Regional coordination can alleviate the cost burden of a low-carbon electricity system

Regional coordination can alleviate the cost burden of a low-carbon electricity system | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Regional coordination can alleviate the cost burden of a low-carbon electricity system Jacob Wessel, AFM Kamal Chowdhury, Thomas Wild, Franklyn Kanyako, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6505314/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Oct, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract Long-term planning of low-carbon electricity systems in the Global South involves deeply uncertain infrastructure investments, often undertaken independently from neighboring energy systems. In principle, national grids benefit from greater regional integration, but the nature of these benefits is sensitive to techno-economic uncertainty and natural resource distribution. We examine the value of regional electricity coordination for a South American subregion with abundant, geographically variable renewable resources, under stringent emissions reduction targets and a range of techno-economic assumptions. Results show decarbonization is achievable with modest cost premiums which are further mitigated by international coordination. Differences in renewables deployment across scenarios tend to offset system-wide; however, country-level variability suggests national decarbonization pathways are sensitive to technology characteristics. Achieving mitigation goals without coordination requires additional generation capacity, at more than triple the added cost of coordinated planning scenarios ( $ 14.7-22.8B vs. $ 3.5-7.0B). Beyond South America, these results are relevant to regions looking to meet emissions targets through greater international cooperation. Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigation Physical sciences/Energy science and technology/Energy modelling Scientific community and society/Energy and society/Energy supply and demand Physical sciences/Engineering/Energy infrastructure/Energy grids and networks decarbonization capacity expansion modeling electricity trade techno-economic uncertainty regional electricity coordination Global South transmission renewable energy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Highlights • Investing in transmission can provide three-fold savings in capacity requirements • Coordination can hedge against technological uncertainty in the energy transition • Regional grid planning can increase energy interdependence, especially for importers • Winners and losers can emerge differently across scenarios with similar net benefits Plain Language Summary The supply of electricity to the grid from renewable sources like wind and solar can vary substantially by geographical region. Grid planners must decide on the best combination of resources to serve an area’s electricity needs subject to many competing objectives, such as cost, reliability, security, and climate mitigation; they then make long-term capital-intensive infrastructure investments to meet these goals. However, many countries plan their electricity grids independently from their neighbors, rather than coordinating investments and trading electricity. These issues are particularly salient in the Global South. We explore the potential for a region in South America to lower electricity costs in the future through expanding international transmission lines and co-optimizing power plant investments, which allows broader access to the region’s geographically dispersed renewable resources. Our results suggest that this grid integration can reduce planning uncertainty and is more economical region-wide, but may benefit (or burden) individual countries differently. 1. Introduction Transitioning to a low-carbon future is likely to strain bulk electric power systems, especially when paired with increasing demand 1 ; nevertheless, decarbonizing electricity is one of several key pillars for achieving global sustainable development objectives, such as limiting climate change and its impacts 2 . System planners in countries overseeing such a transition must make long-term infrastructure planning decisions in the face of uncertainty about the future 3 , 4 . Navigating national decarbonization pathways will require robust strategies to utilize diverse, intermittent, and unevenly distributed resources 5 . In many developing economies, achieving emissions reductions while balancing cost, reliability, and energy security will continue to be a critical challenge. At the same time, large-scale (international to global) decarbonization efforts will hinge on more fragmented national- and subnational-scale infrastructure investments and decisions 6 – 8 . In many regions, neighboring electricity systems have natural complementarities in renewable (namely, wind and solar) and hydropower potential, built infrastructure, and demand patterns 9 , 10 . In some cases, such differences can emerge as competitive advantages. Often, especially in the developing world, there is opportunity for greater regional coordination of electricity grid planning and operation, with the potential for economic, environmental, and societal co-benefits 10 (see Text S1). To fully enable these benefits, investments in future grids should explore the value of expanding interregional transmission in addition to optimizing investments in generation capacity 11 – 13 . However, assessing the complementarity of one country’s renewable resource endowment with another’s is sensitive to numerous uncertainties, which may emerge differently across scales 14 – 17 . Adopting finer-scale spatiotemporal representations of wind and solar PV buildout, for example, can lead to larger uncertainty in distributional outcomes among individual countries, even when the aggregated system’s sensitivity to uncertainty remains small 16 , 18 – 20 ; this adds to the difficulty in providing robust planning decisions across scales 21 , 22 . Separately, uncertainty surrounding the techno-economic performance of wind and solar PV technologies can also drive future capacity expansion outcomes 23 , 24 . It is important, then, to understand how uncertainty in spatially resolved modeling inputs may impact larger-scale regional infrastructure decisions and coordinated grid outcomes 25 – 27 (see Text S2). Further, assessing the degree to which country-level burdens and benefits may be distributed within a decarbonizing regional system subject to these uncertainties