix remaining relatively stable.

Approximately 44% of electricity comes from

hydropower, 50% from onshore wind, 2.6% from

biomass, and the remainder from PV. However, at

least 11.8% of generated electricity is curtailed in

2021.

In the electricity-only scenario, there are no

signicant changes in subsequent years. By 2040,

new large-scale renewables are not expected with

the decommissioning of existing renewable energy

sources, so biomass must provide around 5 TWh

of electricity. In 2050, with a larger expansion and

diversication of renewables, biomass returns to

operating as a peak power plant.

expansion, with PV, onshore wind, and oshore

wind capacities increasing by 2040 and 2050. This

scenario requires in total 22.4 GW of renewables

by 2040, exceeding the 18 GW new RE target in

the ocial roadmap. The new installations contrast

sharply with the existing capacity and expected

evolution.

108

The hydrogen roadmap scenario is quite similar

to the electricity-only scenario in 2025 regarding

electricity generation and consumption, with some

curtailment replaced by hydrogen production. By

2030, curtailment is fully replaced by hydrogen

production, and biomass plants operate supply

electricity for electrolyzer. In 2040 and 2050, the

expansion of renewables, supported by exibility

measures, allows direct operation of electrolyzers.

However, 23% of electricity is curtailed in 2040

and 29% in 2050.

For the market-driven hydrogen production

scenarios, results vary widely based on the given

hydrogen prices. In the initial years (2021 and

2025), installed capacities remain similar to the

only electricity scenario, except for the 3 USD/

kg H2 scenario, which sees additional onshore

wind by 2025. By 2030, higher price scenarios

(2.5 and 3 USD/kg H2) show signicant PV and

onshore wind capacity expansions. From 2040

onwards, scenarios diverge more. The 1 USD/

kg H2 remains similar to the electricity scenario,

while the 2 USD/kg H2 fully exploits onshore wind

potential and adds 75 GW of PV by 2050. Higher

price scenarios (2.5 and 3 USD/kg H2) achieve

maximum potential for PV and wind oshore by

2050.

As a perspective, the Table 4 shows the

produced hydrogen per year and scenario. The

orders of magnitude among scenarios are not

comparable; they show the magnitude of the

possible market that Uruguay could have under

favorable conditions. In lower price scenarios (1

and 1.5 USD/kg H2), in the rst years, hydrogen

production is driven by the full utilization of existing

power plants, specically the biomass plant.

In 2040, the increase in the electricity demand

Figure 6. Electrolyzer capacity through the years and scenarios

and the decommissioning of older PV and wind

plants will lead to a reduction of available surplus

electricity and, therefore, a reduction in hydrogen

production in the 1 USD scenario and a slight

reduction in the 1.5 USD scenario. In 2050, due

to price reductions, it is worthwhile to further

expand renewable energies, and hydrogen

production will increase again. Electric generation

and hydrogen production grow signicantly for the

higher price scenarios (2, 2.5, and 3 USD/kg H2).

109

Table 4. Hydrogen production quantities in the dierent scenarios and years.

Some curtailment remains, but most electricity

produced is used for hydrogen production. This

shift means that electricity demand becomes a

secondary service, with the primary goal being

hydrogen production. According to our model,

this would be protable for the country, but the

actual implications for infrastructure, including

Regarding the hydrogen production system,

Figure 6 shows the required electrolyzer capacity

expansion across dierent scenarios. The

roadmap scenario diers to the values given in the

ocial hydrogen roadmap, for 2025 approximately

70 MW of electrolyzer are required, in comparison

to the 100 MW reported, in 2040 0.43 GW vs 0.6

GW, in 2040 8.69 GW vs 9 GW. These dierences

can be explained by the dierence in the utilization

of the electrolyzers; here, they are operated for

more hours, so for the same hydrogen demand,

you require less electrolyzer capacity. In the

Hydrogen roadmap, most projects are assumed

as o-grid systems, whether they are fully Wind,

PV or PV+Wind operated, and therefore with

lower utilization hours.

