essay on hydroelectric power system

ENCYCLOPEDIC ENTRY

Hydroelectric energy.

Hydroelectric energy is a form of renewable energy that uses the power of moving water to generate electricity.

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Damed river in a valley marked with agricultural fields along the flood plains surrounded by rolling hills.

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Hydroelectric energy , also called hydroelectric power or hydroelectricity , is a form of energy that harnesses the power of water in motion—such as water flowing over a waterfall—to generate electricity. People have used this force for millennia. Over 2,000 years ago, people in Greece used flowing water to turn the wheel of their mill to ground wheat into flour.

How Does Hydroelectric Energy Work?

Most hydroelectric power plants have a reservoir of water, a gate or valve to control how much water flows out of the reservoir , and an outlet or place where the water ends up after flowing downward. Water gains potential energy just before it spills over the top of a dam or flows down a hill. The potential energy is converted into kinetic energy as water flows downhill. The water can be used to turn the blades of a turbine to generate electricity, which is distributed to the power plant’s customers.

Types of Hydroelectric Energy Plants

There are three different types of hydroelectric energy plants, the most common being an impoundment facility. In an impoundment facility, a dam is used to control the flow of water stored in a pool or reservoir . When more energy is needed, water is released from the dam. Once water is released, gravity takes over and the water flows downward through a turbine . As the blades of the turbine spin, they power a generator.

Another type of hydroelectric energy plant is a diversion facility. This type of plant is unique because it does not use a dam. Instead, it uses a series of canals to channel flowing river water toward the generator-powering turbines .

The third type of plant is called a pumped-storage facility. This plant collects the energy produced from solar, wind, and nuclear power and stores it for future use. The plant stores energy by pumping water uphill from a pool at a lower elevation to a reservoir located at a higher elevation. When there is high demand for electricity, water located in the higher pool is released. As this water flows back down to the lower reservoir, it turns a turbine to generate more electricity.

How Widely Is Hydroelectric Energy Used Around the World?

Hydroelectric energy is the most commonly-used renewable source of electricity. China is the largest producer of hydroelectricity. Other top producers of hydropower around the world include the United States, Brazil, Canada, India, and Russia. Approximately 71 percent of all of the renewable electricity generated on Earth is from hydropower.

What Is the Largest Hydroelectric Power Plant in the World?

The Three Gorges Dam in China, which holds back the Yangtze River, is the largest hydroelectric dam in the world, in terms of electricity production. The dam is 2,335 meters (7,660 feet) long and 185 meters (607 feet) tall, and has enough generators to produce 22,500 megawatts of power.

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Hydroelectric Power: Advantages of Production and Usage Completed

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Nothing is perfect on Earth, and that includes the production of electricity using flowing water. Hydroelectric-production facilities are indeed not perfect (a dam costs a lot to build and also can have negative effects on the environment and local ecology), but there are a number of advantages of hydroelectric-power production as opposed to fossil-fuel power production.

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Representatives of more than 170 countries reached consensus at the Top World Conference on Sustainable Development, in Johannesburg (2002), and at the 3rd World Forum on Water, in Kyoto (2003): hydroelectric generation is renewable and has certain merits Here are ten reasons leading them to this conclusion.

1. Hydroelectricity is a renewable energy source.

Hydroelectricity uses the energy of running water , without reducing its quantity, to produce electricity. Therefore, all hydroelectric developments, of small or large size, whether run of the river or of accumulated storage, fit the concept of renewable energy.

2. Hydroelectricity makes it feasible to utilize other renewable sources.

Hydroelectric power plants with accumulation reservoirs offer incomparable operational flexibility, since they can immediately respond to fluctuations in the demand for electricity. The flexibility and storage capacity of hydroelectric power plants make them more efficient and economical in supporting the use of intermittent sources of renewable energy, such as solar energy or Aeolian energy.

3. Hydroelectricity promotes guaranteed energy and price stability.

River water is a domestic resource which, contrary to fuel or natural gas, is not subject to market fluctuations. In addition to this, it is the only large renewable source of electricity and its cost-benefit ratio, efficiency, flexibility and reliability assist in optimizing the use of thermal power plants .

4. Hydroelectricity contributes to the storage of drinking water.

Hydroelectric power plant reservoirs collect rainwater, which can then be used for consumption or for irrigation. In storing water, they protect the water tables against depletion and reduce our vulnerability to floods and droughts.

5. Hydroelectricity increases the stability and reliability of electricity systems.

The operation of electricity systems depends on rapid and flexible generation sources to meet peak demands, maintain the system voltage levels, and quickly re-establish supply after a blackout. Energy generated by hydroelectric installations can be injected into the electricity system faster than that of any other energy source. The capacity of hydroelectric systems to reach maximum production from zero in a rapid and foreseeable manner makes them exceptionally appropriate for addressing alterations in the consumption and providing ancillary services to the electricity system, thus maintaining the balance between the electricity supply and demand.

6. Hydroelectricity helps fight climate changes.

The hydroelectric life cycle produces very small amounts of greenhouse gases (GHG). In emitting less GHG than power plants driven by gas, coal or oil, hydroelectricity can help retard global warming. Although only 33% of the available hydroelectric potential has been developed, today hydroelectricity prevents the emission of GHG corresponding to the burning of 4.4 million barrels of petroleum per day worldwide.

7. Hydroelectricity improves the air we breathe.

Hydroelectric power plants don't release pollutants into the air. They very frequently substitute the generation from fossil fuels, thus reducing acid rain and smog. In addition to this, hydroelectric developments don't generate toxic by-products.

8. Hydroelectricity offers a significant contribution to development.

Hydroelectric installations bring electricity, highways, industry and commerce to communities, thus developing the economy, expanding access to health and education, and improving the quality of life. Hydroelectricity is a technology that has been known and proven for more than a century. Its impacts are well understood and manageable through measures for mitigating and compensating the damages. It offers a vast potential and is available where development is most necessary.

9. Hydroelectricity means clean and cheap energy for today and for tomorrow.

With an average lifetime of 50 to 100 years, hydroelectric developments are long-term investments that can benefit various generations. They can be easily upgraded to incorporate more recent technologies and have very low operating and maintenance costs.

10. Hydroelectricity is a fundamental instrument for sustainable development.

Hydroelectric enterprises that are developed and operated in a manner that is economically viable, environmentally sensible and socially responsible represent the best concept of sustainable development. That means, "development that today addresses people's needs without compromising the capacity of future generations for addressing their own needs" (World Commission on the Environment and Development, 1987).

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HOW DO WE GET ENERGY FROM WATER?

Hydropower , or hydroelectric power, is a renewable source of energy that generates power by using a dam or diversion structure to alter the natural flow of a river or other body of water. Hydropower relies on the endless, constantly recharging system of the water cycle to produce electricity, using a fuel—water—that is not reduced or eliminated in the process. There are many types of hydropower facilities , though they are all powered by the kinetic energy of flowing water as it moves downstream. Hydropower utilizes turbines and generators to convert that kinetic energy into electricity, which is then fed into the electrical grid to power homes, businesses, and industries.

HOW EXACTLY IS ELECTRICITY GENERATED AT HYDROPOWER PLANTS?

Because hydropower uses water to generate electricity, plants are usually located on or near a water source. The energy available from the moving water depends on both the volume of the water flow and the change in elevation—also known as the head—from one point to another. The greater the flow and the higher the head, the more the electricity that can be generated.

At the plant level, water flows through a pipe—also known as a penstock—and then spins the blades in a turbine, which, in turn, spins a generator that ultimately produces electricity. Most conventional hydroelectric facilities operate this way, including run-of-the-river systems and pumped storage systems .

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Home — Essay Samples — Science — Technology & Engineering — Hydroelectric Power

Essays on Hydroelectric Power

Hydroelectric power is a renewable energy source that is generated by harnessing the power of flowing water. It is a clean and reliable source of energy that has been used for centuries to power various machines and devices. In recent years, there has been a growing interest in hydroelectric power as a viable alternative to traditional fossil fuels. This essay will discuss the benefits of hydroelectric power and explore various topics related to this important energy source.

One of the most compelling reasons to consider hydroelectric power as an essay topic is its environmental impact. Unlike fossil fuels, which release harmful pollutants into the atmosphere, hydroelectric power is a clean and sustainable source of energy. By using the natural power of flowing water, hydroelectric power plants can generate electricity without producing harmful emissions. This makes hydroelectric power an attractive option for reducing greenhouse gas emissions and combating climate change.

Another important aspect of hydroelectric power is its reliability and consistency. Unlike solar or wind power, which are dependent on weather conditions, hydroelectric power can be generated consistently throughout the year. This makes it a reliable source of energy that can help meet the growing demand for electricity. In addition, hydroelectric power plants can be designed to store water in reservoirs, allowing for greater control over energy production and distribution.

The construction of hydroelectric power plants can also have a significant impact on local ecosystems and communities. Large-scale hydroelectric projects can alter the natural flow of rivers and disrupt the habitats of various plant and animal species. This can lead to environmental degradation and loss of biodiversity. On the other hand, small-scale hydroelectric projects can be designed to minimize their impact on the environment and provide sustainable energy solutions for local communities.

Hydroelectric power also has the potential to provide economic benefits to countries and regions that invest in this energy source. The construction and operation of hydroelectric power plants can create jobs and stimulate economic growth in areas that may be struggling with high unemployment rates. In addition, the generation of hydroelectric power can reduce dependence on imported fossil fuels, saving money and improving energy security.

There are also important debates surrounding the social and cultural impacts of hydroelectric power. The construction of large-scale hydroelectric projects can lead to the displacement of local communities and the loss of cultural heritage sites. This has led to conflicts and protests in many parts of the world, as communities seek to protect their land and way of life. On the other hand, hydroelectric power can also bring new opportunities for economic development and improved living standards for local communities.

One important topic to consider when writing an essay on hydroelectric power is the potential for technological advancements and innovations in this field. New technologies and engineering designs are constantly being developed to improve the efficiency and environmental performance of hydroelectric power plants. These innovations can help to make hydroelectric power an even more attractive and viable option for meeting the world's energy needs.

Another important aspect to consider is the role of government policies and regulations in promoting the development of hydroelectric power. Many countries have adopted incentives and subsidies to encourage investment in renewable energy sources, including hydroelectric power. These policies can have a significant impact on the growth of the hydroelectric power industry and the adoption of this important energy source.

The choice of hydroelectric power as an essay topic offers a wide range of interesting and important issues to explore. From its environmental and economic benefits to its social and cultural impacts, hydroelectric power is a complex and multifaceted topic that can provide valuable insights into the challenges and opportunities of transitioning to a more sustainable energy future. By examining these topics in depth, students can gain a deeper understanding of the complexities of energy policy and the potential of hydroelectric power to contribute to a more sustainable and secure future.

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Challenges in the Management of Hydroelectric Generation in Power System Operations

• Regional Renewable Energy (M Negrete-Pincetic, Section Editor)
• Published: 09 June 2020
• Volume 7 , pages 94–99, ( 2020 )

Cite this article

• Álvaro Lorca 1 , 2 , 3 ,
• Marcel Favereau 1 , 2 &
• Daniel Olivares 1 , 3  

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4 Citations

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Purpose of Review

The management of hydroelectric generation in the context of power system operations has been a difficult and important problem since the inception of power systems more than a century ago; however, various current developments are leading to important new associated challenges and opportunities: massive integration of variable renewable energy and other disruptive technologies, climate change effects on the availability of hydro inflows, and also new efficient techniques for optimization under uncertainty.

Recent Findings

Multistage stochastic optimization and stochastic dual dynamic programming are currently the dominant techniques for hydroelectric generation scheduling problems; however, there are many recent extensions and improvements on such techniques, and alternative approaches are being developed with significant potential for future concrete applications from power system operators and policy makers.

In this context, this paper presents a literature review on hydroelectric generation scheduling models, and a discussion on the critical challenges, open research questions, and future lines of research associated to this problem.

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This research was partially supported by CONICYT/FONDECYT/11170423 and CONICYT-PFCHA/National Doctorate Program/2019-21190693.

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Álvaro Lorca, Marcel Favereau & Daniel Olivares

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Lorca, Á., Favereau, M. & Olivares, D. Challenges in the Management of Hydroelectric Generation in Power System Operations. Curr Sustainable Renewable Energy Rep 7 , 94–99 (2020). https://doi.org/10.1007/s40518-020-00152-6

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• Published: 18 September 2023

Balancing-oriented hydropower operation makes the clean energy transition more affordable and simultaneously boosts water security

• Zhanwei Liu 1 &
• Xiaogang He   ORCID: orcid.org/0000-0001-7428-0269 1  

Nature Water volume  1 ,  pages 778–789 ( 2023 ) Cite this article

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• Climate-change mitigation
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Reservoir hydropower offers a compelling combination of stability and flexibility services for modern water and power grids. However, its operating flexibility is poorly characterized in energy system planning, missing opportunities to cost-effectively uptake variable renewable energy (VRE) for a clean energy transition. In this study, we have developed a fully coupled reservoir operation and energy expansion model to quantify the economic and environmental benefits attained from adaptive hydropower operation in a high VRE future. Our case study of the China Southern Power Grid reveals that, in a 2050 net-zero grid, simply adapting hydropower operations to balance VRE can reduce 2018–2050 total system costs by 7% (that is, US$28.2 billion) and simultaneously save 123.8 km 3 of water each year (that is, more than three times the reservoir capacity of the Three Gorges Dam). These vast, yet overlooked, cost- and water-saving potentials highlight the importance of incorporating balancing-oriented hydropower operation into future pathways to jointly decarbonize and secure power and water grids.

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The decarbonization of power systems is crucial to mitigate climate change 1 and requires a high penetration of variable renewable energy (VRE; mainly solar photovoltaic (PV) and wind power) to displace carbon-emitting electricity (for example, coal- and gas-fired power). Yet, the generation of weather-dependent VRE is unlikely to align with the timing of grid demand due to the intermittent, fluctuating and stochastic nature of weather systems 2 . In a high VRE power system, it is therefore crucial to deploy diverse and flexible technologies 3 to increase grid flexibility so that a reliable and resilient power supply can be ensured. Among the many alternative flexible options, reservoir hydropower is by far the most mature and the largest grid-connected clean technology 4 . It not only supplies carbon-free and cost-competitive energy, but also delivers vital flexibility services that can facilitate the low-carbon transition, characterized by its short start-up and shut-down time, quick ramping speed, low operation costs and long-duration (for example, interseasonal or interannual) energy buffering capabilities 5 . Globally, hydropower alone supplies 4,252 TWh of electricity, accounting for 17% of the total electricity consumed in 2021. Hydropower also contributes nearly one-third (29%) of global flexibility services measured by hourly ramping needs 6 .