is needed to justify coordination. Here, we use an open-source optimization-based capacity expansion model (GridPath 28 ) to assess the role of full regional coordination on electricity capacity expansion and deep decarbonization pathways among five countries in South America: Argentina, Brazil, Chile, Paraguay, and Uruguay (Fig. 1 ). This region is actively developing economically while simultaneously working to further decarbonize its electricity system. Its history of shared binational hydroelectric projects and regional economic integration through the Mercosur or “Southern Cone” trade bloc 29 positions these countries favorably for longer-term, coordinated electricity planning (Text S3). Historically a hydro-dominated grid, Mercosur has also adopted other forms of clean electricity to meet growing demand (and balance its hydroelectric generation) while avoiding future emissions 30 . Previous studies evaluating benefits of regional electricity coordination vary in their treatment of specific, isolated uncertainties and the level of detail in model structure 11 , 12 , 22 , 31 , 32 (see Text S1). As such, there are gaps in our understanding of how regionally coordinated grid infrastructure planning may be influenced by spatially explicit resource potentials under techno-economic uncertainty, and how the impacts will be distributed across scales. It is thus an open question how regional electricity systems can better utilize finer scale representations of resource potentials to cooperatively design robust, co-optimized generator fleet and transmission expansion pathways. We help address these gaps by exploring a broad range of scenarios to glean insights on the tradeoffs and synergies stemming from regional grid coordination under uncertainty, using a model with highly detailed spatial resolution of resource endowment. We explore an ensemble of 80 scenarios (Table 1 ) that vary across four levers: level of regional electricity coordination, mid-century emissions policy, wind turbine characteristics, and solar PV tracking type (see Methods). For the level of regional electricity coordination, we consider a Full Coordination assumption in which countries trade openly while co-optimizing international transmission investments as part of the capacity expansion problem, and a Limited Coordination assumption in which trade and transmission capacity are limited to 2020 levels. For emissions policy, we consider the Mitigation assumption in which CO 2 emissions from the electricity system are reduced 90% relative to 2020 levels, and the Reference assumption in which emissions are assumed to be unconstrained. For wind turbine characteristics and PV tracking type, we vary the hourly capacity factors and capital costs of candidate wind and solar projects throughout the region at 0.5-degree resolution – considering assumptions from ten wind turbine models and fixed vs. single-axis solar tracking. Our analysis reveals insights around the ability of coordination and electricity trade to cost-effectively insulate against deep uncertainties in the energy transition, and the emergence of substantial but potentially uneven economic benefits under a range of possible futures. The modeling and experimental setup can be adapted to any region with the potential for regional grid coordination, while the insights articulated here are particularly pertinent to groups of countries with geographically variable resource endowments and diversity in size and demand patterns. 2. Results 2.1. System costs and the benefits of coordination We start by evaluating the system-wide costs of electricity decarbonization under a range of techno-economic uncertainties. Comparing Mitigation and Reference scenarios, Fig. 2 (a) shows that across a range of technology assumptions and levels of coordination, decarbonization can be achieved with cost increases ranging from ~ 5 to 18%. Here, scenario technology assumptions in the choice of wind turbine and solar tracking drive the range of outcomes shown, and can make a significant difference in subsequent planning decisions. Full Coordination alleviates this cost burden of decarbonization (Fig. 2 (b)), while also slightly reducing the overall uncertainty in the cost premium. Figure 2 (c) disaggregates individual components of cumulative system costs, computed as differences between pairs of Mitigation scenarios which differ only by the coordination policy. These cost components are broken out from the Mitigation box in Fig. 2 (b), which in turn reflects the difference (on a cost basis) between the two boxes in Fig. 2 (a). Limiting coordination results in a net increase in total costs (sum of the individual components), primarily driven by the costs of newly installed capacity (Fig. 2 (c)). This suggests an overbuilding of new generation capacity in limited coordination scenarios compared to allowing full and open trade (Figure S1 ). This capacity requires over triple the investment than the costs of expanding transmission capacity in Full Coordination scenarios ( $ 14.7-22.8B vs. $ 3.5-7.0B). Investment in new transmission capacity enables countries to access variable renewable resources in neighboring regions, allowing for potential cost savings through more optimal placement of wind and solar projects. The reduced investment requirement for new generating capacity under Full Coordination is primarily due to the increased efficiency in grid balancing achieved by integrating a larger system of generators. Figure 3 (a) shows the effect of Full Coordination on capacity investments under Mitigation . System-wide investment in new generating capacity is lower for most technologies under most conditions, except for fossil generators built mainly in Chile (as well as wind and hydropower for a few scenarios). Figure 3 (b) shows differences in the generation mix in 2050 between Full and Limited Coordination scenarios, revealing a different trend: total wind generation tends to be substantially more under Full Coordination , even though investments in wind capacity tend to decrease. This system-wide trend comes mainly from new wind installations in Chile under Limited Coordination . These capacity additions are required to meet growing demands with zero-carbon energy and complement Chile’s diurnal solar generation, but have lower capacity factors than more productive sites in Argentina and Brazil. Even though solar PV is the dominant renewable resource in Chile, a point is reached at which the most favorable wind projects become