In market price scenarios, varying hydrogen prices

lead to dierent scales of electrolyzer capacity

expansion. The 1 USD/kg H2 scenario maintains

modest growth with around 290.6 MW of alkaline

electricity and hydrogen transport, as well as river

transport, maritime, and port infrastructure, are

not considered.

electrolyzers until 2040. As prices increase,

signicant expansions occur. The 2 USD/kg H2

scenario reaches about 27.2 GW by 2040 and

118.5 GW by 2050. The 2.5 USD/kg H2 scenario

sees even more growth, with capacities reaching

around 113 GW by 2040 and 482.4 GW by 2050.

The highest price scenario of 3 USD/kg H2 shows

exponential growth, achieving around 320.9 GW

by 2040 and 503.8 GW by 2050, illustrating

potential massive scale-up under favorable

economic conditions.

Technological changes occur over time, with

alkaline electrolyzers initially dominant. By

2040, PEM electrolyzers become competitive

due to increased eciency, and by 2050, new

installations are about 80% PEM and 20% alkaline

for scenarios with capacities above 2 GW.

Regarding the installed battery capacity and

power, the Figure 7 shows the results. Battery

expansion becomes necessary by 2030 in all

110

scenarios, with hydro dams providing exibility

until then. A synergy between the electrical system

and hydrogen production reduces power capacity

needs in scenarios with signicant hydrogen

production. Storage capacity is notably reduced in

Figure 7. Battery capacity and power according to the scenario and year

Another technology that delivers exibility to

the system are hydrogen tanks for H2 storage,

with signicant expansions in the H2 roadmap

scenario. By 2050, hydrogen tanks are about 50%

of installed electrolyzer capacities but below 7.5%

of yearly hydrogen demand. The H2 roadmap

scenario requires hydrogen tanks due to constant

Figure 8 presents the LCOE for each year

and scenario; it shows a shift from operation

and maintenance cost-based to investment

cost-based systems due to renewable energy

expansion. LCOE reacts to investment decisions,

which can either increase or decrease it,

depending on the utilization of new capacity, as

seen in 2025 in the dierent scenarios. In the “Only

electricity” and “H2 Roadmap” scenarios, LCOE

increases because the new biomass plant isn’t

used, while in other scenarios, it reduces overall

costs by producing useful electricity. Having huge

yearly hydrogen demand, necessitating storage to

shift hydrogen delivery to low production hours.

In market price scenarios, hydrogen is sold

directly once produced, eliminating the need for

production shifting.

the H2 roadmap and extreme hydrogen scenarios

(2, 2.5, and 3 USD/kg H2), especially by 2050.

3.1. LCOE

exible hydrogen production (scenarios 2, 2.5,

and 3 USD/kg H2) decreases the need for battery

exibility. For example, in the “Only electricity”

scenario, batteries account for about 6 USD/

MWh in LCOE in 2040 and 2050. The LCOE for

the “Only electricity” scenario tends to be higher

due to the fact there is no other sector or product

to share them with, and all capacity expansion

costs are solely to electricity. This means that

integrating and expanding the system based

on hydrogen market prices benets the country

and electricity consumers, promoting renewable

111

Figure 8. Levelized cost of electricity, by cost categories. Inv: investment. PP: power plants. O&M:

Operation and maintenance, including fuel

3.2. LCOH

energy expansion and lowering LCOE. The only

exception is in 2030, where signicant capacity

expansions in the 2.5 and 3 USD/kg H2 scenarios

cause higher LCOEs. Despite dierent power

Figure 9 shows the LCOH divided into dierent

cost categories for various scenarios and

years. The most signicant costs in the LCOH

come from the electricity used for hydrogen

production, making LCOH closely related to

LCOE and reecting its changes over time.

LCOH also depends on the capacity expansion

of the hydrogen system, including electrolyzer and

hydrogen tank capacities. Due to the modeling

assumptions hydrogen storage is only required in

the H2 roadmap scenario, adding costs in 2030,

2040, and 2050 in line with storage costs reported

in the roadmap (Ministerio de Industria, Energía y

Minería, 2023b).

The “H2 Roadmap” scenario indicates that a scale

mismatch in the early years (2025 and 2030) can

cause higher LCOH due to underutilized electricity

systems. In 2040, a signicant demand increase

leads to a peak in costs driven by storage and

electrolyzer investments. In the 1 and 1.5 USD

scenarios, LCOHs are above market prices, but

the model optimizes total costs by expanding

electrolyzers to use otherwise curtailed electricity.

plant expansions, LCOE remains relatively stable,

indicating cost-optimal decisions.