Reservoir hydropower plays a versatile role in safeguarding both power and water grids (that is, complex cascade reservoirs connected by river networks) owing to its generational flexibility and storage services 7 . Yet, conventional hydropower operations are being used to minimize load demand fluctuations 8 . These hydropower operations were designed on the basis of historical conditions when VRE did not represent a large share of electricity generation. In a rapidly evolving power grid with a growing penetration of VRE, such outdated operations could miss the opportunity to fully tap the potential of flexible hydropower to support VRE integration 2 , 9 . They could also unintentionally increase the vulnerability of power 10 and water 11 grids, especially under climate shocks. For instance, low reservoir inflows during prolonged droughts exacerbate water allocation trade-offs between hydropower production, agricultural supply and environmental flow requirements 12 . In addition to these sectoral water-use trade-offs, increased hydroclimate variability also heightens intertemporal trade-offs between storing water to enhance long-term drought resilience and releasing water to mitigate short-term flood risks 13 . There remain great, yet underexplored, opportunities 12 to minimize these trade-offs by leveraging the large-scale flexibility of hydropower and the rapid growth of VRE. This can be achieved by shifting reservoir operations from a conventional peak-shaving-oriented operation scheme 9 , which is load demand-oriented and unable to adapt to a changing power mix, to a balancing-oriented hydropower operation that supports VRE integration and is well suited to adapt to changing temporal dynamics of electricity supply. However, it remains unclear to what extent adaptive hydropower operation can support the cost-effective integration of VRE and how VRE contributes to water sustainability.

A growing number of modelling studies 2 , 9 , 12 , 14 , 15 , 16 , 17 , 18 have investigated the spatiotemporal complementarity between VRE and hydropower in a power mix with an increasing share of VRE. However, most of these studies were conducted in silos, either focusing on the energy sector 2 , 9 , 14 , 15 , 16 or on the water sector 12 , 17 . A multi-benefit assessment of balancing-oriented hydropower operation accounting for the non-linear and dynamic nature of water–energy interactions is still lacking, especially for benefits related to water sustainability. This is hampered by a lack of modelling capabilities in existing energy expansion models to fully account for hydropower flexibility, which requires high spatial (both plant-level reservoir characteristics and regional-level reservoir cascade typologies), temporal (hourly to daily) and process (for example, generation flow variability, water head dynamics and power dispatch) resolutions (see Methods for details). Inadequate representation of hydropower flexibility can lead to over-investment in alternative flexible energy (for example, pumped storage hydropower and lithium-ion batteries) and therefore miss the opportunity to cost-effectively decarbonize power grids in an environmentally friendly manner. In this study, we developed an energy expansion model called P athways for R enewable E nergy P lanning coupling S hort-term H ydropower O pera T ion (PREP-SHOT; Supplementary Fig. 1 ) with explicit representation of hydropower flexibility to jointly optimize long-term investment and short-term operational decisions at the lowest cost (see Methods for details). We used PREP-SHOT to quantify the benefits of adaptive hydropower reoperations by comparing two operation schemes: ‘FixedHydro’ (conventional peak-shaving-oriented operations to maximize hydropower generation) and ‘AdaptiveHydro’ (balancing-oriented operations to compensate for the intermittency of VRE to minimize total system cost; see Methods for details).

We investigated the impact of balancing-oriented hydropower operation on energy expansion through a case study focusing on a renewable-dominated power grid, the China Southern Power Grid (CSG; Fig. 1a ), where hydropower and VRE shared 40% of total power supplies in 2020 and will increase to 90% of the projected total load demand in 2050 19 (Fig. 1b ). We designed 81 plausible carbon emission reduction scenarios (Fig. 1c ) to represent uncertain decarbonization policies. We found that shifting reservoir operations from FixedHydro to AdaptiveHydro reduces the total system-wide cost over the period 2018–2050 by 7.1% if the CSG is fully decarbonized in 2050. Such cost savings are largely driven by the decline in curtailment rate of VRE supported by adaptive hydropower operations. Moreover, we estimated that a 1 MWh increase in VRE can save 320.0 m 3 of water each year under normal inflow conditions if short-term hydropower operations are jointly optimized with long-term energy expansion. This overlooked water-saving potential implies that a diversification of the power mix towards more VRE to reduce hydropower dependency has co-benefits that can simultaneously enhance the resilience of water grids to withstand future climate shocks. While our analysis focuses on China, PREP-SHOT and the quantitative assessment framework can be readily applied to other regions to design optimal energy decarbonization expansion pathways and guide sustainable water management.

a , Locations and installed capacity of 46 large hydropower stations (installed capacity exceeding 300 MW) in the CSG (see Supplementary Table 1 for plant-level details and Supplementary Fig. 2 for a detailed topology). Blue lines indicate major rivers; purple areas represent the five provinces (Guangdong, Guangxi, Guizhou, Yunnan and Hainan) in the CSG. The height of the spikes represents the installed capacity of each hydropower station. b , Previous and future energy generation portfolios of the CSG. The total renewable generation (including hydropower, solar PV and wind power) accounts for around 40%, 60% and 90% of the total power supplies in 2020, 2030 and 2050, respectively. c , Carbon emissions under different reduction scenarios. Each thin line represents the upper bound of a carbon dioxide emission reduction scenario. The five bold lines (from top to bottom) represent scenarios with 20%, 40%, 60%, 80% and 100% reductions in carbon emissions in 2050 relative to 2018 levels.

Source data

Balancing-oriented hydropower operation reduces system costs.

We found that balancing-oriented hydropower operation (AdaptiveHydro) can substantially reduce the total energy system cost compared with peak-shaving-oriented hydropower operation (FixedHydro) if the short-term operation of hydropower is jointly optimized with long-term energy expansion (Fig. 2a ). Total cost reductions (=  \({\,{{\mbox{cost}}}\,}_{{{{\rm{FixedHydro}}}}}^{{{{\rm{total}}}}}-{\,{{\mbox{cost}}}\,}_{{{{\rm{AdaptiveHydro}}}}}^{{{{\rm{total}}}}}\) ) increase almost linearly with carbon emission reduction targets, despite the non-linear increase in the total system-wide cost (Fig. 2a,b ). With a 20% cut in 2050 carbon emissions from the 2018 levels, the 2018–2050 total system-wide cost of AdaptiveHydro (US$341.5 billion) is US$14.6 billion less than that of FixedHydro (US$356.1 billion), a 4.1% reduction. Such cost savings almost double to US$28.2 billion if carbon emissions are cut by 100% (Fig. 2a ).

The AdaptiveHydro scheme reduces total system-wide cost and lowers the curtailment rate of VRE compared with FixedHydro. Such cost savings are driven by AdaptiveHydro and increase with decarbonization effort. a , Total system-wide costs over the entire planning horizon (2018–2050) under FixedHydro and AdaptiveHydro and the corresponding cost savings (yellow area) across different carbon emission reduction targets. b , Decomposition of total cost savings into six categories (that is, investment cost savings of coal-fired plant, investment cost savings of solar, investment cost savings of wind, investment cost savings of pumped storage hydropower, fuel cost savings of coal-fired plants and cost savings of operation and maintenance (O&M)) and how each category varies with the level of decarbonization. Negative cost savings indicate that the cost of a particular technology in the AdaptiveHydro scheme is greater than that of FixedHydro, as is the case with solar power. c , Curtailment reduction (yellow area) of VRE under AdaptiveHydro operation (pink line) compared with FixedHydro operation (blue line) across different decarbonization scenarios. d , Median (bold lines) and uncertainties (shading) of total cost savings under normal, dry ( n  = 8) and wet ( n  = 8) inflow conditions, as well as inflow conditions with interannual variability ( n  = 100) across different carbon emission reduction targets. The light and dark shading represent 95% and 50% confidence intervals, respectively.

The attained cost savings are mainly driven by the reduced investment cost of VRE (that is, a 63% reduction in wind investment cost, although a 40% increase in solar investment cost) and the lower fuel cost of coal-fired plants (42% reduction in fuel cost), which, in total, make up ~65% of the total cost savings supported by adaptive hydropower operation (Fig. 2b ). With the enhanced operational flexibility delivered by AdaptiveHydro compared with FixedHydro, a larger investment in solar is prioritized over wind (Supplementary Fig. 3a,b,e,f ) given the lower cost of solar (Supplementary Table 2 ), despite its more fluctuating nature (sharp rise in the morning and sharp fall at dusk; Supplementary Figs. 3 and 4 ). Although the investment cost of solar is higher in AdaptiveHydro than in FixedHydro (Supplementary Fig. 3j ), the combined investment cost of VRE (both solar and wind) in AdaptiveHydro is much lower (Fig. 2b and Supplementary Fig. 3i,j ). This is mainly driven by the enhanced integration of VRE, as evidenced by the reduced curtailment rate of VRE (0.7–3.8% lower in AdaptiveHydro, depending on the level of decarbonization, Fig. 2c ). Such elevated VRE generation in the power mix displaces more coal-fired generation, thus substantially reducing the cost of fuel for coal-fired power (US$6.2–11.1 billion, Fig. 2b ).

The total cost savings from adaptive hydropower operations also depend on inflow variability, with consistently higher benefits in dry years than in wet years regardless of the decarbonization level (Fig. 2d ). For a zero-carbon grid in 2050, an additional US$0.7 billion can be saved during dry years (US$28.8 billion, median estimate) compared with a normal year (US$28.1 billion). The total savings would, however, diminish to US$24.1 billion in scenarios with wetter inflow conditions, and these are associated with higher uncertainties (US$20.0–28.4 billion, 95th percentile range). Additionally, if the interannual variability of inflow is considered, the median estimate of such cost savings would further decline by 34.0%, 32.4% and 21.2% compared with dry, normal and wet years, respectively, under a zero-carbon grid in 2050 (Fig. 2d ). According to our previous analysis (Fig. 2a,c ), because a higher curtailment reduction of VRE is associated with higher cost savings, the reduced difference in the median VRE curtailment rate between FixedHydro and AdaptiveHydro (Supplementary Fig. 5a ) explains why lower cost savings are obtained after considering the inflow interannual variability. The lower VRE curtailment reduction (Supplementary Fig. 5a ) in interannual variability scenarios is mainly due to systematic seasonal biases between observed and simulated inflows (Supplementary Fig. 5b ) over the representative periods, which leads to a biased yet heightened seasonal complementarity between hydro and VRE (Supplementary Fig. 5c,d ).

Increased VRE penetration boosts water security

We found that high VRE penetration can deliver additional non-monetary benefits that mitigate water use conflicts and enhance water availability beyond VRE’s traditional role of decarbonization (Fig. 3a ). In a high renewable energy system, increased VRE generation supported by reservoir hydropower and energy storage (for example, pumped storage hydropower, Fig. 3b ) not only reduces the power grid’s reliance on hydropower production (Fig. 3a and Supplementary Fig. 6e–h ), but also increases the operational water head of hydropower stations (Supplementary Fig. 7 ).

a , Annual hydropower production and water savings (scaled by circle size) versus VRE generation across all carbon emission reduction scenarios. The trend line (dashed line) was obtained by ordinary least-squares linear fitting between VRE generation and hydropower generation. b , Stacked area plot showing how the planned capacity of different technologies in 2050 varies with the degree of decarbonization. c , Variation of water savings with VRE generation and reservoir inflow. Each dot represents a combination of a carbon emission reduction scenario and an inflow scenario. Dots of the same colour represent one inflow scenario across all carbon emission reduction scenarios. The black dashed lines (from top to bottom) are linear fitting lines for scenarios with 40% wetter, normal and 40% drier inflow conditions. The black solid lines represent a certain carbon emission reduction scenario (that is, 20%, 60% and 100%). d , Uncertainties in the WSV across a dry inflow (brown line; n  = 8), a wet inflow (green line; n  = 8) and an inflow considering interannual variability (light-blue violin-shaped area; n  = 100) for different ranges of decarbonization. The median values of WSV estimated from dry inflow, wet inflow and inflow with interannual variability are represented by brown dots, green dots and vertical blue lines, respectively. The black squares represent the WSV under normal inflow conditions. The brown and green triangles represent the values of WSV estimated from dry inflow and wet inflow used to derive statistics.

We further quantified the water sustainability value (WSV) of VRE and examined how it varies with inflow conditions (Fig. 3c,d ). The WSV measures how much water can be saved per megawatt hour power generation from VRE facilitated by AdaptiveHydro (empirically, the WSV is the slope of the dashed lines in Fig. 3c , see Methods for details). In a deeply decarbonized world (carbon emissions are cut by 80–100% in 2050 relative to 2018 levels), we found that a 1 MWh increase in VRE leads to annual water savings of ~320.0 m 3 under normal inflow conditions if the VRE is balanced by adaptive hydropower operations. The estimated WSV in wet years is considerably higher than the WSV in dry years, but the difference narrows as carbon emission reduction targets become more ambitious (Fig. 3d ). For instance, with low decarbonization targets, the median WSV (across all inflow scenarios in the range) declines by 238.1 m 3  MWh −1 , an 82.0% decrease from 290.5 m 3  MWh −1 in wet years compared with 52.4 m 3  MWh −1 in dry years. This difference decreases to 148.5 m 3  MWh −1 , a 31.3% reduction in the WSV in wet years (473.9 m 3  MWh −1 ) compared with that in dry years (325.4 m 3  MWh −1 ), when a high carbon emission reduction target is achieved. Furthermore, the response of the WSV to inflow variability is less sensitive in dry years than in wet years. The variation in the WSV in dry years spans 58.0 m 3  MWh −1 across the high carbon emission reduction scenarios (95% range of 306.8–364.8 m 3  MWh −1 ), which almost quadruples to 217.0 m 3  MWh −1 (95% range of 386.8–603.8 m 3  MWh −1 ) in wet years, despite the substantial increase in the absolute magnitude of the WSV.

In addition to the sensitivity analysis of the WSV to inflow changes in dry and wet years, we also analysed how the interannual variability of inflow affects WSV estimates. Unlike the response of cost savings to interannual variability (Fig. 2d ), we did not find any consistent patterns in the WSV after accounting for inflow interannual variability (Fig. 3d ). The estimated WSV range considering interannual variability falls between the WSV ranges for dry and wet years, except in the case of the high decarbonization targets. These findings suggest that the effects of inflow interannual variability on WSV are more complex than its impact on cost savings.

Hydropower-driven flexibility plays a crucial role in supporting VRE uptake towards power system decarbonization. Yet, its monetary and environmental value remains poorly characterized, especially in a high VRE future with uncertain decarbonization policies. Research on hydro–VRE complementarity traditionally has focused on economic dispatch 20 . In this study, we shifted the focus to jointly considering short-term economic dispatch with long-term energy expansion. This was aided by the new features of PREP-SHOT, which explicitly implements a two-way coupling of long-term energy expansion and short-term hydropower operation and therefore allows a more accurate quantification of total system cost and water sustainability. We found high, but overlooked, cost-saving (Fig. 2a ), water-saving (Fig. 3a ) and carbon abatement potential (see discussion below) if hydropower operations can be simply shifted from peak-shaving-oriented operation (with fixed and archaic operations) to balancing-oriented operation (with flexible and adaptive operations). Our results also show the value of AdaptiveHydro to be ‘path-dependent’ (Figs. 2a and 3d ): earlier and deeper decarbonization allows the water sustainability benefits of VRE to be reaped to a larger extent.