economically viable compared with the remaining unbuilt solar PV sites (and the battery storage needed to complement them). Refer to Figures S1 -S4 for country-level outcomes. Figure 2 and Fig. 3 show that under Mitigation and Limited Coordination , clean generating capacity is overbuilt to meet the emissions policy, which also results in more curtailment of the intermittent wind and solar (see Figure S4). This is supplemented only slightly by additional investments in battery storage, nuclear, and biomass, deployed in later periods. Higher curtailment and storage deployment reflect a relative deterioration in grid efficiency and reliability, respectively, when limiting coordination; battery storage is deployed to mitigate this impact when economically viable. The temporal mismatch between intermittent generation (which increases under Mitigation ) and demand on sub-daily scales can be dampened through a more efficient utilization of resources across a wider region, and by aggregating load from individual countries exhibiting different diurnal load profiles (see Figure S5). This reduces reliance on grid-scale battery storage, decreases total curtailment, and lowers the use of more expensive peaking plants to prevent supply shortfalls (see Figure S6 and Figure S7). 2.2. Trade under generation portfolio uncertainty We find that Full Coordination facilitates a substantial increase in total bilateral electricity exchanges (Fig. 4 (a)); Mitigation scenarios experience the largest growth in all but one interconnection (Argentina-Uruguay). This consistently higher trade with increased variability suggests that trade has a role in dampening cost burdens that may arise from techno-economic uncertainty and emissions policy, by enabling more efficient adoption of renewables. In other words, Full Coordination can facilitate emissions reduction by providing protection against techno-economic uncertainty and system-wide fluctuations in costs through investment in transmission expansion (Fig. 2 and Figure S8). In the case of Argentina-Uruguay, higher trade in Reference scenarios is mainly due to fossil fuel exports from Argentina providing reliable and low-cost grid balancing for Uruguay’s intermittent renewables in a few scenarios in which the future techno-economic characteristics of wind power are unfavorable (Figure S2 ). Under Mitigation , because emissions are a binding constraint, Uruguay installs more domestic renewable capacity to meet and balance its load, rather than importing it from larger countries. Figure 4 (b,c) shows each country’s cumulative exports and imports, respectively, for the 2020–2050 horizon. As the largest country, Brazil’s high demand and capacity additions (Figure S1 ) relative to other Mercosur countries proportionally drives system-level outcomes, even as it becomes increasingly reliant on imported power. Argentina and Paraguay are net exporters, with Paraguay continuing to export hydropower to Brazil. Hydropower, though shown to increase in nominal capacity over time, decreases in share in the system’s generation mix, suggesting a shift from a hydro-dominated system to a more balanced mix of complementary clean technologies (Figure S2 ). Still, no countries switch roles from net exporter to net importer (or vice-versa) across our scenarios. Argentina plays a central role in connecting and balancing the Mercosur grid, due not only to its size, central geographical position, and wind resource potential, but also its use of natural gas in complementing the growing share of renewables across the system, even as it is phased out over time under Mitigation . Thus, in addition to a heterogeneity in resources (including bridge fuels), more interconnections to neighboring load zones could help position Argentina to benefit from providing ancillary services to a regional grid. The net exporters in this system under Mitigation and Full Coordination (Argentina and Paraguay, Fig. 4 ) experience higher total costs, due to the additional generating capacity installed to serve export demand (Figure S3). This cost increase does not reflect the revenue from the additional exports or ancillary services. Further, allocating the costs of building new transmission capacity between countries can impact relative outcomes; here, these costs are assumed to be shared equally by the two countries represented in each expanded interconnection. Additionally, increased investment in these countries for building export capacity is a favorable outcome for bringing high-quality jobs and spurring further domestic development, the value of which is left for future work to quantify. Brazil, on the other hand, must build new generating capacity mainly to meet its own demand growth, and increases reliance on imports from Paraguay and Argentina to dampen the overall cost burden from mitigation. Thus, while increased regional coordination can lower costs system-wide through grid balancing, countries capable of outpacing domestic demand growth with clean electricity deployment may capture additional economic benefits in a decarbonization policy environment. For Chile, allowing Full Coordination under Mitigation increases newly installed fossil fuel generating capacity (Figure S1 ) and its overall generation share by 2050 (Figure S2 ). This suggests that a coordinated regional emissions policy optimizing for cost may also influence the siting of new fossil-based generators throughout the system. Such a shift in where fossil-based generation occurs, even under deep decarbonization, could have negative local environmental and health impacts not captured by the model 33 . Regarding the adoption of renewables, capturing Chile’s full potential for solar PV is particularly limited by the ability to integrate its diurnal variability into the larger grid. Because Chile’s wind potential is mostly concentrated in the far south of the country far from existing transmission, its capacity expansion pathway under Mitigation relies on producing large amounts of solar during the day, while importing wind and hydropower from Argentina at night. Under Limited Coordination , Chile instead relies on battery storage to integrate its solar resources (Figure S4), and must supplement with wind capacity (Figure S1 ). Thus, through trade, Full Coordination can exploit the complementarity of diverse portfolios of renewables to smooth out seasonal and diurnal swings associated with resource intermittency (Figure S9, Figure S1 0); this in turn enables more strategic deployment of the most economic and highest quality wind and solar resources throughout the region. However, although the system is able to manage increasing levels of solar generation through Full Coordination , countries deploying substantial solar PV capacity could become dependent on transnational electricity trade for balancing diurnal intermittency, especially under Mitigation (Text S4). Additionally, without further cost declines in battery storage, we find significant amounts of solar generation could still be curtailed even under Full Coordination (Figure S4, Figure S5). 