This results in higher costs in 2040 due to reduced

surplus electricity and limited infrastructure use.

For higher price scenarios (2, 2.5, and 3 USD),

LCOHs are below market prices, leading to

large expansions and high production quantities.

Overall, LCOHs tend to decrease over time, with

increases during expansion years. Despite dierent

development scenarios for Uruguay’s electricity

and hydrogen systems until 2050, LCOH remains

relatively homogeneous, following similar trends.

112

Figure 9. Levelized cost of hydrogen, by cost categories. Inv: investment. O&M: Operation and

maintenance, including fuel costs.

4. RESULTS AND DISCUSSION

This work presents a methodology for assigning

and distributing costs for a system with co-

production of two or more commodities; this

methodology can be applied to any energy system

that analyzes sector coupling. We also present

a comprehensive methodology for deriving all

input data, such as demands, year-specic

and country-specic CAPEX and OPEX, and

economic factors, such as WACC and discount

rates. The study presents dierent scenarios for

optimizing Uruguay’s electricity and hydrogen

systems. Each scenario demonstrates distinct

pathways for the evolution of the energy system,

highlighting the potential impacts of integrating

hydrogen production on installed capacities,

electricity generation, and consumption patterns.

The research highlights the strategic role of

hydropower and the necessity of battery storage

in maintaining grid stability and enhancing system

eciency, particularly to support renewable energy

expansions in photovoltaic and wind power.

The research also emphasizes the potential

economic benets of the hydrogen roadmap,

including reduced electricity costs for domestic

consumers and the promotion of renewable

energy sources with costs. The ndings suggest

that, under favorable market conditions, hydrogen

production could signicantly contribute to

Uruguay’s economy, positioning the country as a

major hydrogen exporter.

Recommendations for policymakers and

stakeholders include investing in renewable energy

capacities and hydrogen production, storage, and

transportation infrastructure; developing robust

market conditions and incentives for integrated

renewable energy investments and electrolyzer

capacities; exploring the implications for river,

maritime, and port infrastructure to handle

hydrogen transport and export; and investigating

advancements in electrolyzer technologies and

exibility measures to enhance system eciency

and reduce costs.

In conclusion, this study provides valuable insights

into optimizing Uruguay’s electricity and hydrogen

systems, demonstrating the transformative

potential of integrating hydrogen production into

the national energy mix. The research oers a

roadmap for policymakers and stakeholders to

navigate the energy transition, emphasizing the

importance of strategic planning, infrastructure

investment, and supportive policies to realize the

full potential of hydrogen as a key component of

Uruguay’s sustainable energy future.

113

5. ACKNOWLEDGMENTS

7. REFERENCES

The authors gratefully acknowledge the support of the German Federal Ministry of Education and

Research for funding this research under the URGE-H2 project. The author appreciates the support

from CSIC-UdelaR, PEDECIBA, and ANII. Author is a researcher at PEDECIBA/United Nations.

ADME. (2024). ADME. https://adme.com.uy/index.php

AUTHOR. (2024). Renewable Energy Potential Analysis in Bavaria. Chair of Renewable and Sustainable Energy

Systems.

Bouzas, B., Teliz, E., & AUTHOR. (2024). Green hydrogen production in Uruguay: A techno-economic approach.

International Journal of Chemical Reactor Engineering, 22(7), 783–795. https://doi.org/10.1515/ijcre-2024-0066

BP. (2022). Statistical Review of World Energy 2022.