Cost and water savings driven by increased penetration of hydro-compensated VRE can be explained by two intertwined mechanisms: volume effects and timing effects. Volume effects refer to more VRE being supplied in the power mix to match demand, directly substituting carbon-emitting electricity. Timing effects refer to more hydropower being generated at the ‘right’ time (for example, night-time) when VRE generation is low, which can lower the curtailment rate of VRE (Fig. 2c ) and thereby indirectly substitute carbon-emitting electricity. Timing effects become more pronounced under AdaptiveHydro operation than under FixedHydro operation (Fig. 4 and Supplementary Fig. 6 ). This is evidenced by the sub-daily operation process in which AdaptiveHydro tends to shift hydropower generation from daytime to night-time periods when VRE is less available (Fig. 4 and Supplementary Fig. 6 ), whereas under FixedHydro, hydropower generation (Fig. 4 and black lines in Supplementary Fig. 6 ) is still largely prioritized during the daytime (to shave peak load). As decarbonization becomes more ambitious, volume effects become stronger (because of a growing penetration of VRE; Fig. 3a,b ) and are further reinforced by timing effects (because of enhanced flexibility; Supplementary Fig. 6 ). Together, they drive a much larger reduction in VRE curtailment (Fig. 2c ) as well as total system costs (Fig. 2a ). In addition, we found that as VRE integration increases (that is, stronger volume effects), hydropower plants tend to operate at higher water heads in AdaptiveHydro operation (Supplementary Fig. 7 ). This not only enhances hydropower flexibility, but also reduces the volume of water required to generate an equivalent amount of electricity. As a result, greater water savings can be achieved under AdaptiveHydro.

a , b , Hour-by-hour energy generation portfolios and load demand for the CSG during the spring of 2045 for a zero-carbon emission scenario under FixedHydro ( a ) and AdaptiveHydro ( b ) operation schemes. Negative values denote the charging of storage. PSH, pumped storage hydropower.

Intriguingly, we also found an opposite response of water and cost savings to inflow variabilities. The gains in VRE-driven water savings are substantially smaller in dry years than in wet years (Fig. 3d ). This is in line with our expectations because reservoir inflow decreases in dry years, leading to declined hydropower production (Supplementary Fig. 8a ). As a certain proportion of water still needs to be kept in reservoirs and pass through turbines to generate hydropower and provide flexibility services, less water can be saved for non-hydro purposes. In contrast to water savings, cost savings delivered by flexible hydropower are higher in dry years than in wet years (Fig. 2d ). This counterintuitive finding can be explained by the fact that although inflows in dry years reduce hydropower generation (Supplementary Fig. 8a ), AdaptiveHydro operation does not require much additional investment in pumped storage hydropower (Supplementary Fig. 9l ). This is because of the undiminished timing effects in AdaptiveHydro, in which the joint optimization of flexible hydropower can still maintain relatively high flexibility and continually support volume effects under high VRE penetration (Supplementary Figs. 10 and 11 ). However, if hydropower is not adaptively operated (FixedHydro), to aid increased VRE integration under drier conditions, investment in storage-driven flexibility (provided by pumped storage hydropower) will need to be linearly scaled up (Supplementary Fig. 9l ) to make up for the loss of generation-driven flexibility (provided by reservoir hydropower), substantially increasing total system costs. These findings demonstrate that, in addition to decarbonization policies (Figs. 2b and 3b ), inflow variabilities can also drive structural changes in the optimal mix of energy portfolios (Supplementary Fig. 9 ). A changing climate with more unpredictable inflows could further complicate the trade-offs between long-term investment (considering cost savings) and environmental sustainability concerns (considering water savings).

Our study also suggests that AdaptiveHydro can deliver huge non-monetary benefits that were previously overlooked. Our back-of-the-envelope analysis estimates that an average 57.2 Mt of carbon emissions can be avoided each year in high carbon emission reduction scenarios (see Supplementary Note 4 for methodological details) if hydropower operations are shifted from peak-shaving-oriented operation to balancing-oriented operation. These avoided emissions are equivalent to 10.7% of the total carbon emissions of the electricity sector in the CSG in 2018 (536.9 Mt) 21 . In addition to these carbon abatements, higher VRE penetration simultaneously improves water availability facilitated by large-scale hydropower flexibility and thus leads to additional water savings, especially under deeper decarbonization scenarios. For instance, in a normal inflow year, if the CSG achieves a 100% cut in 2050 carbon emissions from 2018 levels, 123.8 km 3 of water can be preserved each year to support non-hydro purposes (Fig. 3a ), for example, managed aquifer recharge 22 . Volumetrically, that is more than twice the annual agricultural water demand (60.2 km 3 ) 23 of the CSG and over three times the reservoir storage of the Three Gorges Dam (Fig. 1a ), the world’s largest hydropower dam with a 39.3 km 3 maximum storage capacity 23 .

It should be noted that a few factors not considered in our analysis may bias our estimates of cost savings and the WSV. On the one hand, our current analysis of cost savings and the WSV may be conservative because we did not consider small-reservoir and run-of-river hydropower plants or reduced cooling water consumption due to the phasing out of coal-fired power plants 24 . Consideration of these additional water savings could lead to a higher estimate of the WSV. On the other hand, our analysis could potentially overestimate cost savings and the WSV as we did not consider sediment trapping in reservoirs 25 , which reduces storage capacity and therefore jeopardizes hydropower flexibility. In addition, reservoir operations in our study were optimized in a deterministic way, assuming perfect inflow forecast without errors. In reality, uncertain and volatile inflow conditions complicate reservoir operations, especially during flood seasons, as power grid dispatchers tend to adopt conservative dispatching strategies for flood control purposes and therefore power generation may be sacrificed. This causes hydropower operation to deviate from the optimal state and thus the anticipated cost savings and WSV are hard to achieve. Improving inflow forecast 26 and reducing sediment trapping in reservoirs 25 are crucial to fully tap the potential of hydro–VRE complementarity to maximize cost savings and the WSV. Additionally, we did not address certain environmental concerns, such as total dissolved gas constraints, which could limit maximum discharge and reduce hydropower flexibility, leading to lower cost savings. Moreover, our model set-up does not consider the dynamic interactions between water savings and changes in the water head. A more complex water management analysis is needed towards a more accurate quantification of water savings. Last but not the least, we did not consider the dynamics of the electricity market, which could potentially lead to an overestimation of the benefits of AdaptiveHydro operations. This is because, in practice, hydropower utilities may lack the necessary incentives to transition from FixedHydro to AdaptiveHydro operations. To encourage such a shift, it is essential to implement suitable cost and revenue allocation strategies, such as the transfer of payments of regional transmission organization to hydropower producers 16 .

While our study focused on China’s CSG, the PREP-SHOT model developed here can be readily applied to other renewable-rich regions (for example, Southeast Asia, West Africa and the Amazon) to scale up VRE and guide low-impact hydropower development 27 , 28 . This is facilitated by the recent proliferation of publicly available data related to climate (for example, reanalysis 29 ), hydrology (for example, natural inflow 30 and reservoir and hydropower characteristics) and socioeconomics (for example, technology cost parameters 31 and capacity factors of VRE 32 ). For data that are not readily available, such as national or sub-national hourly electricity demand, existing well-established methods 33 can be used to generate proxy data and use them in sensitivity analyses to deliver robust findings. However, big challenges may exist, especially across regions with transboundary rivers, where the joint optimization of cascade reservoirs across countries requires improved cross-border cooperation.

Energy expansion optimization model

Model overview.

We have developed a transparent, modular and open-source energy expansion model, PREP-SHOT (see Supplementary Fig. 1 for a high-level structural overview), to investigate how short-term cascade hydropower reservoir operation affects long-term energy planning. Compared with existing energy planning models, which treat hydropower as fixed processes 17 , overlook the dynamic nature of water heads 16 or simply aggregate multiple hydropower stations into a single unit 14 , a unique feature of PREP-SHOT is that it explicitly considers the plant-level water head dynamics (that is, time-varying water head and storage) and system-level network topology of all hydropower stations within a regional grid. This allows us to better capture the multi-scale dynamic feedbacks between hydropower operation and energy system expansion, as well as to realistically simulate the magnitude and spatiotemporal variability of hydropower output, especially in regions with a large number of cascade hydropower stations. PREP-SHOT uses a multi-scale, constrained optimization approach that jointly considers both short-term (for example, hourly to daily) dispatch processes and long-term (for example, annual to decennary) capacity expansion decisions in different planning zones and horizons. The goal of PREP-SHOT is to identify optimal expansion pathways of power system decisions (for example, technology portfolio, transmission capacity and generation process) that minimize the total system cost (that is, variable and fixed O&M cost, fuel cost and investment cost) subject to a set of power balance, water balance and available resources constraints. Mathematically, the objective function of PREP-SHOT is formulated in Supplementary Equation ( 1 ), subject to the following 10 sets of constraints:

Lifetime constraints (Supplementary Equations ( 11 ) and ( 12 ) in Supplementary Note 3 , similarly hereafter)

Carbon emission constraints (Supplementary Equations ( 13 ) and ( 14 ))

Power balance constraints (Supplementary Equation ( 15 ))

Transmission constraints (Supplementary Equations ( 16 )–( 18 ))

Power output constraints (Supplementary Equations ( 19 )–( 23 ))

Power output variation constraints (Supplementary Equations ( 24 )–( 26 ))

Energy storage constraints (Supplementary Equations ( 27 )–( 32 ))

Water balance constraints (Supplementary Equations ( 33 )–( 35 ))

Reservoir outflow constraints (Supplementary Equations ( 36 )–( 38 ))

Reservoir storage constraints (Supplementary Equations ( 39 )–( 41 ))

Cost estimations and model constraints are detailed in Supplementary Notes 2 and 3 . We implemented two methods (that is, the simplex method and the barrier method) to solve the above linear programming problem. Solutions were selected from the method that obtained the optimal conditions with less computational time.

Representation of short-term cascade reservoir operations

The power output of hydropower station s at hour h of month m of year y ( \({\,{{\mbox{power}}}\,}_{s,h,m,y}^{{{{\rm{hydro}}}}}\) ) was determined by the net water head ( \({\,{{\mbox{head}}}\,}_{s,h,m,y}^{{{{\rm{net}}}}}\) ) and the discharge flowing through the turbines (called generation flow, \({\,{{\mbox{outflow}}}\,}_{s,h,m,y}^{{{{\rm{gen}}}}}\) ) according to the following equation:

where η s is the output efficiency of the hydropower station s , ρ is the density of water (1,000 kg m − 3 ) and g is the acceleration of gravity (9.8 m s − 2 ). The output efficiency η s is constant for a specific hydropower station. Here, we treated \({\,{{\mbox{outflow}}}\,}_{s,h,m,y}^{{{{\rm{gen}}}}}\) as a decision variable and calculated \({\,{{\mbox{head}}}\,}_{s,h,m,y}^{{{{\rm{net}}}}}\) using a set of non-linear equality constraints. Specifically, \({\,{{\mbox{head}}}\,}_{s,h,m,y}^{{{{\rm{net}}}}}\) can be calculated by subtracting the tailrace water level ( \({z}_{s,h,m,y}^{{{{\rm{tailrace}}}}}\) ) and total water head loss ( \({\,{{\mbox{head}}}\,}_{s,h,m,y}^{{{{\rm{loss}}}}}\) ) from the average forebay water level \(\left(({{z}_{s,h-1,m,y}^{{{{\rm{forebay}}}}}+{z}_{s,h,m,y}^{{{{\rm{forebay}}}}}})/{2}\right)\) between hour h  − 1 and h as follows (see Supplementary Fig. 12 for a detailed illustration):

\({z}_{s,h,m,y}^{{{{\rm{tailrace}}}}}\) can be determined from the tailwater rating curve (Supplementary Fig. 13a ) through a piecewise linear function ( \({f}_{s}^{\mathrm{zq}}(\cdot )\) ) that empirically links \({z}_{s,h,m,y}^{{{{\rm{tailrace}}}}}\) with the total released flow (that is, the sum of the generation flow ( \({\,{{\mbox{outflow}}}\,}_{s,h,m,y}^{{{{\rm{gen}}}}}\) ) and the released flow over spillways ( \({\,{{\mbox{outflow}}}\,}_{s,h,m,y}^{{{{\rm{spillage}}}}}\) )):

\({\,{{\mbox{head}}}\,}_{s,h,m,y}^{{{{\rm{loss}}}}}\) is determined by a quadratic function of \({\,{{\mbox{outflow}}}\,}_{s,h,m,y}^{{{{\rm{gen}}}}}\) following the method suggested in ref. 34 :

where K s is a constant representing the experimental water head loss coefficient.

Similarly, forebay water level ( \({z}_{s,h,m,y}^{{{{\rm{forebay}}}}}\) ) is a function of reservoir storage ( \({\,{{\mbox{storage}}}\,}_{s,h,m,y}^{{{{\rm{reservoir}}}}}\) ) and thus can be determined by the stage-storage curve (Supplementary Fig. 13b ) as follows:

where \({f}_{s}^{\mathrm{zs}}(\cdot )\) represents the piecewise linear function of the stage-storage curve. While it is computationally challenging to estimate water head dynamics due to the discrete, non-convex, non-linear and high-dimensional nature of the optimization problem 35 , to balance the trade-off between numerical accuracy and computational efficiency, PREP-SHOT implements a simulation-based iterative procedure 36 to explicitly calculate the time-varying net water heads instead of using traditional piecewise linear functions 35 or fitted non-linear functions 37 . The simulation-based iterative procedure involves the following five steps (see Supplementary Fig. 14 for the complete flow chart):

Set the initial net water head ( \({\,{{\mbox{head}}}\,}_{s,h,m,y}^{{{{\rm{net}}}}}\) , here the designed water head of a hydropower station is used as the initial net water head) for hydropower station s at hour h of month m of year y and the number of iterations n  = 1.

Solve the PREP-SHOT model using the initial (or previously updated) net water head. Because a fixed net water head is set, the original non-linear optimization problem can be simplified to a linear programming model that can readily be solved.

Obtain \({\,{{\mbox{outflow}}}\,}_{s,h,m,y}^{{{{\rm{spillage}}}}}\) , \({\,{{\mbox{outflow}}}\,}_{s,h,m,y}^{{{{\rm{gen}}}}}\) and \({\,{{\mbox{storage}}}\,}_{s,h,m,y}^{{{{\rm{reservoir}}}}}\) from the optimal solutions obtained in Step 2 and then substitute them into equations ( 2 )–( 5 ) to compute the intermediate water head ( \({\,{{\mbox{head}}}\,}_{s,h,m,y}^{{{{\rm{intermediate}}}}}\) ).

Calculate the relative error (RE) between \({\,{{\mbox{head}}}\,}_{s,h,m,y}^{{{{\rm{intermediate}}}}}\) and \({\,{{\mbox{head}}}\,}_{s,h,m,y}^{{{{\rm{net}}}}}\) as follows:

where abs( ⋅ ) is a function that returns the absolute value of a number and ∣ ⋅ ∣ represents the number of elements in the set (see Supplementary Note 1 for detailed description of all sets).

Compare the RE with a given threshold ϵ and n with a predefined maximum number of iterations N . If RE ≤  ϵ or n  ≥  N , stop the iteration and obtain the optimal solutions from Step 2. Otherwise, update \({\,{{\mbox{head}}}\,}_{s,h,m,y}^{{{{\rm{net}}}}}\) according to the following formula, update n  :=  n  + 1 and then return to Step 2:

where α represents the step size (varying from 0 to 1) used to update the net water head during the iteration. It is a parameter that controls the convergence speed of the numerical algorithm. Here, we set α  = 1/ n to balance the solving speed and accuracy.