2.3. Uncertainty in renewables deployment Mercosur’s future electricity portfolio is strongly tied to the presence of an emissions reduction policy, which shapes the uncertainty surrounding the deployment of different technologies. Further, a fully coordinated system seeking to decarbonize has a more flexible set of country-level pathways to choose from in order to manage technoeconomic uncertainty. This, however, can heavily impact the optimal generator fleet at the country scale, adding to the relative uncertainty in a country’s infrastructure development pathway even while hedging against technological uncertainty system wide. Examining this uncertainty in Fig. 5 , we explore a measure of how the variability in a technology’s capacity share changes across our scenarios. We find that in almost all cases, both country-level and system-wide, Mitigation reduces deployment uncertainty associated with wind, solar PV, and hydropower (IQR ratio < 1). In other words, a deep decarbonization policy greatly narrows the range of system-wide capacity outcomes for each of these generation technologies; however, the region-wide results do not consistently reflect any one country. Fossil plants do not exhibit this same pattern (Fig. 5 (d)). Although these generators are dispatched much less under Mitigation , new reserve capacity is still built throughout the region, subject to the demands of different wind and solar PV technology characteristics. As such, system-wide capacity mix uncertainty is higher for fossil generators under Mitigation . In most cases under Full Coordination , the decarbonization policy drives more modest reductions or even increases in clean energy deployment uncertainty; the flexibility afforded by a larger interconnected system allows for a broader range of outcomes in the region’s capacity mix. One notable case is wind capacity uncertainty in Argentina (Fig. 5 (b)), due to Argentina’s central balancing role in Mercosur increasing its sensitivity to wind technology under Mitigation . Uncertainty in fossil fuel capacity under Full Coordination again shows less stability than other technologies, and country-level findings further diverge from the Limited Coordination scenarios (Fig. 5 (d)). This further illustrates how policy levers and regional coordination can cause heterogeneous subregional impacts to emerge, but that the underlying techno-economic uncertainty driving grid outcomes can also be exacerbated or ameliorated across these scales. Uncertainty in country-level capacity expansion decisions within a larger coordinated grid helps reveal how regional decarbonization pathways may evolve under uncertainty, and if key vulnerabilities or consistent burdens are experienced by particular countries. Full Coordination can allow individual countries undergoing deep decarbonization (i.e., Mitigation ) to exploit more of the broader region’s highest quality renewable resources (Figure S1 2, Figure S1 3), while utilizing trade for balancing intermittency and load patterns. For example, Chile, a country known for its excellent solar resource potential, must install some wind capacity to meet its Mitigation target under Limited Coordination , but deploys only solar PV under Full Coordination while Argentina and Paraguay invest more in wind (Figure S1 ). Regarding country-level emissions, the nature of the impacts of Full Coordination are again dependent on the presence of an emissions target (Figure S4, bottom row). Because Mitigation is implemented as a region-wide emissions cap, the emissions pathways are quite narrow when aggregated to the full system; however, individual countries show some variability. Thus, by developing cooperative strategies for the larger interconnected system, countries may be able to implement emissions reduction plans that are more cost-effective and robust to techno-economic uncertainties. At the same time, Full Coordination may not be able to spur emissions reduction on its own without further cost declines. Additional analysis in future work is needed to quantify and characterize the effects of different strategies. 3. Discussion This study contributes a regional analysis with globally generalizable insights describing the opportunity and value of grid coordination in long-term regional electricity systems planning. We construct 80 future development pathways using GridPath-Mercosur, a five-node electricity system model co-optimizing generating capacity and transmission expansion decisions for the Mercosur region of South America. This framework extends previous analyses by combining broad spatiotemporal coverage with techno-economic uncertainty in variable renewable energy to examine implications of regional coordination (or a lack thereof) under a deep decarbonization policy target. Region-wide, we find consistent and substantial benefits in planning and operating under Full Coordination , through increased electricity trade and unrestricted internodal transmission expansion. Under a deep decarbonization Mitigation target, a three-fold savings in new capacity costs is achieved through relatively modest investment in transmission expansion (Fig. 2 ). These savings are even greater when considering the further reduction in grid operating costs (which includes fuel costs). In addition to reducing the cost premium of decarbonizing, Full Coordination also decreases uncertainty in total system costs (Fig. 2 (a)). Through open electricity trade between countries (Fig. 4 , Figure S9), regional coordination provides flexibility to respond to regional advantages and disadvantages in resource utilization, which hedges against technological uncertainty in the energy transition. Future work could examine the capital cost or duration of storage technologies, to explore where the economic viability of storage competes with the benefits of coordination in facilitating a low-carbon energy transition. Further, the Mitigation target in this work represents