Chi, J., & Yu, H. (2018). Water electrolysis based on renewable energy for hydrogen production. Chinese Journal

of Catalysis, 39(3), 390–394. https://doi.org/10.1016/S1872-2067(17)62949-8

Corengia, M., Estefan, N., & Torres, A. I. (2020). Analyzing Hydrogen Production Capacities to Seize Renewable

Energy Surplus. In Computer Aided Chemical Engineering (Vol. 48, pp. 1549–1554). Elsevier. https://doi.

org/10.1016/B978-0-12-823377-1.50259-7

Corengia, M., & Torres, A. I. (2022). Coupling time varying power sources to production of green-hydrogen: A

superstructure based approach for technology selection and optimal design. Chemical Engineering Research and

Design, 183, 235–249. https://doi.org/10.1016/j.cherd.2022.05.007

Damodaran, A. (2024). Useful Data Sets. https://pages.stern.nyu.edu/~adamodar/New_Home_Page/

dataarchived.html

Dorfner, J. (2016). Open Source Modelling and Optimisation of Energy Infrastructure at Urban Scale [PhD Thesis,

Technische Universität München]. https://mediatum.ub.tum.de/1285570

Dorfner, J. (2023, July 18). Urbs: A linear optimisation model for distributed energy systems—Urbs 1.0.0

documentation. https://urbs.readthedocs.io/en/latest/

Dorfner, J., Schönleber, K., Magdalena Dorfner, sonercandas, & froehlie, smuellr, dogauzrek, WYAUDI, Leonhard-B,

lodersky, yunusozsahin, adeeljsid, Thomas Zipperle, Simon Herzog, kais-siala, & Okan Akca. (2019). Urbs (Version

v1.0.1) [Python]. tum-ens/urbs. https://doi.org/10.5281/zenodo.3265960

EPRI. (2023). Electrolysis Techno-Economic Analysis (Version v2.2.0) [Computer software]. https://lcri-tools.epri.

com/tea-electrolysis

García-Gusano, D., Espegren, K., Lind, A., & Kirkengen, M. (2016). The role of the discount rates in energy

systems optimisation models. Renewable and Sustainable Energy Reviews, 59, 56–72. https://doi.org/10.1016/j.

rser.2015.12.359

114

Global Modeling and Assimilation Oce (GMAO). (2015a). MERRA-2 tavg1_2d_rad_Nx: 2d, 1-Hourly, Time-

Averaged, Single-Level, Assimilation, Radiation Diagnostics V5.12.4. Goddard Earth Sciences Data and Information

Services Center (GES DISC). 10.5067/VJAFPLI1CSIV

Global Modeling and Assimilation Oce (GMAO). (2015b). MERRA-2 tavg1_2d_slv_Nx: 2d, 1-Hourly, Time-

Averaged, Single-Level, Assimilation, Single-Level Diagnostics V5.12.4. Goddard Earth Sciences Data and

Information Services Center (GES DISC). https://doi.org/10.5067/VJAFPLI1CSIV

Global Wind Atlas 3.0. (2022). Global Wind Atlas 3.0, a Free, Web-Based Application Developed, Owned

and Operated by the Technical University of Denmark (DTU). https://windenergy.dtu.dk/english/news/

nyhed?id=3f952ab8-0be0-4ab5-8962-0b787f84503a

Ibagon, N., Muñoz, P., AUTHOR, Teliz, E., & Correa, G. (2023). Techno-economic analysis for o-grid green hydrogen

production in Uruguay. Journal of Energy Storage, 67, 107604. https://doi.org/10.1016/j.est.2023.107604

Inter-American Development Bank, & Paredes, J. R. (2017). La Red del Futuro: Desarrollo de una red eléctrica

limpia y sostenible para América Latina. Inter-American Development Bank. https://doi.org/10.18235/0000937

International Energy Agency. (2019). The Future of Hydrogen. IEA. https://iea.blob.core.windows.net/

assets/9e3a3493-b9a6-4b7d-b499-7ca48e357561/The_Future_of_Hydrogen.pdf

International Energy Agency. (2022). World Energy Outlook 2022. https://www.iea.org/reports/world-energy-

outlook-2022

IRENA. (2020). Green hydrogen cost reduction: Scaling up electrolysers to meet the 1.5C climate goal. International

Renewable Energy Agency. https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2020/Dec/IRENA_

Green_hydrogen_cost_2020.pdf

IRENA. (2022). Geopolitics of the Energy Transformation: The Hydrogen Factor. International Renewable Energy

Agency. https://www.irena.org/publications/2022/Jan/Geopolitics-of-the-Energy-Transformation-Hydrogen