The default setting of the PREP-SHOT model described above is referred to as AdaptiveHydro, in which hydropower operations can be adjusted flexibly up to a certain extent (rather than using a separately optimized fixed operation process) to balance load demand with anticipated future increases in VRE penetration. This is similar to the balancing-oriented operation detailed in ref. 2 . To quantify the benefit of short-term flexible hydropower operation for the long-term energy expansion, we also designed a baseline FixedHydro scenario, in which we directly solved an energy expansion model with a predefined hydropower output process (that is, constant input with a fixed reservoir operation process) rather than treating hydropower output processes as decision variables (that is, dynamic input with an adaptive reservoir operation process that can be optimized). Here, the fixed hydropower output process was obtained by maximizing the benefit of peak-shaving and hydroelectricity generation, a typical strategy towards peak-shaving-oriented operation 8 . In contrast to AdaptiveHydro, which tries to minimize total system-wide cost (Supplementary Equation ( 1 )), optimization in FixedHydro tries to minimize the sum of squared remaining demand ( F ; equation ( 8 )), which has the following form:

where demand h , m , z is the total electric demand at hour h of month m in zone z and \({\mathrm{power}}^{{\mathrm{hydro}}}_{h,m,s}\) is the power output of hydropower station s at hour h of month m . Here, the optimization in FixedHydro shares the same sets of constraints (that is, power output, power output variation, water balance, reservoir outflow and reservoir storage) as AdaptiveHydro. Similarly, the simulation-based iterative procedure described above was also used here to estimate the net water heads in a computationally efficient manner.

Model configuration

PREP-SHOT was designed as a multi-technology, multi-node and intertemporal optimization model. PREP-SHOT supports multi-technology expansion and groups all energy technologies into four categories: ‘hydro’, ‘storage’, ‘non-dispatchable’ and ‘dispatchable’ technologies. As we were particularly interested in the value of flexible hydropower, PREP-SHOT considers a location-specific, plant-level ‘hydro’ generation process, rather than aggregating all hydropower generation at a larger spatial scale (province level in this study), a typical strategy widely adopted in other existing models 14 . ‘Storage’ technologies in PREP-SHOT typically include pumped storage hydropower and lithium-ion batteries. We assumed that these technologies can discharge when the total power output of all technologies cannot meet the total electric demand and the amount of discharged electricity is limited only by the stored electricity level. Excess electricity will be charged if the total power output of all technologies exceeds the total electric demand. In real-world applications, it is typically avoided to have ‘storage’ technologies charged and discharged simultaneously 38 . This practice stems from the goal of minimizing system costs, aiming to prevent any unnecessary loss of electricity. While our model does not enforce a specific constraint regarding this, the principle of cost minimization inherent in our model ensures that this practice is upheld. ‘Non-dispatchable’ technologies refer to VRE that can be curtailed when total supply exceeds total demand, mostly solar PV and wind power. Such technologies are limited by capacity factors driven by instantaneous weather conditions that are location-dependent (see Supplementary Note 5 for details). In contrast to the ‘non-dispatchable’ technologies, ‘dispatchable’ technologies can be controlled within a certain range and usually serve as complementary and flexible power supply. These include coal-fired plants, nuclear plants and gas plants, among others, and can be dispatched flexibly between technical minimum output and installed capacity (see Supplementary Note 3 for details).

PREP-SHOT also includes a zone index z for decision variables and parameters that are zone-specific. This feature allows energy planners to model large-scale interconnected regional grids with multiple spatial nodes (for example, provinces and countries), where electricity can be transmitted between different nodes through existing or planned transmission lines. PREP-SHOT applies a transportation model (also called pipeline model) to simulate the power transmission between spatial nodes. This model assumes that the transmitted power is limited by only the capacity of the transmission lines between two spatial nodes 39 . In addition, all transmission lines are bidirectional, but simultaneous bidirectional transmission is physically not allowed. Similar to the ‘storage’ technologies, simultaneous bidirectional transmission is automatically prohibited to align with the objective of minimizing system costs. In this study, the geometric centre of each province in the CSG was selected as the location of the node to calculate the distance between province pairs. This distance was then used to calculate the cost of newly built transmission lines (Supplementary Table 3 ).

On top of the multi-node feature, PREP-SHOT also introduces a time domain with a multi-layer time slice ( h , m , y ) to support intertemporal optimization. For instance, multi-period near-future and long-term investment decisions can be optimized over the entire planning horizon. Intertemporal constraints on short-term power output processes (for example, power output variation constraints) can also be explicitly represented (see Supplementary Note 2 for detailed cost estimations and Supplementary Note 3 for the power output variation constraints used for the intertemporal optimization). To maintain computational tractability while also realistically representing both long-term investment decisions and short-term operational details of the energy system, PREP-SHOT implements a three-level time slice to solve the intertemporal optimization. The top-level time slice is Year ( y ), characterizing the investment period at the annual timescale. As it is computationally expensive to optimize the high-dimensional energy system for a full year (8,760 h) over the entire planning horizon (usually a few decades), PREP-SHOT introduces a second-level time slice, Month ( m ), to reduce the computational burden, while at the same time still representing seasonal variability, which is especially relevant for hydropower. The third-level time slice is Hour ( h ), at which scale PREP-SHOT can realistically model the short-term (for example, hourly) variability of energy demand, the intermittency of VRE and the operational flexibility of hydropower.

In this study, we ran PREP-SHOT over 48 consecutive hours for two representative days in each season (that is, January–March, April–June, July–September and October–December) for seven modelled years (2018, 2025, 2030, 2035, 2040, 2045 and 2050). There are five spatial nodes in PREP-SHOT, representing the five provinces (Guangdong, Guangxi, Guizhou, Hainan and Yunnan; see Fig. 1a ) in the CSG. PREP-SHOT was built using Python 3.8.14 ( https://www.python.org ) with Pyomo 5.7.3 ( https://github.com/Pyomo/pyomo ) and solved by Gurobi 9.5.0 ( https://www.gurobi.com/ ).

Experimental design and scenarios

Carbon emission reduction scenarios.

In this study, we considered 81 carbon emission reduction scenarios (Fig. 1c ) to examine to what extent the value of hydropower flexibility varies with the decarbonization level. These scenarios were designed on the basis of China’s recent pledge to peak carbon dioxide emissions by 2030 and to achieve carbon neutrality by 2060. We assumed that the percentage carbon emission reduction in the period 2018–2050 varies from 20% (less decarbonized) to 100% (fully decarbonized) with a discrete step size of 1% (that is, 20%, 21%, …, 99%, 100%). For each scenario, we assumed a same upper bound of annual carbon emissions over the period 2018–2030, which was set to the CO 2 emissions from power generation in the CSG in 2018 (that is, 536.9 Mt) 21 . From 2030 to 2050, we assumed a linear decrease in the upper bound of CO 2 emissions in each scenario.

Inflow scenarios

We designed 117 inflow scenarios (1 normal, 8 wet, 8 dry and 100 interannual variability) to investigate how inflow variability affects our results. We used the estimated inflow in 2018 (see details in the Natural inflow data section below) to represent normal conditions as the baseline scenario. To obtain the i th inflow scenario ( \({\mathrm{inflow}}^{{\mathrm{net}}}_{s,h,m,y,i}\) ) reflecting drier- or wetter-than-normal conditions, we included a constant factor ( ω i ) to scale the baseline scenario ( \({\mathrm{inflow}}^{{\mathrm{net}}}_{s,h,m,y}\) ) as follows:

where ω i ranges from −40% to 40% in increments of 5% excluding 0% (−40%, −35%, ⋯ , −5%, 5%, ⋯ , 35%, 40%). We applied the estimated net inflow in 2018 to future representative years (2025, 2030, 2035, 2040, 2045 and 2050) for these 17 inflow scenarios. Given that we only had streamflow records for one year (that is, 2018), which cannot capture the year-to-year inflow variability, we used the Global Reach-scale A priori Discharge Estimates for SWOT (GRADES) dataset 30 , a 40-year (1979–2018) global streamflow reanalysis. To factor in the uncertainty of inflow interannual variability, we used Bootstrap to randomly sample seven representative years from the 40-year pool of historical bias-corrected GRADES and repeated this process 100 times. We used a simultaneous sampling approach to obtain historical streamflow data from the same time period for all hydropower plants. This allowed us to maintain the spatial correlation between different hydropower plants, which is important to accurately capture realistic hydraulic connections for watersheds with cascade reservoirs.

Key data assumptions

Technology and cost assumptions.

In this study, we considered six primary technologies, that is, ‘dispatchable’ coal-fired and nuclear power, ‘non-dispatchable’ solar and onshore wind power, conventional hydropower and pumped storage hydropower. We took 2018 (the most recent year with available data) as the starting year to define the initial installed capacity of each technology (Supplementary Table 4 ), derived from various sources, including the China Power Statistics Yearbook 2021 40 , the National Energy Administration 41 and China Nuclear Energy Association 42 . Cost parameters are summarized in Supplementary Table 5 . Projected cost reductions (percentage changes) between 2018 and 2050 were derived from the National Renewable Energy Laboratory’s annual technology baseline assessment in 2020 31 . The investment cost of conventional hydropower and pumped storage hydropower was assumed to be constant over the entire planning horizon. Changes in investment cost for other technologies (that is, wind, solar, coal-fired and nuclear power) are shown in Supplementary Fig. 15 . The investment cost of ultrahigh vacuum direct current transmission lines was set to ¥900 MW −1  km −1 (where ¥ is Chinese yuan; ref. 21 ). The province-level capacity of transmission lines was taken from ref. 5 (Supplementary Table 6 ). We assumed an average 94% efficiency of all transmission lines based on the province-level line loss rate documented in China’s 2021 power industry development report 21 .

The projected carbon content (that is, CO 2 emissions per unit of generated coal-fired electricity) in each modelled year was estimated by extrapolating historical (2005–2020) 40 trends using the optimal piecewise linear fitting approach 43 (Supplementary Fig. 16 ). Three predefined line segments were used and breakpoints were determined by minimizing the total sum of squared errors between fitted values and actual values. We also factored in the age-capacity relationship of existing power stations in each province when we estimated the existing capacity of technologies (see Supplementary Table 7 for the age and corresponding capacity of coal-fired power plants in 2018 obtained from the Global Energy Monitor’s Global Coal Plant Tracker 44 ).

Demand profile

Province-level hourly electric demand in 2018 was compiled by the CSG 45 . The near-future (2018–2030) growth rate of demand was obtained from ref. 46 . We assumed a 40% (for 2030–2040) and 67% (for 2040–2050) 39 , 47 decrease in the growth rate relative to 2018–2030 (Supplementary Table 8 ). To estimate the required load demand for each representative year in the future, we scaled the observed load demand in 2018 using the projected annual average growth rate from 2018 to 2050 47 . As it is computationally extremely expensive to run PREP-SHOT at the hourly time step for the entire year, we focused on a representative 2-day period in spring (January–March), summer (April–June), autumn (July–September) and winter (October–December). Such seasonal-dependent representative load demand profiles were generated on the basis of a k -nearest neighbours algorithm 48 , which can identify the optimal non-linear alignment between two time series via dynamic time warping. We first obtained the optimal clustered demand profiles for two consecutive days (48 h in total, from 00:00 to 23:00). We then searched within each season to find the representative date whose actual 48-h consecutive load demand best matches the clustered load demand time series by minimizing the Euclidean distance between these two time series (Supplementary Fig. 17 ). The obtained representative dates in each season were 10–11 March in spring, 8–9 June in summer, 17–18 August in autumn and 27–28 November in winter (see Supplementary Fig. 18 for the seasonal representative load demand profiles for each province in the CSG) and remained consistent across all scenarios, including normal, dry, wet and interannual variability flows. It should be noted that the selection of representative periods in this study was focused solely on electricity and, therefore, may not accurately represent water use patterns. Future research should incorporate changes in both electricity and water to identify more suitable representative periods.

Natural inflow data

Forty-six hydropower stations with an installed capacity of over 300 MW were selected for the analysis, according to data availability. The cascade topology of these selected hydropower stations is shown in Supplementary Fig. 2 and their key characteristics are summarized in Supplementary Table 1 . As the natural inflow of each reservoir cannot be observed directly (only the outflow downstream of each reservoir is measured), we used net inflow instead. Here, net inflow factors in the net effect of evaporation, upstream withdrawal and precipitation on the control area of the reservoir. The net inflow can be derived using the water balance principle as follows (Supplementary Fig. 19 ):

where \({{{{\rm{inflow}}}}}_{s,h,m,y}^{{{{\rm{net}}}}}\) is the net inflow into hydropower station s at hour h in month m of year y , \({{{{\rm{storage}}}}}_{s,h,m,y}^{{{{\rm{reservoir}}}}}\) represents the observed storage of hydropower station s at hour h in month m of year y , Δ h represents the simulation time step (that is, 1 h), \({{{{\rm{outflow}}}}}_{s,h,m,y}^{{{{\rm{total}}}}}\) refers to the observed total outflow, which is the sum of the spillage outflow ( \({\,{{\mbox{outflow}}}\,}_{s,h,m,y}^{{{{\rm{spillage}}}}}\) ) and generation outflow ( \({\,{{\mbox{outflow}}}\,}_{s,h,m,y}^{{{{\rm{gen}}}}}\) ), \({{{{\mathcal{IU}}}}}_{s}\) represents the set of all immediate upstream hydropower stations of hydropower station s , τ su, s is a constant representing water travel time between hydropower station s and its immediate upstream hydropower station su. It should be noted that some negative values of estimated net inflow, likely caused by evaporation and water withdrawal to meet other needs (for example, agricultural irrigation), were retained in our calculations.

Capacity factors of solar and wind energy

Capacity factors (CFs) of VRE were derived using the Modern-Era Retrospective analysis for Research and Application version 2 (MERRA-2) reanalysis product 49 . Seasonal-varying province-averaged CF over the representative days were estimated according to the following three steps:

Calculate the pixel-level (0.625° × 0.5° spatial resolution) CF using the surface incoming shortwave radiation, the top-of-the-atmosphere incoming shortwave radiation and the temperature at 2 m (for solar power) and wind speed at 10 and 50 m (for wind power; see Supplementary Note 5 for details).

Calculate the province-level CF by spatially averaging all grid cells in each province weighted by the pixel area.

Scale the province-level CF at hour h of month m by the ratio of the average CF in 2018 (CF 2018 ) and the long-term historical (1980–2019) averaged CF (CF hist ) as follows:

Then we selected the scaled CF ( \({\,{{\mbox{CF}}}\,}_{h,m,2018}^{{{{\rm{scaled}}}}}\) ) for the representative dates (Supplementary Fig. 4 ). Here, we applied \({\,{{\mbox{CF}}}\,}_{h,m,2018}^{{{{\rm{scaled}}}}}\) to each representative year by assuming unchanged climatology drivers for solar and wind CFs.