just one possible emissions trajectory for this system; additional modeling efforts are needed to explore alternative pathways in more detail, including countries’ Nationally Determined Contributions as well as economy-wide and non-CO 2 emissions reductions. Although region-wide intermittencies and uncertainty in variable renewable energy technology may be mitigated through coordination, benefits to the region can be distributed differently among the five constituent countries, due to their relative size, advantages in natural resource availability, and modeled technology characteristics. Uncertainty surrounding the relative deployment of various generation technologies (and thus the economic development) vary across countries and policy environments (Fig. 5 ). In other words, differences in installed wind and solar PV capacities in each country tend to offset region-wide, but different countries may stand to benefit or sacrifice across scenarios. These benefits may emerge as gains in both short- and long-term employment, reduction in local air pollution, and a greater ability to attract private capital investments. Thus, when planning a Mercosur-wide electricity decarbonization pathway, balancing country-level impacts must be considered in relation to the system-wide benefits of coordination and increased trade 34 . Internationally coordinated grid planning, with diverse objectives beyond cost minimization, must navigate deep uncertainties and competing interests among stakeholders. Increasing overall electricity trade to target a smaller number of only the highest quality renewables sites relies on developing, maintaining, and using an expanded transmission network to realize the full benefits. A multinational electricity grid designed for cooperative international trade may experience notable negative effects if there are barriers to transmission expansion or transnational electricity trade (Text S4). These potential risks, including substantial levels of unmet demand, curtailment, and stranded assets, suggest that Full Coordination could increase participating countries’ energy dependence on each other. This effect may be especially troublesome for net importers but could be mitigated through a cap on imports or requiring domestic reserve margins. Future work could explore the impacts of being locked into an integrated system in terms of countries’ relative bargaining power. Additionally, calculations of unmet demand and curtailment are first-order estimates, owing to the aggregated representation of the transmission network and load centers. However, though the transmission topology is simplified, a detailed map of existing lines is used as a cost adder to potential project sites (Figure S14). Using the spatial distribution of favorable wind and solar PV project sites, future work could incorporate additional detail into the transmission topology to explore occurrences of, e.g., finer-scale locational marginal prices or transmission bottlenecks 35 . Geopolitical relations and governance among Mercosur countries, though outside the scope of this work, could significantly impact the feasibility of full and open electricity trade on an expanded transmission network 36 . The five countries considered here each operate under slightly different market structures and system operators, including a mix of public and private entities controlling the generation, transmission, and distribution systems (Table S5). Existing compensation structures, such as benefit-sharing, transaction costs, and congestion revenues, may disincentivize trade or create institutional or regulatory barriers to cooperation 37 , 38 . The decision of how to allocate the costs of new transmission lines between countries could further complicate matters 39 . These and other issues make the political and regulatory environment a critical driver of the feasibility of coordination 40 . Recent work analyzing African power pools similarly advocates to “mitigate non-cooperative strategies … and incentivise a shift in national agendas to build confidence in regional trade” 21 . However, the institutional complexities related to cooperative planning are beyond the scope of this study, which seeks to quantify the relative benefits of coordination, and in this context characterize techno-economic uncertainties in a decarbonizing power system. Thus, we do not include institutional friction or other barriers to coordination; Table S5 and Text S4 provide additional discussion and analysis around the regional context and potential institutional vulnerabilities. Our results can provide insights relevant to other regions seeking to achieve emissions reduction goals with potential opportunities for regional coordination with neighboring grids, especially grids which may be early on in their energy transition or simultaneously working to expand energy access. The Mercosur region of South America, though already operating a relatively clean electricity system, will still need additional renewables deployment to reduce current emissions levels and avoid future emissions. We show that regional coordination of the electricity system provides substantial economic benefits through more efficient capacity deployment and grid operations, benefits which accrue differently to individual countries but are economically advantageous to all. If potential energy dependence can be cost-effectively mitigated, aggregating regional grids such as South America’s southern cone could become building blocks of larger synchronous grids, spanning the continent and beyond. 