Kais Siala, Houssame Houmy, Buchenberg, P., Thushara2020, Lodersky, & Sonercandas. (2022). tum-ens/

pyGRETA: Python Generator of REnewable Time series and mAps (Version v2.1) (Version v2.1) [Computer

software]. tum-ens. https://doi.org/10.5281/ZENODO.3727416

Kumar, S., & Himabindu, V. (2019). Hydrogen production by PEM water electrolysis – A review. Materials Science

for Energy Technologies, 2(3), 442–454. https://doi.org/10.1016/j.mset.2019.03.002

Kumar, S., & Lim, H. (2022). An overview of water electrolysis technologies for green hydrogen production. Energy

Reports, 8, 13793–13813. https://doi.org/10.1016/j.egyr.2022.10.127

Laguna-Bercero, M. A. (2012). Recent advances in high temperature electrolysis using solid oxide fuel cells: A

review. Journal of Power Sources, 203, 4–16. https://doi.org/10.1016/j.jpowsour.2011.12.019

Mackenzie, W. (2023, April 3). Latin America (LATAM) & Canada solar PV system pricing 2023. https://www.woodmac.

com/reports/power-markets-latin-america-latam-and-canada-solar-pv-system-pricing-2023-150114048/

Mackenzie, W. (2024, March 1). Latin America solar PV system pricing 2024. https://www.woodmac.com/reports/

power-markets-latin-america-solar-pv-system-pricing-2024-150211127/

Ministerio de Industria, Energía y Minería. (2018). Estudio de Prospectiva de la Demanda Energética 2018. https://

www.gub.uy/ministerio-industria-energia-mineria/comunicacion/publicaciones/estudio-prospectiva-demanda-

energetica-2018

115

Ministerio de Industria, Energía y Minería. (2023a). BEN Balance Energético Nacional. https://ben.miem.gub.uy/

balance.php

Ministerio de Industria, Energía y Minería. (2023b). Uruguay’s Roadmap for Green Hydrogen and Derivatives.

https://www.gub.uy/ministerio-industria-energia-mineria/comunicacion/noticias/hoja-ruta-hidrogeno-verde-

uruguay-version-nal

Moore, M. A., Boardman, A. E., & Vining, A. R. (2020). Social Discount Rates for Seventeen Latin

American Countries: Theory and Parameter Estimation. Public Finance Review, 48(1), 43–71. https://doi.

org/10.1177/1091142119890369

NREL (National Renewable Energy Laboratory). (2023). 2023 Annual Technology Baseline. National Renewable

Energy Laboratory. https://atb.nrel.gov/

OECD. (2021). Assessment of a social discount rate and nancial hurdle rates for energy system modelling in

Viet Nam (OECD Environment Working Papers 181; OECD Environment Working Papers, Vol. 181). https://doi.

org/10.1787/a4f9a3-en

Oeko-Institut. (2023). PTX Business Opportunity Analyser (BOA): Data Documentation. Documentation of data

sources and data processing, version 1.0. Commissioned by Agora Energiewende and Agora Industry.

Ryberg, D. S., Robinius, M., & Stolten, D. (2018). Evaluating Land Eligibility Constraints of Renewable Energy

Sources in Europe. Energies, 11. https://doi.org/10.3390/en11051246

Sánchez Delgado, M. (2019). Desarrollo y validación de un modelo para la simulación de sistemas de electrólisis

alcalina para la producción de hidrógeno a partir de energías renovables [Phd, E.T.S.I de Minas y Energía]. https://

oa.upm.es/62567/

Steinbach, J., & Staniaszek, D. (2015). Discount rates in energy system analysis—Discussion Paper.

Stolten, D., & Emonts, B. (2016). Hydrogen science and engineering, 2 volume set: Materials, processes, systems,

and technology (Vol. 1). John Wiley & Sons.

United Nations. (2015). The Paris Agreement | UNFCCC. https://unfccc.int/process-and-meetings/the-paris-

agreement

Wang, Y., Kowal, J., Leuthold, M., & Sauer, D. U. (2012). Storage System of Renewable Energy Generated

Hydrogen for Chemical Industry. Energy Procedia, 29, 657–667. https://doi.org/10.1016/j.egypro.2012.09.076