Benefits of hydropower flexibility

The WSV (m 3  yr −1  MWh −1 ) of VRE measures how much water can be conserved for non-hydropower purposes each year per megawatt hour increase in electricity generated by VRE, specifically from solar and wind energy. To estimate WSV, we introduced a new term, μ (¥ m − 3 ), which characterizes the marginal value of each unit of water saved, into the objective function, acknowledging the fact that the water saved for other beneficial uses (for example, irrigation) can compensate part of the total system costs. As it is not possible to obtain a reliable estimate of μ , we used the price of irrigation water as a proxy of μ given the fact that the use of water for irrigation dominates our study area. We found that the total water savings over the entire planning horizon are not sensitive to the choice of μ (Supplementary Fig. 20 ), whose range was adjusted according to ref. 50 and the final value was determined through a trial-and-error process. Empirically, WSV can be estimated from the linear slope fitted between water savings (aggregated across all reservoirs) and total VRE generation (Supplementary Fig. 21a ). Here, we used a piecewise linear fit instead of fitting all data points using a single linear function because of the inherent non-linear relationship between water savings and total VRE generation. To obtain a robust fit (which requires sufficient data samples) while also capturing the non-linearities (which requires sufficient segments), the piecewise line fit was performed over four groups according to the degree of decarbonization: low (20–39%), medium (40–59%), medium–high (60–79%) and high (80–100%). Within each group, we pooled all data points to obtain the optimal slope and estimate the 95% confidence interval using Bootstrap (10,000 times).

Curtailment rate of renewables

The curtailment rate (CR) of VRE is defined as the ratio between curtailed renewable generation and total potential generation:

where gen h , m , y , z , e represents the generation of non-dispatchable technology e for zone z at hour h in month m of year y , CF h , m , y , z , e is the CF of non-dispatchable technology e at hour h in month m of year y and \({\,{{\mbox{cap}}}\,}_{y,z,e}^{{{{\rm{existing}}}}}\) represents the existing installed capacity of non-dispatchable technology e in zone z in year y .

Model limitations

It is important to note that our framework relies on simplified modelling assumptions to balance the trade-offs between model representability and computational efficiency. For example, in actual hydropower operations, shifting from FixedHydro to AdaptiveHydro can lead to increased operating costs due to more frequent ramping and start/stop of hydropower plants. While directly including such constraints in PREP-SHOT would more accurately quantify system-level costs and water savings, it would also require optimizing ~183,000 additional binary variables, making the optimization problem intractable, particularly for sensitivity analyses with a large number of scenarios. Despite these limitations, our post analysis shows that the attained cost savings are only slightly reduced by 3.69–6.36% (Supplementary Fig. 22 ) with almost no impact on water savings (reduced by 0.09–0.32%; Supplementary Fig. 23 ) if the start/stop constraints are considered. Another important consideration is the representation of complex river dynamics. The default setting of PREP-SHOT uses a constant travel time in river routing processes as this allows us to substantially reduce the computational burden associated with the complex hydraulic connections of cascade reservoirs. Although a more sophisticated river routing method, such as an impulse response function 8 , could better capture river dynamics, it would increase the computational burden by more than a factor of five, even for a single experiment. Nevertheless, we found that using a more complex river routing method, such as the impulse response function, does not affect our main conclusions, as the attained cost and water savings remain largely the same (Supplementary Fig. 24 ).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

MERRA-2 data can be downloaded from https://disc.gsfc.nasa.gov/datasets?project=MERRA-2 . GRADES data can be downloaded from https://www.reachhydro.org/home/records/grades . The river lines and basin boundaries used for map illustration can be downloaded from https://www.hydrosheds.org/products . Dam and reservoir characteristics can be downloaded from https://www.globaldamwatch.org/ . All other data used in the optimization are provided in the Supplementary Information and are cited from publicly available sources. Source data are provided with this paper.

Code availability

The energy expansion model PREP-SHOT is available under the GNU General Public License version 3 (GPLv3) and can be downloaded from the GitHub repository ( https://github.com/PREP-NexT/PREP-SHOT ) of the Pathways for REsilient Planning of water-energy-food Nexus Transformation (PREP-NexT) Lab. The Python scripts used to produce the results in this paper are available upon request from Z.L.

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Acknowledgements

This work was supported by Singapore’s Ministry of Education (MOE) Academic Research Fund Tier-1 (A-0009297-01-00 and A-8000190-00-00). X.H. acknowledges the National University of Singapore’s College of Design and Engineering for providing additional financial support through Outstanding Early Career Awards (A-8001228-00-00, A-8001389-00-00 and A-8001389-01-00). The computational work for this study was (fully/partially) performed using the resources of the National Supercomputing Centre, Singapore.

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Source data fig. 1.

Guangxi, Guangdong, Yunnan, Guizhou, Hainan, Hongkong and Taiwan map; selected 46 reservoir locations and installed capacity; rivers; past and projected future generation of China Southern Power Grid (1995–2050); carbon emission reduction scenarios across 2018–2050.

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Total cost over 2018–2050 over different carbon emission reduction scenarios; cost savings under normal inflow scenario by technologies over different carbon emission reduction scenarios; curtailment rate of wind and solar power across different carbon emission reduction scenarios; cost savings under different inflow scenarios over different carbon emission reduction scenarios.

Source Data Fig. 3

Variable renewable energy power generation; water savings versus hydropower generation under different carbon emission reduction scenarios and normal inflow scenario; optimized installed capacity of various technologies under different carbon emission reduction scenarios and normal inflow scenario; water savings versus variable renewable energy power generation under wet, dry and normal inflow scenarios and different carbon emission reduction scenarios; water sustainability value under wet, dry, normal and interannual variability inflow scenarios as well as different carbon emission reduction scenarios.

Source Data Fig. 4

Power generation portfolios and load demand during the spring of 2045 for a zero-carbon emission scenario under FixedHydro and AdaptiveHydro operation schemes.

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Liu, Z., He, X. Balancing-oriented hydropower operation makes the clean energy transition more affordable and simultaneously boosts water security. Nat Water 1 , 778–789 (2023). https://doi.org/10.1038/s44221-023-00126-0

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Hydroelectric Power Dams: Harnessing the Power of Water

Table of contents, introduction, the function and operation of hydroelectric power dams, benefits of hydroelectric power dams, environmental considerations and mitigation measures.

• Water Intake: Hydroelectric dams are strategically constructed across rivers to create reservoirs that store vast amounts of water. To control the flow of water, intake structures are built. These structures regulate the amount of water entering the dam, ensuring a consistent supply for power generation.
• Turbines: When water is released from the reservoir, it flows through channels or penstocks and strikes the blades of the turbines. The force of the flowing water causes the turbines to rotate. The design and arrangement of the turbines may vary depending on the specific dam configuration. As the turbines rotate, they convert the kinetic energy of the water into mechanical energy.
• Generators: The rotating turbines are connected to generators, which are essentially large electromagnets. As the turbines spin, they rotate the shaft of the generator, inducing a magnetic field. This magnetic field causes electrons to move within the generator's wires, generating an electric current. The generated electricity is then harnessed and transmitted through power lines to homes, businesses, and industries via the power grid.
• Renewable and Clean Energy: Hydroelectric power is a sustainable and renewable energy source. It relies on the natural water cycle, harnessing the energy of flowing or falling water to generate electricity. Unlike fossil fuel power plants, hydroelectric dams produce minimal greenhouse gas emissions, contributing to the reduction of carbon dioxide and other pollutants in the atmosphere. This makes hydroelectric power a vital component of efforts to mitigate climate change and transition to cleaner energy alternatives.
• Reliable Power Generation: Hydroelectric power provides a reliable and consistent source of electricity. The availability of water is relatively constant, ensuring a steady supply of energy. This reliability reduces the risks associated with fuel price fluctuations and supply disruptions often experienced with fossil fuel power plants. Hydroelectric power plants can quickly respond to fluctuations in electricity demand, making them highly adaptable to varying energy needs.
• Water Resource Management: Hydroelectric power dams play a crucial role in water resource management. The reservoirs created by these dams allow for the storage of water during periods of high flow, such as during heavy rains or snowmelt. This stored water can then be released during times of low flow or drought, ensuring a consistent water supply for various purposes. Hydroelectric dams contribute to agricultural irrigation, municipal water supply, and the prevention of floods by controlling the release of water downstream.
• Ecosystem Impact: The construction of dams can have significant effects on aquatic ecosystems. The alteration of natural river flow, the obstruction of fish migration paths, and changes in water temperature and oxygen levels can disrupt aquatic habitats. To mitigate these impacts, various measures can be implemented. Fish ladders or fish bypass systems can be installed to provide fish with a way to navigate around the dam and reach their spawning grounds. Additionally, environmental flow releases can be scheduled to mimic natural flow patterns and maintain the ecological balance of the river system.
• Land and Habitat Alteration: The flooding of large areas of land during dam construction can result in the loss of terrestrial habitats and the displacement of wildlife. To mitigate these effects, it is important to consider the creation of protected areas surrounding the dam site. These protected areas can serve as sanctuaries for displaced wildlife and promote habitat restoration programs. It is also crucial to identify and preserve key biodiversity areas to ensure the conservation of important ecosystems and species.
• Sedimentation and Water Quality: Dams have the potential to trap sediments and disrupt the natural flow of sediments downstream. This can lead to reduced water quality and ecosystem degradation. To address this issue, sediment management strategies can be employed. Sediment flushing involves releasing controlled amounts of sediment downstream during periods of high flow to mimic natural sediment transport. Sediment bypass systems can also be implemented to redirect sediments around the dam, allowing them to continue their natural course downstream. These measures help maintain downstream ecosystems and preserve water quality.

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Hydro, wind and solar power as a base for a 100% renewable energy supply for South and Central America

Affiliations Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, São Paulo, Brazil, VTT Technical Research Centre of Finland Ltd., Lappeenranta, Finland

Affiliation Lappeenranta University of Technology, Lappeenranta, Finland

Affiliation VTT Technical Research Centre of Finland Ltd., Lappeenranta, Finland

* E-mail: [email protected]

• Larissa de Souza Noel Simas Barbosa, 
• Dmitrii Bogdanov, 
• Pasi Vainikka, 
• Christian Breyer

• Published: March 22, 2017
• https://doi.org/10.1371/journal.pone.0173820
• Reader Comments

Power systems for South and Central America based on 100% renewable energy (RE) in the year 2030 were calculated for the first time using an hourly resolved energy model. The region was subdivided into 15 sub-regions. Four different scenarios were considered: three according to different high voltage direct current (HVDC) transmission grid development levels (region, country, area-wide) and one integrated scenario that considers water desalination and industrial gas demand supplied by synthetic natural gas via power-to-gas (PtG). RE is not only able to cover 1813 TWh of estimated electricity demand of the area in 2030 but also able to generate the electricity needed to fulfil 3.9 billion m 3 of water desalination and 640 TWh LHV of synthetic natural gas demand. Existing hydro dams can be used as virtual batteries for solar and wind electricity storage, diminishing the role of storage technologies. The results for total levelized cost of electricity (LCOE) are decreased from 62 €/MWh for a highly decentralized to 56 €/MWh for a highly centralized grid scenario (currency value of the year 2015). For the integrated scenario, the levelized cost of gas (LCOG) and the levelized cost of water (LCOW) are 95 €/MWh LHV and 0.91 €/m 3 , respectively. A reduction of 8% in total cost and 5% in electricity generation was achieved when integrating desalination and power-to-gas into the system.

Citation: Barbosa LdSNS, Bogdanov D, Vainikka P, Breyer C (2017) Hydro, wind and solar power as a base for a 100% renewable energy supply for South and Central America. PLoS ONE 12(3): e0173820. https://doi.org/10.1371/journal.pone.0173820

Editor: Vanesa Magar, Centro de Investigacion Cientifica y de Educacion Superior de Ensenada Division de Fisica Aplicada, MEXICO

Received: September 16, 2016; Accepted: February 26, 2017; Published: March 22, 2017

Copyright: © 2017 Barbosa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: Public financing of Tekes (Finnish Funding Agency for Innovation) for the ‘Neo-Carbon Energy’ project under the number 40101/14; PhD scoolarship from CNPq (Brazil Council for Scientific and Technological Development). The study was funded by VTT Technical Research Centre of Finland Ltd. The funder provided support in the form of salaries for authors (LSNSB, DB, PV, CB), but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section.

Competing interests: The commercial affiliation (VTT Technical Research Centre of Finland Ltd) does not alter our adherence to PLOS ONE policies on sharing data and materials.

Abbreviations: BAU, Business as usual scenario; Capex, capital expenditures; CCGT, combined cycle gas turbine; ccs, carbon capture and storage; chp, combined heat and power; csp, concentrating solar thermal power; el, electricity; fix, fixed; gdp, gross domestic product; gt, gas turbine; HHV, base on higher heating value of fuel; hvdc, high voltage direct current; lcoc, levelized cost of curtailment; lcoe, levelized cost of electricity; lcoebau, levelized cost of electricity of BAU scenario; lcoebau-CO2, levelized cost of electricity of BAU scenario considering CO2 costs; lcog, levelized cost of gas; lcos, levelized cost of storage; lcot, levelized cost of transmission; lcow, levelized cost of water; LHV, base on lower heating value of fuel; ocgt, open cycle gas turbine; opex, operational expenditures; phs, pumped hydro storage; PtG, power-to-gas; PV, photovoltaic; RE, renewable energy; RoR, run-of-river; sng, synthetic natural gas; st, steam turbine; swro, seawater reverse osmosis; tes, thermal energy storage; th, thermal; wacc, weighted average cost of capital

Introduction

South and Central America are economically emerging regions that have had sustained economic growth and social development during the last decade. The regions’ 3% gross domestic product (GDP) growth rate [ 1 ] followed by an estimated fast-paced electricity demand growth over the coming decades [ 2 ] requires the development of the power sector in order to guarantee efficiency and security of supply.

The South and Central American electrical energy mix is the least carbon-intensive in the world due to the highest share of renewable energy, mainly based on hydropower installed capacities [ 3 , 4 ]. However, the need to reduce the vulnerability of the electricity system to a changing hydrological regime is evident. Natural climate variability and climate change have been modifying the hydrological cycle and water regime in the drainage basis, threatening the availability and reliability of hydropower sources of many countries in the region, especially Brazil [ 5 ]. Serious droughts and severe weather events in Brazil have caused a reduction of 45% in the average water levels in hydro dam reservoirs in the last four years [ 6 ], and due to the fact that 71% of the electricity supply in the country relies on hydropower [ 7 ], the changes have endangered the country’s electricity security and supply. Over the past decade hydropower’s share in South and Central America has been declining and the indications for the future are that the downward trend will continue [ 2 ]. Regarding non-hydro renewable energy (RE) potential, South and Central America have vast solar, wind and biomass potentials, which could allow the region to maintain its high share of renewables, even under a low hydropower future scenario [ 2 ].

Most parts of the region lies within the Sun Belt region of highest solar radiation [ 8 ], with Chile, Bolivia and Argentina among the ten countries in the world with maximum irradiation for fixed, optimally tilted PV systems [ 9 ]. Moreover, the Atacama Desert has the best global maximum solar irradiation of 2,770 kWh/(m 2 ∙a) (for fixed, optimally tilted PV systems) and is an excellent region for solar photovoltaics (PV) energy production [ 9 ].