4. Methods Electricity System Modeling Framework. We apply an open-source modeling framework developed in 28 using the electricity system modeling platform GridPath 41 , informed by spatially resolved, time-varying potentials of wind, solar, and hydropower. GridPath-Mercosur, used in this study, solves five-year time steps from 2020–2050, using 2020 as a historical calibration baseline. The model co-optimizes capacity investment decisions for generation, storage, and transmission infrastructures, as well as grid operation, which is simulated for 288 representative month-hourly time-slices for each model year. We represent the Mercosur study region with a single demand node for each of the five countries, interconnected by bilateral high-voltage transmission lines. By varying model parameters and operating conditions to construct an ensemble of scenarios, this framework is used to explore the value of full regional coordination under a stringent emissions reduction target and techno-economic uncertainty in wind turbines and solar photovoltaics. We use the outputs of 80 unique model realizations to assess the resulting variability in system-wide and country-level costs, as well as the new generation and transmission capacity required to meet emissions goals. GridPath is written in python and uses Gurobi as the solver. Capacity Expansion. The capacity expansion problem in GridPath is constructed as a mixed-integer linear program (MILP), which optimizes all infrastructure investments (i.e., generating capacity, storage capacity, and cross-border transmission) to meet demand at the lowest cost, subject to, e.g., reserve requirements, emissions constraints, and planned retirements. The total cost is computed as a net present value which sums both capital and operating (including fuel) costs over the model horizon. Dispatchable thermal generating capacity is selected by the model at the country scale, while wind and solar capacity is chosen from among spatially distributed project sites of different quality and potential capacity. Hydropower deployment similarly uses spatially distributed project sites but is based on a binary decision to build (or not), rather than a variable amount of capacity being chosen. Key outputs include new capacity costs, capacity mix, and transmission investments. Other input data, including historical load, demand growth rates, existing generation capacity, technology capital costs, and existing cross-border transmission capacity, are obtained following 28 . Hourly Grid Operations. For each of seven model periods, electricity is cost-optimally dispatched to meet each node’s demand for 288 representative month-hourly time slices throughout a year (24 hours × 12 months); each “time-point” contains the average demand for that month-hour and is weighted by the number of days in each month to construct a full year of operation. Similarly to country-level demand, energy supply from wind and solar PV is determined by the month-hourly average at each spatially distributed site. Peak demand is addressed via a 15% planning reserve margin, constrained to be supplied primarily by dispatchable generators. Dispatchable generators are subject to various constraints and operating characteristics; these include heat rates and ramping limits, as well as seasonal and/or daily energy availability (for hydropower and battery storage). Key outputs include total operating costs, generation mix, trade, and emissions. Wind and Solar PV. An added advantage of the model is the spatiotemporal resolution of variable renewable energy potential and project sites, which vary capacity factor and installation costs of candidate wind and solar projects throughout the region and according to modeled technology characteristics. Potential sites for wind and solar PV capacity deployment are estimated at 0.5-degree resolution throughout the study period and described by the technical potential available within each site 42 as well as any existing capacity located there 43 . To improve computational tractability, high-quality sites were pre-selected from the full set to include in the model, resulting in a total of 978 wind and 1,650 solar PV sites, each with a minimum of 50MW capacity potential and 10% average annual capacity factor (Table S7, Figure S15, Figure S16). For each gridded site, historical hourly capacity factors were obtained using Renewables.ninja 44 , 45 , which takes as inputs the tilt angle and tracking configuration for solar PV, and turbine-specific characteristics for wind. The reference scenario in 28 , for example, uses single-axis tracking and a tilt angle equal to latitude for solar PV, and a 100m hub height Vestas V90 2MW turbine for wind. The historical, technology-specific hourly capacity factor data was then used to construct month-hourly time series of energy availability for use with GridPath. By replicating these steps for ten different wind turbines and two solar PV types over each potential site, we produced a suite of model inputs with which to construct our scenario ensemble. We estimate capital costs using available aggregate data (Table S6), which is scaled to the cost trajectory of the reference case in 28 , as specific turbine cost data is not generally reported publicly. The combination of month-hourly resource potential, capital cost, and distance to the nearest transmission line describes the spatially resolved wind and solar PV sites available in the model. Although new transmission lines and flows within a country are not modeled, the distance from wind and solar PV projects to the nearest existing line is used to further inform site feasibility by computing a levelized cost of installing new lines, as a cost adder to the project’s overnight capital cost. Hydropower. Future expansion of hydropower is constrained to choosing only from a set of projects based on 46 . Like wind and solar PV, only projects with a minimum of 50MW capacity are made available to the model (leaving 201 total candidate projects), which covers over 90% of the total planned hydropower capacity in the region. The seasonality of hydropower production is incorporated following the methodology in 28 and summarized here. The inputs to GridPath are monthly hydropower availabilities estimated using the global hydrologic model Xanthos 47 , which is forced with the WFDEI bias corrected reanalysis dataset 48 . The resulting monthly historical hydropower simulations are used to estimate average monthly capacity factors for existing hydropower plants in the Mercosur region. These monthly capacity factors are assumed stationary and do not change over time. Note that for planned (future) hydropower projects, capacity factors are estimated using the capacity factors of the nearest existing hydropower facility within the same river basin. Hourly hydropower generation is dispatched by the model subject to these seasonal capacity factors. Scenario Ensemble. The highest quality wind and solar resources in Mercosur do not always form the most economically attractive projects, as particular areas may be protected, inaccessible, far from existing transmission, or crowded out by existing projects 42 . The technical potential of these resources is also subject to the characteristics of the technologies themselves, i.e., the power curve of a wind turbine or the solar PV tracking technology. Under regional coordination of the electricity grid, the locations of the best (most economical) candidate