Regarding the potential for wind energy generation, Brazil (northeast region), Chile (northwest region), Paraguay (north region), Bolivia (southeast region) and Argentina (south and east region) have high annual wind energy potentials [ 10 ], which make the region highly valuable for wind power. In fact, one of the best wind sites globally is located in the region of Patagonia, Argentina.

Concerning biomass resources, South and Central America have suitable climatic conditions, land availability and cheap labor when compared to other countries [ 11 ]. In total biofuel production, Brazil and Argentina are, respectively, the second biggest ethanol and biodiesel producers globally and a recent wave of investments from the governments has boosted the production of biofuels over the medium and long terms [ 11 , 12 ]. In addition, South and Central American solid wastes, and agricultural and industrial residues are able to generate 1025 TWh LHV per year in the region [ 13 ].

Added to the above mentioned facts, a few numbers of South American countries have been supported not only by a regulatory framework that has raised investments in renewable energy generation, but also by low-carbon development plans. Long-term electricity auctions, aiming either at guaranteeing the adequacy of the system or at RE system electricity support, have been occurring in South and Central American countries [ 14 ]. Over 13,000 MW of capacity has been contracted through tendering since 2007 in Argentina, Brazil, Chile and Peru [ 15 ]. Competitive bidding in Uruguay has reached the country target of 1 GW of wind power capacity by 2015 and Central American countries such as El Salvador, Guatemala, Honduras and Panama released bids for renewable energy in 2014 [ 15 ]. Brazil, Colombia, Bolivia, Chile, Costa Rica and Peru have national plans with climate change mitigation initiatives and scenarios [ 16 , 17 ] that can lead to national sustainable development and drive the changes in the countries’ energy systems. Chile’s government roadmap, launched in September 2015, is an excellent example of initiative since the report calls for no less than 70% of the country’s electricity demand being met by renewable energy sources by 2050, with an increase in 58% of actual renewable energy sources [ 18 ]. Costa Rica had been very close to reaching the 100% RE target already in 2015, since for 94 consecutive days of the year the total electricity had been covered by RE and the country reached 98% in total for the year [ 19 ]. Uruguay has slashed its carbon footprint in the last 10 years and, despite already having 94.5% of its electricity and 55% of its overall energy mix provided by RE, has announced a 88% cut in carbon emissions by 2017 compared with the average for 2009–13 [ 20 ].

As long as the energy systems in the region have a broad range of possible RE options and solutions supported by a regulatory framework, it has an essential role in addressing climate change and limiting global warming to less than 2°C compared to pre-industrial levels. High shares of renewables for the Latin American energy system have been outlined in other modelling studies for the year 2050, such as in [ 21 ] and [ 17 ]. Martínez et al. (2015) have considered three different assessment models to determine the energy and emissions trends in Brazil and the rest of the Latin American region up to 2050 based on a set of scenarios consistent with current trends and with the 2°C global mitigation target [ 17 ]. Greenpeace (2015) reports a compelling vision of what an energy future may look like for a sustainable world [ 21 ]. It presents two global scenarios in which energy is supplied 100% by renewable energy technologies with different reductions on energy intensity. The main differences between these studies and the study presented in this paper concern methodology and the existence of flexibility options for an overall balanced system. Both Martínez et al. (2015) and Greenpeace (2015) studies [ 21 , 17 ], for instance, have considered yearly resolution models (and not hourly resolution models) for RE generation, energy demand and supply. This approach, however, is not appropriate for systems relying on high shares of renewable energy since the energy generation varies hourly over time and does not guarantee that the hourly energy supply in a year covers the local demand from all sectors. Furthermore, the existence of different types of flexibility in the system, such as demand side management and energy shifted in location (transmission grids connecting different locations) were not evaluated in these studies either, and storage of energy at one location (and energy shifted in time) was only mentioned and not quantified.

Other studies [ 22 , 23 , 24 , 25 , 26 ] have performed the optimization of energy systems on an hourly basis with a high penetration of renewable energy for countries such as Ireland, USA, Australia and Northeast Asia. This study, using a similar hourly based model and analysing different grid development levels, aims at designing an optimal and cost competitive 100% RE power system for South and Central America. A potential evolution of the generation mix was considered and takes into account:

• the actual electricity trade and transmission infrastructure of different sub-regions of South and Central America
• an optimal system design and wise utilization of considered available RE resources
• synergy between various resources and different regions that increase the efficiency of the power sector

Three different scenarios with different high voltage direct current (HVDC) transmission grid development levels (region-wide, country-wide and area-wide energy systems) and one integrated scenario were analysed and compared. The integrated scenario considers an additional electricity demand for water desalination and industrial natural gas production, in order to give the system flexibility and to decrease overall cost guarantee that the water demand of the region will be fulfilled.

Methodology

The energy system model used in this study is based on linear optimization of energy system parameters under applied constraints and is composed of a set of power generation and storage technologies, as well as water desalination and synthetic natural gas (SNG) generation via power-to-gas (PtG) for industrial use, which operate as flexible demand. For a complete understanding of the whole energy system, a fully integrated scenario that also considers heat and mobility demand has to be modeled, even though this is not in the scope of this study. As the applied energy system model has already been described in [ 23 ] and [ 27 ], the coming sections do not include a detailed description of the model, its input data and the applied technologies. However, it presents a comprehensive summary and all additional information that has been assumed for the model in the present study. Further technical and financial assumptions can be found in the Supporting Information section in this paper.

Model summary

The energy system optimization model is based on a linear optimization of the system parameters under a set of applied constraints as described in detail previously [ 23 and 27 ]. The main constraint for the optimization is to guarantee that for every hour of the year the total electric energy supply within a sub-region covers the local demand from all considered sectors and enables a precise system description including synergy effects of different system components for the power system balance.

The aim of the system optimization is to achieve a minimal total annual energy system cost. The annual energy system cost can be calculated as the sum of the annual costs of installed capacities of the different technologies, costs of energy generation and costs of generation ramping. On the other hand, for residential, commercial and industrial electricity prosumers the target function is minimal cost of consumed energy, calculated as the sum of self-generation, annual cost and cost of electricity consumed from the grid, minus benefits from selling of excess energy. Prosumers are the ones that install respective capacities of rooftop PV systems and batteries and produce and consume electricity at the same time.

The model flow diagram that contains all the considered input data, system models and model output data is presented on Fig 1 .

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0173820.g001

Several types of input datasets and constraints are used in the model, as described previously by [ 23 ] and [ 27 ]:

• historical weather data for direct and diffuse solar irradiation, wind speed, precipitation amounts and geothermal data,
• synthetic load data for every sub-region,
• water and industrial natural gas demand,
• technical characteristics of used energy generation, storage and transmission technologies, such as power yield, energy conversion efficiency, power losses in transmission lines and storage round trip efficiency,
• capital expenditures, operational expenditures and ramping costs for all technologies,
• electricity costs for residential, commercial and industrial prosumers,
• limits for minimum and maximum installed capacity for all energy technologies,
• configuration of regions and interconnections.

Description of historical weather data can be found in [ 23 ] and [ 27 ] and is not highlighted in this paper.

Geothermal data are evaluated based on existing information on the surface heat flow rate [ 28 , 29 ] and surface ambient temperature for the year 2005 globally. For areas where surface heat flow data are not available, an extrapolation of existing heat flow data was performed. Based on that, temperature levels and available heat of the middle depth point of each 1 km thick layer, between depths of 1 km and 10 km [ 30 , 31 , 32 ] globally with 0.45°x0.45° spatial resolution, are derived.

Due to the fact that in the future, depletion and deterioration of available water resources can lead to water shortages, water demand was calculated based on water consumption projections and future water stress [ 33 ]. Water stress occurs when the water demand exceeds renewable water availability during a certain period of time. It is assumed that water stress greater than 50% shall be covered by seawater desalination and that there are no restrictions on the variable operation of the desalination plants [ 34 , 35 ]. Transportation costs are also taken into account and the methodology and calculations for seawater desalination are described in [ 36 ]. The energy consumption of seawater reverse osmosis desalination plants is set to 3.0 kWh/m 3 and horizontal and vertical pumping are 0.04 kWh/(m 3 ∙h∙100km) and 0.36 kWh/(m 3 ∙h∙100m), respectively [ 36 ]. The levelized cost of water (LCOW) includes water production, electricity, water transportation and water storage costs and will change according to renewable resource availability and cost of water transport to demand sites.

Present industrial gas consumption is based on natural gas demand data from the International Energy Agency statistics [ 37 ] and natural gas consumption projections for the year 2030 were calculated considering industrial annual growth projections based on the World Energy Outlook [ 1 ].

Applied technologies

The technologies used in the South and Central American energy system optimization can be divided into four different categories: conversion of RE resources into electricity, energy storage, energy sector bridging (for definition, see later), and electricity transmission.

The RE technologies for producing electricity applied in the model are ground-mounted (optimally tilted and single-axis north-south oriented horizontal continuous tracking) and rooftop solar PV systems, concentrating solar thermal power (CSP), onshore wind turbines, hydropower (run-of-river and dams), biomass plants (solid biomass and biogas), waste-to-energy power plants and geothermal power plants. Hydro run-of-river plants are the ones located in rivers that have a small reservoir capacity that stores maximum 48 full load hours of water in energy and hydro dams are the ones with bigger reservoirs, capable of storing energy up to months.

For energy storage, batteries, pumped hydro storage (PHS), adiabatic compressed air energy storage (A-CAES), thermal energy storage (TES) and power-to-gas (PtG) technology are integrated to the energy system. PtG includes synthetic natural gas (SNG) synthesis technologies: water electrolysis, methanation, CO 2 scrubbing from air, gas storage, and both combined and open cycle gas turbines (CCGT, OCGT). The synchronization of the operation of SNG synthesis technologies are important once the model does not include hydrogen and CO 2 storage. A 48-hour biogas buffer storage allows part of the biogas to be upgraded to biomethane and injected into the gas storage.

The energy sector bridging technologies provide more flexibility to the entire energy system, thus reducing the overall cost. One bridging technology available in the model is PtG technology for the case that the produced gas is consumed in the industrial sector and not as a storage option for the electricity sector. The second bridging technology is seawater reverse osmosis (SWRO) desalination, which couples the water sector to the electricity sector.

For electricity transmission most transmission lines are based on high voltage alternating current (HVAC) technology. However, for better efficiency over very long distances high voltage direct current (HVDC) technology are usually used. Alternating current (AC) grids within the sub-regions exist but are beyond of the methodological options of the current model since grid costs and distribution data are not accessible for the entire region and, therefore, would implicate a bad estimation on the respective costs and grid distribution. However, for inter-regional electricity transmission, HVDC grids are modeled. Power losses in the HVDC grids consist of two major components: length dependent electricity losses of the power lines and losses in the converter stations at the interconnection with the AC grid.

An energy system mainly based on RE and in particular intermittent solar PV and wind energy requires different types of flexibility for an overall balanced and cost optimized energy mix. The four major categories are generation management (e.g. hydro dams or biomass plants), demand side management (e.g. PtG, SWRO desalination), storage of energy at one location and energy shifted in time (e.g. batteries), and transmission grids connecting different locations and energy shifted in location (e.g. HVDC transmission).

The full model block diagram is presented in Fig 2 .

https://doi.org/10.1371/journal.pone.0173820.g002

Scenario assumptions

Regions subdivision and grid structure..

The South America region and also Central American countries that connect South America to North America (Panama, Costa Rica, Nicaragua, Honduras, El Salvador, Guatemala and Belize) were considered in this study. The super region was divided into 15 sub-regions: Central America (that accounts for Panama, Costa Rica, Nicaragua, Honduras, El Salvador, Guatemala and Belize), Colombia, Venezuela (that accounts for Venezuela, Guyana, French Guiana, Suriname), Ecuador, Peru, Central South America (that accounts for Bolivia and Paraguay), Brazil South, Brazil São Paulo, Brazil Southeast, Brazil North, Brazil Northeast, Argentina Northeast (includes Uruguay), Argentina East, Argentina West and Chile. Brazil and Argentina, which are the biggest countries in population and territory, were divided into five and three sub-regions respectively, according to area, population and national grid connections.

In this paper four scenarios for energy system development options are discussed:

• regional energy systems, in which all the regions are independent (no HVDC grid interconnections) and the electricity demand has to be covered by the respective region’s own generation;
• country-wide energy system, in which the regional energy systems are interconnected by HVDC grids within the borders of nations;
• area-wide energy system, in which the country-based energy systems are interconnected;
• integrated scenario: area-wide energy system scenario with SWRO desalination and industrial natural gas demand. In this scenario, RE sources combined with PtG technology are used not only as electricity generation and storage options within the system, but also as energy sector bridging technologies to cover water desalination and industrial gas demand, increasing the flexibility of the system.

Fig 3 presents the South and Central American region’s subdivision and grid configuration. HVDC interconnections for energy systems of the countries are shown by dashed lines. The structure of HVDC grid is based on existing configuration of South and Central American grids.

https://doi.org/10.1371/journal.pone.0173820.g003

Financial and technical assumptions.

The model optimization is performed in a technological and financial status for the year 2030 in a currency value of the year 2015. The overnight building approach as typically applied for nuclear energy [ 38 ] was considered. The financial assumptions for capital expenditures (capex), operational expenditures (opex) and lifetimes of all components, for all the considered scenarios, are provided in Table A in S1 File . Weighted average cost of capital (WACC) is set to 7% for all scenarios, but for residential PV self-consumption WACC is set to 4%, due to lower financial return requirements. The technical assumptions concerning power to energy ratios for storage technologies, efficiency numbers for generation and storage technologies, and power losses in HVDC power lines and converters are provided in Tables A, B and C in S1 File . Since the model calculates electricity generated by prosumers, electricity prices for residential, commercial and industrial consumers in most of the region countries for the year 2030 are needed, being taken from [ 39 ] except for Ecuador, Suriname, Venezuela, Guyana and French Guiana, whose electricity prices are taken from local sources. Prices are provided in Table E in S1 File . As the electricity price is on a country basis, the sub-regions’ electricity prices in Brazil and Argentina have the same value. The production and consumption of electricity by prosumers are not simultaneous and, consequently, prosumers cannot self-consume all electricity generated by their solar PV system. The excess electricity produced by prosumers is assumed to be fed into the grid for a transfer selling price of 2 €cents/kWh. Prosumers cannot sell to the grid more power than their own annual consumption.

Feed-in profiles for solar and wind energy.

The feed-in profiles for solar CSP, optimally tilted and single-axis tracking PV, and wind energy were calculated according to [ 23 ] and [ 27 ]. Fig 4 presents the aggregated profiles of solar PV generation (optimally tilted and single-axis tracking), wind energy power generation and CSP solar field. The profiles are normalized to maximum capacity averaged for South America. A table with the computed average full load hours (FLH) is provided in Table F in S1 File .

https://doi.org/10.1371/journal.pone.0173820.g004

The feed-in values for hydropower are calculated based on the monthly resolved precipitation data for the year 2005 as a normalized sum of precipitation in the regions. Such an estimate leads to a good approximation of the annual generation of hydropower plants, as described previously in [ 23 ].

Biomass and geothermal heat potentials.

For biomass and waste resource potentials, data is taken from [ 13 ] and classified as described in [ 23 ]. Costs for biomass are calculated using data from the International Energy Agency [ 40 ] and Intergovernmental Panel on Climate Change [ 41 ]. For solid wastes a 75 €/ton gate fee for incineration is assumed. Calculated solid biomass, biogas, solid waste and geothermal heat potentials and prices for biomass fuels are provided in Tables G and H in S1 File . Price differences between countries are because of different waste and residue component shares. Heating values are based on lower heating values (LHV).

For regional geothermal heat potentials the calculations are based on spatial data for available heat, temperature and geothermal plants for depths from 1 km to 10 km. Geothermal heat is used only for electricity generation in the model. For each 0.45°x0.45° area and depth, geothermal LCOE is calculated and optimal well depth is determined. It is assumed that only 25% of available heat will be utilized as an upper resource limit. The total available heat for the region is calculated using the same weighed average formula as for solar and wind feed-in explained in [ 23 ], except for the fact that areas with geothermal LCOE exceeding 100 €/MWh are excluded.

Upper and lower limitations on installed capacities.

Lower and upper limits calculations are described in [ 23 ]. Lower limits on already installed capacities in South and Central American sub-regions are provided in Table I in S1 File and all upper limits of installable capacities in South and Central American sub-regions are summarized in Table J in S1 File . For other technologies, upper limits are not specified unless for biomass residues, biogas and waste, for which it is assumed that the available and specified amount of the fuel can be used during the year.

The demand profiles for sub-regions are calculated using a synthetic algorithm, calibrated according to previous load curves for Argentina, Brazil and Chile [ 42 ]. The data is in hourly resolution for the year 2015. It is computed as a fraction of the total country energy demand based on load data weighted by the sub-regions’ population. Fig 5 represents the area-aggregated demand of all sub-regions in South and Central America. The increase in electricity demand by year 2030 is estimated using IEA data [ 1 ] and local data. Solar PV self-consumption prosumers have a significant impact on the residual load demand in the energy system as depicted in Fig 5 (right). The overall electricity demand and the peak load are reduced by 22.8% and 15.0%, respectively, due to prosumers.

https://doi.org/10.1371/journal.pone.0173820.g005

Industrial gas demand values (gas demand excluding electricity generation and residential sectors) and desalinated water demand for South and Central American sub-regions are presented in Table K in S1 File . Gas demand values are taken from IEA data [ 37 ] and desalination demand numbers are based on water stress and water consumption projection [ 36 ].

Main findings on the optimized energy system structure and costs

As the main results, cost minimized electrical energy system configurations are derived for the given constraints for all the studied scenarios. The configurations are also characterized by optimized installed capacities of RE electricity generation, storage and transmission for every modelled technology and hourly electricity generation, storage charging and discharging, electricity export, import, and curtailment are calculated. In order to determine whether or not the project is interesting compared to other similar project’s average rates, the average financial results of the different scenarios for the total system (including PV self-consumption and the centralized system) are expressed as levelized costs. The levelized costs used are: levelized cost of electricity (LCOE), levelized cost of electricity for primary generation (LCOE primary), levelized cost of curtailment (LCOC), levelized cost of storage (LCOS) and levelized cost of transmission (LCOT). All levelized costs, total annualized cost, total capital expenditures, total renewables capacity and total primary generation for South and Central America region are presented in Table 1 .

https://doi.org/10.1371/journal.pone.0173820.t001

In Table 1 the importance of HVDC transmission lines in 100% RE systems is clear: it leads to a significant reduction in RE installed capacities, electricity cost, annual expenditures for the system and storage costs; electricity cost of the entire system in the case of area-wide open trade power transmission decreases by 4.4% and 8.7% compared to the country-wide and region-wide scenarios, respectively. Grid utilization decreases the primary energy installed conversion capacities by 7.3% and 13.5% in reference to country-wide and region-wide scenarios, respectively, and reduces storage utilization, according to Table 2 . Cost of transmission is relatively small in comparison to the decrease in primary generation and storage costs. Curtailment costs are reduced by 40.9% and 56.7% in the area-wide scenario compared to the country-wide and region-wide scenarios, respectively, decreasing more significantly than storage costs in the case of broader grid utilization; however, the impact of excess energy on total cost is rather low.

https://doi.org/10.1371/journal.pone.0173820.t002

A further decrease in LCOE of 17.5% compared to the area-wide open trade scenario can be reached by the integration of water desalination and industrial gas sectors. This cost reduction is mainly explained by a reduction of storage cost by 35% since industrial gas and desalination sectors decrease the need for long-term storage utilization, giving additional flexibility to the system through demand management. An 11% decrease in primary electricity generation cost can be noticed as well and is explained by an increase in the flexibility of the system and the utilization of low-cost wind and solar electricity as can be seen in Table 2 . For biogas, a fraction of 24% of the biogas used in biogas power plants in the area wide-open trade scenario is re-allocated from the electricity sector to the industrial gas demand for efficiency reasons. The sub-region Brazil Northeast has a peculiarity that has to be highlighted in the integrated scenario: 26.8 TWh of its industrial gas demand is supplied only by biogas plants and no PtG is needed (Table K in S1 File . The numeric values for LCOE components in all sub-regions and scenarios are summarized in Table N in S1 File .

Concerning RE installed capacities, all the RE technologies present a reduction of total installed capacity with an increase of grid utilization ( Table 2 ); solar PV technologies have the highest GW installed capacity in all the analyzed scenarios, accounting for 61%, 62%, 60% and 71% of the total installed capacity in region-wide, country-wide, area-wide and integrated scenarios, respectively. The high share of solar PV can be explained by the fact that this is the least cost RE source for the region as a whole, as a consequence of assuming a fast cost reduction of solar PV and battery storage in the next fifteen years [ 43 , 44 ]. Furthermore, the area-wide open trade scenario leads to 64% of solar PV total installed capacity being provided by PV prosumers as a result of prosumer LCOE competitiveness all over the region.

A PV self-consumption overview is given in Table L in S1 File . Self-generation plays a crucial role in 100% RE power systems for South and Central America due to rather high electricity prices throughout South and Central America and low self-consumption LCOE. Self-generation covers 99.3% of residential prosumers’ demand, 91.6% and 92% of demand for commercial and industrial prosumers.

Despite the fact that an upper limit 50% higher than the current capacity was considered for hydro dams and hydro RoR plants, the total hydropower plants’ installed capacity practically did not change considering all the studied scenarios: PV and wind seemed to be more profitable technologies according to the availability of the regions’ resources.

For energy storage options, transmission lines decrease the need for storage technologies, since energy shifted in time (storage) can be partly cost effectively substituted by energy shift in location; total installed capacities of batteries, PHS, A-CAES, PtG and gas turbines decrease with the grid expansion. PtG electrolyzers have a rather low installed capacity in the region-wide and country-wide scenarios and for the area-wide scenario, PtG is not needed for seasonal storage. On the other hand, hydro dams have a key role as virtual batteries for solar and wind long-term balancing, reducing interregional electricity trade and electricity transmission costs.

Concerning water desalination need, although the South American region has high water availability and rainfall, regions such as Chile, the western part of Argentina and Venezuela, shall present a need for water desalination by 2030 according to water stress calculations (Table K in S1 File .

An overview of the electricity generation curves for the area-wide scenario can be seen in Fig 6 . All 8760 hours of the year are sorted according to the generation minus the load, which is represented by the black line. A higher electricity generation than demand can be observed for 3500 hours of the year, which is used for charging storage. This is caused by a high electricity generation from inflexible energy sources, due to high shares of solar PV and wind energy in the South and Central American energy mix, and a higher solar irradiation and wind speed in the region during these hours of the year. As a consequence, flexible electricity generation options (such as hydro dams, biomass and biogas) and discharge of storage plants are needed. On the other hand, during the other hours of the year, the inflexible electricity generation reduces significantly in comparison to the decrease in electricity demand, increasing the need for flexible electricity generation, energy storage discharge and grid utilization. The storage plants are operated for about 3500 hours of the year in charging mode and about 5250 hours in discharging mode. Electricity curtailment is only significant for some hundreds of hours in the year and constant during almost the entire period since the existence of HVDC transmission lines enables that sub-regions with the best RE resources to export electricity to the ones with a shortage in RE resources.

https://doi.org/10.1371/journal.pone.0173820.g006

Main findings on the optimized energy system structure in a sub-region analysis

If a sub-regional analysis is considered, as presented in Figs 7 – 9 , some differences between the scenarios, especially between the area-wide and the integrated scenarios, can be noticed. Additional demand in the case of a RE-based energy system can change the entire system structure because of shifting optimal cost structure parameters and areas being confronted with their upper resource limits. For region-wide and area-wide scenarios, solar PV dominates in almost all the sub-regions considered; for the integrated scenario, in which an additional electricity demand was included, the sub-regions that have excellent wind conditions and, therefore, low cost wind energy, have high shares of wind installed capacities in their energy mix. The shift to power in the industrial gas and desalination sectors is driven by a higher supply of least cost wind sites in sub-regions such as Central South America, Brazil Northeast, Argentina East, Argentina Northeast, Argentina West and Chile. Still considering the integrated scenario, for all other sub-regions, the increase in electricity demand system flexibility is followed by an increase in solar PV single-axis installed capacities, being in this case, the least cost RE source.

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https://doi.org/10.1371/journal.pone.0173820.g008

https://doi.org/10.1371/journal.pone.0173820.g009

The interconnected HVDC transmission grid significantly decreases total installed capacities ( Fig 7 and Table 2 ): mainly solar PV single-axis (i.e. PV single-axis installed capacities are reduced by 100% in Argentina East from region-wide to area-wide scenario) and wind turbines (i.e. wind installed capacities are decreased by 99.8% in Brazil Southeast from region-wide to area-wide scenario) for almost all the sub-regions. Some exceptions are Central South America, Brazil Northeast and Argentina West, that had an increase in 1.6%, 4.3% and 51.9%, respectively, in total RE installed capacities from region-wide to area-wide scenario. Despite a significant reduction in PV single-axis capacities (99.7%, 98.7%, 99.3%, respectively), an increase in wind capacities (486.7%, 34.8% and 244.4%, respectively) was observed due to excellent wind energy conditions in the respective sub-regions. The structure of HVDC power lines and utilized RE resources strongly influence the total storage capacity needed. In this context, the already installed hydro dams are an important RE source that can act as virtual batteries for long-term storage. Data of storage systems’ discharge capacities, energy throughput and full load cycles per year are summarized in Table M in S1 File . The generation capacities of storage technologies decrease with integration of the HVDC grid. However, for the integrated scenario capacities of storage technologies increase in absolute numbers. State-of-charge profiles for the area-wide scenario for battery, PHS, A-CAES and gas storages and hydro dams are provided in Fig E and F in S2 File . The state-of-charge diagrams show the system optimized operation mode of the different storage technologies: mainly daily (battery, PHS), mainly weekly (A-CAES) and mainly seasonal (gas, hydro dams).

Electricity import/export

For the region-wide open trade scenario, all sub-regions of South and Central America need to match their demand using only their own RE resources. Nonetheless, in the case of the country-wide and area-wide open trade scenarios, a division of sub-regions into net exporters and net importers with interregional electricity flows can be observed ( Fig 10 ). Net exporters are sub-regions with the best renewable resources and net importers are sub-regions with moderate ones. Due to export and import, there are differences in generation and demand but in a minor quantity also due to storage losses. For the area-wide integrated scenario (not shown in Fig 10 ) the differences are mainly due to energy consumption for SNG production.

https://doi.org/10.1371/journal.pone.0173820.g010

Fig 10 reveals the net exporter sub-regions: Central South America, Brazil North, Brazil Northeast, Argentina West and Chile. Net importers are Peru, Argentina Northeast, Argentina East and Brazil Southeast. The remaining sub-regions are classified as balancing sub-regions since electricity is both imported and exported during the day and throughout the year. Hourly resolved profiles for regional generation in an importer sub-region (Argentina Northeast), balancing sub-region (Central America) and exporting region (Brazil North) are presented in Fig A, B and C in S2 File , respectively). Considering the integrated scenario, SNG producing regions tend to increase the intra-regional electricity generation to fulfill the increased demand for the desalination and SNG producing sectors what would change the picture remarkably.

The import/export shares in all regions and scenarios are summarized in Table N in S1 File . The share of export is defined as the ratio of net exported electricity to the generated primary electricity of a sub-region and the share of import is defined as the ratio of imported electricity to the electricity demand. The area average is composed of sub-regions’ values weighted by the electricity demand.

Concerning interregional electricity flows between the sub-regions, Fig 11 shows that electricity trade increases during the night and first morning hours all throughout the year and decreases during the same daily period in the winter time. This tendency can be explained by the fact that high shares of solar PV electricity generation requires that during the night, not only storage technologies are used but also electricity is imported by sub-regions with higher inflexible electricity generation. In this case, the electricity flow is directed from sub-regions with high hydropower generation, such as Brazil North and Central South America, to regions with high solar PV generation, such as Venezuela and Peru. During the winter time the electricity demand decreases and, consequently, the need for electricity trade. An overview of the power transmission lines, the key parameters and the percentage of grid utilization can be found in Table O in S1 File .

https://doi.org/10.1371/journal.pone.0173820.g011

Energy flow for 100% RE power systems for South and Central America

An energy flow diagram is capable of showing the breakdown of energy production, utilization and losses according to each technology and sector. The energy flow for the integrated system is presented in Fig 12 ; diagrams for the region-wide and area-wide scenarios are presented in Fig F in S2 File .

https://doi.org/10.1371/journal.pone.0173820.g012

The flows are comprised of primary RE resource generators, energy storage technologies, HVDC transmission grids, total demand of each sector and system losses. Potentially usable heat and ultimate system losses consist of the difference of primary power generation and final electricity demand. Both are comprised of curtailed electricity; heat produced by biomass, biogas and waste-to-energy power plants; heat of transforming power-to-hydrogen in the electrolyzers, hydrogen-to-methane in methanation and methane-to-power in the gas turbines; and the efficiency losses in A-CAES, PHS, battery storage, as well as by the HVDC transmission grid.

Power system costs for the studied scenarios

From the presented results for the South and Central America region, and from the results presented in [ 23 ] and [ 27 ] for the Northeast Asian region, it can be concluded that different levels of grid development lead to different power system configurations and costs. The installation of an HVDC transmission grid between sub-regions enables a significant decrease not only in the cost of electricity but also in RE and storage installed capacities in the RE-based system. The total levelized cost of electricity in the region decreased from 61.9 €/MWh for the region-wide open trade scenario to 59.1 €/MWh for the country-wide open trade scenario and 56.5 €/MWh for the area-wide open trade scenario. The total annualized cost of the system decreased from 115 b€ for the region-wide open trade scenario to 104 b€ for the area-wide open trade scenario. In parallel the capex requirements are reduced from 948 b€ for the region-wide open trade to 912 b€ and 889 b€ for the country-wide and area-wide open trade scenarios, respectively. Additional costs of HVDC transmission lines (56 b€ annual cost for area-wide scenario) are compensated by a substantial decrease in generation and storage capacities enabled by lower losses and costs of energy transmission compared to energy storage, and access to low cost electricity generation in other regions. The HVDC transmission grid may not increase the chances to supply electricity to rural people that do not yet have access to electricity nowadays in South and Central America regions. However, RE-based mini-grid solutions and solar home systems may be a proper solution in addition to grid extension [ 45 , 46 , 47 ].