wind and solar PV projects may change and even cross international borders due to this uncertainty, affecting the trade dynamics of the system. As more renewable electricity capacity is built to meet decarbonization plans, the uncertainty in potential capacity expansion outcomes grows. Table 1 describes the 80-member scenario ensemble developed to explore this uncertainty, which configures GridPath-Mercosur along four dimensions: level of regional electricity coordination, mid-century emissions policy, solar PV tracking type, and wind turbine characteristics. The mid-century emissions policy achieves a 90% reduction in the annual CO 2 emitted by the electricity system between 2020 and 2050; this policy is compared with a Reference case with no CO 2 emissions target. The Mitigation (90% CO2 Cut) case represents a deep decarbonization scenario generally consistent with reaching established end-of-century mitigation targets 49 , for which net-zero is achieved shortly after 2050. The level of coordination in each realization is modeled as either Full Coordination (transnational interconnection expansion is co-optimized in the capacity expansion problem; electricity trade is unrestricted) or Limited Coordination (no new investment in transmission interconnections; gross electricity trade cannot exceed 2020 levels). Thus, the level of coordination represents both a willingness to invest in transmission and the overcoming of institutional barriers preventing increased trade. Wind and solar PV technology characteristics are used to estimate spatially resolved hourly capacity factors of candidate wind and solar projects throughout the region at 0.5-degree resolution 44 , 45 , and to estimate project-specific capital costs 50 . Two types of solar tracking technology are included ( Fixed vs. 1-axis tracking ); the tradeoff between cost, efficiency, and land requirement among tracking technologies has been broadly identified in the literature, with individual case studies differing in their optimal selection of tracking type depending on the local context 51 – 53 . Similarly, wind turbine output is also technology-specific and dependent on characteristics such as hub height and the turbine’s swept area. Here, we include hourly capacity factors of ten wind turbines to explore cost-performance tradeoffs. Refer to Table S6 and Table S7 for further details. Table 1 Scenario grouping for the 80-member ensemble run using GridPath-Mercosur. Each group contains ten scenarios: one for each wind turbine type (shown in Table S6). Scenario Group Level of Coordination Emissions Policy Solar PV Characteristics Wind Turbine Characteristics 1 Limited Coordination Reference (No CO 2 Target) 1-axis tracking Turbines T1-T10 2 Limited Coordination Reference (No CO 2 Target) Fixed Turbines T1-T10 3 Limited Coordination Mitigation (90% CO 2 Cut) 1-axis tracking Turbines T1-T10 4 Limited Coordination Mitigation (90% CO 2 Cut) Fixed Turbines T1-T10 5 Full Coordination Reference (No CO 2 Target) 1-axis tracking Turbines T1-T10 6 Full Coordination Reference (No CO 2 Target) Fixed Turbines T1-T10 7 Full Coordination Mitigation (90% CO 2 Cut) 1-axis tracking Turbines T1-T10 8 Full Coordination Mitigation (90% CO 2 Cut) Fixed Turbines T1-T10 Declarations Data and Code Availability Model input data and processed model output data will be deposited at Zenodo and publicly available as of the date of publication. Model scenario outputs have been deposited at Zenodo and will be publicly available as of the date of publication at https://doi.org/10.5281/zenodo.15096839. GridPath is an open source model accessible at https://doi.org/10.5281/zenodo.5822994; the version utilized in this work is located at https://github.com/blue-marble/gridpath/releases/tag/v0.8.1. All original code for data processing, analysis, and figure generation will be deposited at Zenodo and publicly available after publication. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request. Supplemental Information Files Document S1. Text S1-S4, Tables S1-S7, and Figures S1-S20. Acknowledgements This material is based upon work supported by the National Science Foundation under Grant No. 1855982. The authors acknowledge the Tufts University High Performance Compute Cluster (https://it.tufts.edu/high-performance-computing) which was utilized for the research reported in this paper. T.W. and G.I. are also affiliated with Pacific Northwest National Laboratory, which did not provide specific support for this paper. Author Contributions J.W. and A.F.M.K.C. conceptualized the study, T.W. and J.L. acquired the funding, A.F.M.K.C. and J.W. developed the methodology and model framework, J.W. and A.F.M.K.C. curated the data, J.W. conducted the formal analysis, J.W., J.L., A.F.M.K.C., T.W., G.I., and F.K. wrote and edited the paper, J.L. and A.F.M.K.C. supervised the project. References Clarke, L. et al. Energy Systems. In IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (2022) doi:10.1017/9781009157926.008. Sachs, J. D. et al. 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A comprehensive study of techno-economic and environmental features of different solar tracking systems for residential photovoltaic installations. Renewable and Sustainable Energy Reviews 129 , 109923 (2020). Additional Declarations There is NO Competing Interest. Supplementary Files DocumentS1.docx Document S1: Supplemental Information Cite Share Download PDF Status: Published Journal Publication published 10 Oct, 2025 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6505314","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":456395446,"identity":"80457801-2c70-454f-a08d-3d407885dfe5","order_by":0,"name":"Jacob Wessel","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-9238-5179","institution":"Tufts University","correspondingAuthor":true,"prefix":"","firstName":"Jacob","middleName":"","lastName":"Wessel","suffix":""},{"id":456395447,"identity":"22690061-d227-4768-bcee-364b9286f040","order_by":1,"name":"AFM Kamal Chowdhury","email":"","orcid":"https://orcid.org/0000-0003-3763-1204","institution":"University of Maryland","correspondingAuthor":false,"prefix":"","firstName":"AFM","middleName":"Kamal","lastName":"Chowdhury","suffix":""},{"id":456395448,"identity":"6dc88dd8-9eb5-472a-83be-6a2563641ea2","order_by":2,"name":"Thomas Wild","email":"","orcid":"https://orcid.org/0000-0002-6045-7729","institution":"University of Maryland","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Wild","suffix":""},{"id":456395449,"identity":"070b8e8b-4962-4382-99f4-06761acf2915","order_by":3,"name":"Franklyn