The role and influence of PV technologies on 100% RE system for South and Central America by 2030

PV technologies have the highest share in installed capacities for a 100% RE energy mix in all the analyzed scenarios, which is in accordance with the fact that these technologies have well distributed FLH all over the sub-regions and are the least cost RE technology in most of the cases. Besides, the installation of distributed small-scale and centralized PV plants is already profitable in numerous regions in the word and PV electricity generation cost are set to decrease even more in the coming years [ 48 , 49 ], especially in regions with high PV FLH.

In addition, PV self-consumption has to be analyzed in more detail since prosumers’ electricity generation provokes some positive and negative distortion in the system demand profile ( Fig 5 ) and costs [ 23 ]. In order to measure the influences of PV prosumers, region-wide, country-wide and area-wide open trade scenarios are also calculated without PV self-consumption and the total demand is assumed to be covered by a more centralized system. The annualized costs for the more centralized 100% RE system are 12.2% lower for the region-wide scenario (101 b€ against 115 b€ base scenario), 12.8% lower for the country-wide scenario and 13.5% lower for the area-wide open trade scenario for the RE system without PV self-consumption. This result is explained by the fact that PV self-consumption provokes additional costs because of a different target function of prosumers. Prosumers will install PV systems, if LCOE of PV self-consumption is lower than the grid electricity selling price. However, LCOE of PV self-consumption can be higher than the total system LCOE. Consequently, the system reacts by installing more flexibility granting capacities, such as low cost RE or further storage capacities, which increase the system costs as well., As in South and Central America there are only slight differences in electricity consumption during the whole year, the peak, minimum and average load, and total remaining electricity demand in the system are significantly decreased by 15–23% due to PV prosumers’ electricity production. Thus, the most expensive peak hours throughout the year are substantially reduced by about 15% by PV self-consumption, which exhibits a substantial economic value. The electricity consumption in the centralized system was higher in the first morning hours and during the evenings, and with PV prosumers influence, there was a lower electricity consumption during the afternoon. For the region-wide scenario a comparable low cost increase due to the decentralized generation can be explained by the fact that additional disturbance cost in the system (provoked by prosumers) is compensated by access to low cost residential electricity (for residential consumers WACC is assumed to be 4%). Finally, PV self-consumption is in particular valuable in area constrained regions, since zero impact areas on rooftops can be utilized for local electricity generation, which in turn reduces the requirement of imports. This may be in some regions a policy option for reaching higher local value creation and less supply risk due to higher electricity imports.

Advantages of the system’s flexibility

The integrated scenario is the scenario in which water desalination and industrial gas sectors are integrated into the power sector. The integration can be considered for the reason that both new integrated sectors require only electricity to cover projected natural gas demand (except the gas demand for power generation and residential purposes that are not considered in this study) and renewable water demand by SNG generation and SWRO desalination, respectively. In parallel with supplying demand, such an integration gives the system additional flexibility, especially for seasonal fluctuation compensation. Variable PtG and desalination plants enable the production of synthetic gas and water during periods of excess electricity, reducing LCOG and LCOW. Recent SWRO desalination plants, for instance, such as the Hadera plant in Israel and Al Khafji in Saudi Arabia have been designed to work on variable power input [ 35 , 50 ]. Al-Nory and El-Beltagy (2014) also discuss the variable operation of desalination plants depending on the availability of renewable energy in the grid. In 100% RE systems, generation and supply management and grid integration are very important tools that diminish curtailed electricity, integrate other sectors to the power sector and connect RE plants across a wide geographical area complementing their resources. The availability of RE in South and Central America is sufficient to cover additional electricity demand for producing 640 TWh LHV of SNG and 3.9 billion m 3 of renewable water. Adding 967 TWh el for gas synthesis and SWRO desalination induces an additional installation of RE generation capacities of 410 GW of PV and about 66 GW of wind energy. As well, former long-term gas storage is partly substituted by short-term battery storage. Next, there is a significant increase in electrolyzer units of about 131 GW and substantially reduced gas turbines.

The integration benefit for the electricity, water and industrial gas sectors is estimated to be about 13.1 b€ of the annual system cost. An additional decrease in the electricity demand by 167 TWh and the curtailed electricity by 23 TWh can be observed also. These benefits are of 8%, 5% and 23%, respectively, compared to the non-integrated, separate systems. Further, the cost of renewable water seems to be quite affordable at 0.91 €/m 3 , and the cost of electricity decreases by 18% to 46 €/MWh for the integrated scenario compared to the area-wide open trade scenario without sector integration. However, the cost of synthetic gas, at 95.1 €/MWh LHV , appears to be significantly higher than the current price.

Other alternatives for achieving a low carbon based energy system

The conclusions for this study clearly show the potential of the region for RE generation and for a global climate change mitigation strategy. The results of a fairly low LCOE for the year 2030 (in all the considered scenarios) added to the already existing RE policies and low carbon development plans can boost the development of a renewable power system in the South and Central American region in the coming years. Among the alternatives for achieving a low carbon based energy system, non-renewable options, such as nuclear energy, natural gas and coal carbon capture and storage (CCS) have been also highlighted [ 51 ]. The LCOE of the alternatives are as follows [ 51 ]: 112 €/MWh for new nuclear (assumed for 2023 in the UK and Czech Republic), 112 €/MWh for gas CCS (assumed for 2019 in the UK) and 126 €/MWh for coal CCS (assumed for 2019 in the UK). However, a report published by [ 52 ] concludes that CCS technology is not likely to be commercially available before the year 2030 [ 23 ]. In the mid-term, the findings for Europe can be also assumed for South and Central America. These other alternatives have still further disadvantages such as nuclear melt-down risk, nuclear terrorism risk, unsolved nuclear waste disposal, remaining CO 2 emissions of power plants with CCS technology, a diminishing conventional energy resource base and high health cost due to heavy metal emissions of coal fired power plants. Moreover, the 100% renewable resource-based energy system options for South and Central America presented in this work seem to be considerably lower in cost (about 45–63%) than the other alternatives.

Comparison to a business as usual scenario

A comparison to a business as usual scenario (BAU) is important in order to check, if the estimated LCOE for the 100% RE system is lower than the LCOE of a system based on current policies. In order to do so, a BAU scenario based on the current policy scenario [ 1 ] was analyzed and its LCOE was calculated for the year 2030. The mix of installed capacities for the BAU scenario is displayed in Table 3 .

https://doi.org/10.1371/journal.pone.0173820.t003

Due to the fact that the BAU scenario considers fossil fuel power plants, two different values for LCOE were calculated for this scenario: one that does not take into account CO 2 emission costs (LCOE BAU ) and another that considers a 59.8 €/tCO 2 [ 53 ] emission cost (LCOE BAU-CO2 ). An overnight approach was also assumed and transmission costs were not included since AC grids costs and distribution are not available in the literature. Therefore, for comparing LCOE values for BAU and 100% RE scenarios, LCOT was excluded from total LCOE for country and area-wide scenarios.

Fig 13 shows the result for LCOE BAU and LCOE BAU-CO2 in comparison to LCOE of 100% RE scenarios. The calculated LCOE BAU and LCOE BAU-CO2 values are 67.2 €/MWh el and 77.0 €/MWh el , respectively. Comparing LCOE BAU to LCOE for 100% RE scenarios, the values are at least 9 and at most 16% lower, what shows that even under no CO 2 emission taxes policy, a 100% RE power system is the least cost solution for the increase in the region’s electricity demand by 2030. In addition, if CO 2 emission costs are considered, these percentages are even higher ranging from 24 to 44% as shown on Fig 13 . Although the discussion of other costs and benefits (such as decrease of air pollution, increase in health and quality of life, minimal impact on the environment and economic benefits to regional areas) are not the scope of this paper, it is important to mention that if these costs and benefits are included in the calculations, they would increase even more LCOE of BAU scenarios and decrease LCOE of 100% RE scenarios. As pointed out by [ 54 ], [ 55 ] and [ 56 ] these costs are very high and further substantially increase the real societal costs of current conventional energy systems and have to be regarded as societal very harmful subsidies. For this reason, it is essential to reinforce the importance of a new policy scenario and of the development of RE technologies in South and Central America.

Comparison of LCOE BAU , to country-wide, region-wide and area-wide scenarios (top). Comparison of LCOE BAU-CO2 to country-wide, region-wide and area-wide scenarios (bottom).

https://doi.org/10.1371/journal.pone.0173820.g013

Limitations of the considered model

The model presented in this article presents some limitations that had to be mentioned and analysed. Most of them come from future financial, technical, demographic and economic development assumptions and from technological change and climate policies that were not considered. The key limitations are listed below:

• Since financial and technical assumptions for the renewable energy and storage technologies are global in nature, differences between the assumptions for countries and regions of the same country are not considered. Therefore, divergences in total cost and mix of capacities may be found if a further study that takes into account different assumptions for South and Central American countries is considered.
• Biomass potentials used in this study do not consider differences in biomass availability during the year, i.e. it does not consider that straw and bagasse from sugarcane refineries are available only during the harvest season. This may lead to an overestimation of biomass power plant installed capacities and an underestimation of storage capacities in the current study.
• It was considered that PV prosumers’ surplus electricity production can be fed into the grid for a transfer selling price of 2 €cents/kWh. However, this is a broader estimation in order to calculate the benefits from the selling of excess energy by prosumers and does not consider that each South and Central American country has its own policy (feed-in-tariff or RE auctions) and selling regulation and price.
• Grid interconnections are based on each country’s current national grid although the model’s sub-regional division does not allow that the modelled system accurately represents the current system. In addition, most of the grid interconnection between countries do not yet exist, and were considered in order to show the benefits from grid integration in energy systems with high shares of RE. AC transmission lines were not modelled due to: the lack of information on this data for the entire region; a considerable increase in the computational cost that could unfeasible the model.
• The overnight approach can increase the LCOE of primary generation in regions with already existing high shares of hydropower plants, such as Brazil South. This fact can considerably change the total mix of capacity of the whole region if another approach that considered already existing power plants and future power plants were regarded.

RE technologies can generate enough energy to cover all electricity demand in South and Central America for the year 2030 on a price level of 47–62 €/MWh el , depending on geographical and sectoral integration. The electricity needed to cover PtG technology and SWRO desalination demand can be produced by RE sources as well, providing the region with 100% renewable synthetic natural gas and clean water supply. However, due to high cost obtained for the synthetic gas, government regulation and/or subsidies might be needed to ensure the financial viability of this synthetic fuel, as part of a comprehensive net zero emission strategy.

Due to the need to diminish the dependency of the South and Central American power sector on a changing hydrological profile, different shares of variable RE technologies are essential for 100% RE-based power systems in the region. This need has been an urgent issue for many countries within the region in recent years. In the cost minimized design of the energy mix presented in this study, hydropower continues to dominate in the electricity sector (in terms of TWh of electricity production) in most sub-regions of South and Central America. Nonetheless, the vulnerability of the existing power system is solved by a high share of complementary renewable sources, leading to the least-cost solution for the problem under the given constraints.

For all the studied scenarios solar PV technology emerged as the main energy supply (in terms of GW of installed capacities) in most of the sub-regions; however, with the integration of industrial natural gas and water desalination sectors, the role of PV decreases in sub-regions where wind turbines offer the least cost technology. The HVDC transmission grid plays a key role within the renewable resource-based energy system since the established Super Grid enables a significant cost decrease, a cut-off of storage utilization, and a significant reduction of primary generation capacities. Meanwhile, PV self-consumption induces a moderate increase in total electricity costs of 12–14%. This is due to the fact that consumers tend to utilize higher cost level solar energy and the excess electricity from prosumer generation provokes additional disturbances in the system. In turn, this increases the system need for flexibility.

For the integrated scenario it was found that industrial SNG generation displaces SNG storage as seasonal storage for the electricity sector. Instead of gas turbine utilization in case of an energy deficit, the system curtails the SNG generation in that system set-up as a major source of flexibility to the system.

A fully integrated renewable energy system has to be simulated and deeply studied in order to better understand the findings for the South and Central American region. However, compared to a BAU scenario based on current policies, this research work indicates that a 100% renewable resources-based energy system is a real economic, environmental and health low cost option and is a very important indicator that should be taken into account by policymakers for the development of future policies.

Supporting information

Table A: Financial assumptions for energy system components [ 36 , 49 , 57 , 58 , 59 , 60 , 61 , 62 ,]. Table B: Efficiencies and energy to power ratio of storage technologies. Assumptions are mainly taken from [ 61 ]. Table C: Efficiency assumptions for energy system components for the 2030 reference years. Assumptions are mainly taken from [ 59 ] and from [ 61 ]. Table D: Efficiency assumptions for HVDC transmission [ 63 ]. Table E: Regional end-user grid electricity costs for year 2030. Assumptions for most of the countries were taken from [ 39 ]. Table F: Average full load hours and LCOE for optimally tilted and single-axis tracking PV systems, and wind power plants in Central and South American regions. Abbreviation: full load hour, FLH . Table G: Regional biomass [ 13 ] and geothermal energy potentials. Table H: Regional biomass costs, calculated based on biomass sources mix in the region. Solid wastes cost are based on assumption of 75 €/ton gate fee paid to the MSW incinerator. Table I: Lower limits of installed capacities in South and Central American regions. Data were taken from [ 3 ]. Table J: Upper limits on installable capacities in South and Central America regions in units of GW th for CSP and GW el for all other technologies. Table K: Annual industrial gas [ 37 , 1 ] and water demand [ 36 ] for year 2030. Table L: Overview on prosumers electricity costs installed capacities and energy utilization for South and Central America.Table M: Overview on storage capacities, throughput and full cycles per year for the four scenarios for South and Central America. Table N: Total LCOE components in all sub-regions. Table O: Overview on electricity transmission lines parameters for the area-wide open trade scenario.

https://doi.org/10.1371/journal.pone.0173820.s001

Fig A. Hourly generation profile for Argentina Northeast, example for an importing region. Fig B. Hourly generation profile for Central America, example for a balancing region. Fig C. Hourly generation profile for Brazil North, example for an exporting region. Fig D. Aggregated state-of-charge for the storages in the integrated scenario: battery (top left), PHS (top right), A-CAES (bottom left) and gas storage (bottom right). Fig E. State-of-charge for hydro dams in the integrated scenario. During the rainy season, the water levels in the reservoirs are above 60% of the reservoir storage capacities. Fig F. Energy flow of the system for the region-wide open trade (top) and area-wide open trade (bottom) scenarios.

https://doi.org/10.1371/journal.pone.0173820.s002

Acknowledgments

The authors would like to thank Svetlana Afanasyeva, Arman Aghahosseini, Javier Farfan and Michael Child for helpful support.

Author Contributions

• Conceptualization: CB PV.
• Data curation: DB LSNSB.
• Formal analysis: LSNSB.
• Funding acquisition: LSNSB PV CB.
• Investigation: LSNSB CB.
• Methodology: DB CB.
• Project administration: CB PV.
• Resources: CB PV.
• Software: DB.
• Supervision: CB PV.
• Validation: DB LSNSB.
• Visualization: DB LSNSB CB PV.
• Writing – original draft: LSNSB.
• Writing – review & editing: LSNSB CB PV.
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