Kanyako","email":"","orcid":"https://orcid.org/0000-0001-5510-0293","institution":"University of Massachusetts","correspondingAuthor":false,"prefix":"","firstName":"Franklyn","middleName":"","lastName":"Kanyako","suffix":""},{"id":456395450,"identity":"79593db2-526f-4b23-b03b-6fb559e42628","order_by":4,"name":"Gokul Iyer","email":"","orcid":"https://orcid.org/0000-0002-3565-7526","institution":"University of Maryland","correspondingAuthor":false,"prefix":"","firstName":"Gokul","middleName":"","lastName":"Iyer","suffix":""},{"id":456395451,"identity":"0da73bfd-61a3-46fa-95cc-65239fe646a6","order_by":5,"name":"Jonathan Lamontagne","email":"","orcid":"https://orcid.org/0000-0003-3976-1678","institution":"Tufts University","correspondingAuthor":false,"prefix":"","firstName":"Jonathan","middleName":"","lastName":"Lamontagne","suffix":""}],"badges":[],"createdAt":"2025-04-22 14:36:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6505314/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6505314/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41467-025-64093-8","type":"published","date":"2025-10-10T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82780085,"identity":"c0bf54fd-41b9-4522-8aba-86bfb3f9899a","added_by":"auto","created_at":"2025-05-15 08:01:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":325027,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the five-country study region, which includes Argentina, Brazil, Chile, Paraguay, and Uruguay. Discretized site-specific capacity factors for some wind and solar sites are shown, along with the location and capacity of future candidate hydropower projects. Labeled regions represent areas of particularly rich resource endowments. Refer to Methods for further details.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6505314/v1/2b88afe21adce52bc40833b7.png"},{"id":82780091,"identity":"7da513f2-3813-4fd8-ba60-a0a0dd5b2b62","added_by":"auto","created_at":"2025-05-15 08:01:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":160886,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative system costs across scenarios. (a) Increase in cumulative total system costs (2020-2050) due to \u003cem\u003eMitigation (90% CO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e Cut)\u003c/em\u003e, computed as the percent difference compared with the corresponding \u003cem\u003eReference\u003c/em\u003e scenarios, where the spread of the boxplots represents variability under the technoeconomic assumptions for solar PV and onshore wind; \u003cstrong\u003e(b)\u003c/strong\u003e Net savings in cumulative total system costs due to \u003cem\u003eFull Coordination\u003c/em\u003e, computed as the difference compared with the corresponding \u003cem\u003eLimited Coordination \u003c/em\u003escenarios; \u003cstrong\u003e(c) \u003c/strong\u003eEconomic benefits of regional coordination under \u003cem\u003eMitigation\u003c/em\u003e, computed as differences in the three components of system costs between pairs of \u003cem\u003eMitigation\u003c/em\u003e scenarios which differ by coordination policy. The system costs consist of costs for building new generation capacity, grid operations (including fuel costs), and transmission expansion. Positive values indicate extra investments required under \u003cem\u003eFull Coordination\u003c/em\u003e, while negative values indicate cost savings.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6505314/v1/5bf35e4bd1b7b70652e00700.png"},{"id":82780088,"identity":"a922dc25-4462-40b0-9d0a-f1283a957267","added_by":"auto","created_at":"2025-05-15 08:01:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":144796,"visible":true,"origin":"","legend":"\u003cp\u003eImpacts of \u003cem\u003eFull Coordination \u003c/em\u003eon system-wide outcomes by generation technology, shown as differences between pairs of \u003cem\u003eMitigation\u003c/em\u003escenarios which differ only by the level of coordination.\u003cstrong\u003e (a) \u003c/strong\u003eNew generation capacity investments by 2050;\u003cstrong\u003e (b) \u003c/strong\u003eTotal generation in 2050. The spread of the boxplots represents variability under the technoeconomic assumptions, while positive values indicate a metric that is greater under \u003cem\u003eFull Coordination\u003c/em\u003e.\u003cem\u003e \u003c/em\u003eWind, solar PV, and fossil-based thermal generation (including all gas, coal, and diesel generators) are emphasized.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6505314/v1/a1f0d13820437e78babab0c6.png"},{"id":82780087,"identity":"b60cc390-c881-411a-a548-9c18a18e5a0b","added_by":"auto","created_at":"2025-05-15 08:01:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":98760,"visible":true,"origin":"","legend":"\u003cp\u003eElectricity trade among countries. \u003cstrong\u003e(a)\u003c/strong\u003e Total trade from 2020-2050 in six modeled interconnections for \u003cem\u003eFull Coordination \u003c/em\u003escenarios. The country listed first in each axis label denotes the net exporter for each interconnection. Orange boxes show the range of values seen in \u003cem\u003eLimited Coordination\u003c/em\u003escenarios, which are not prohibited from electricity trade, but rather restricted to not exceed 2020 levels (some are zero and not shown). \u003cstrong\u003e(b) \u003c/strong\u003eTotal exports from each country under \u003cem\u003eFull Coordination\u003c/em\u003e. \u003cstrong\u003e(c) \u003c/strong\u003eTotal imports to each country under \u003cem\u003eFull Coordination\u003c/em\u003e. Uncertainty bars give the full range of outcomes across techno-economic scenarios.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6505314/v1/05c9c944d5d49591b904c8fa.png"},{"id":82780090,"identity":"a0874bc8-5dbc-485d-af8c-45f4bfa0b571","added_by":"auto","created_at":"2025-05-15 08:01:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":294076,"visible":true,"origin":"","legend":"\u003cp\u003eImpacts on capacity mix uncertainty. Panels show uncertainty ratios in the capacity share (fraction of total system capacity made up of each technology) for \u003cstrong\u003e(a) \u003c/strong\u003esolar; \u003cstrong\u003e(b)\u003c/strong\u003e wind; \u003cstrong\u003e(c)\u003c/strong\u003e hydropower; and \u003cstrong\u003e(d) \u003c/strong\u003efossil fuel generators (gas, coal, diesel), for Mercosur and for individual countries. Values are computed as a ratio of the interquartile range (IQR) for \u003cem\u003eMitigation \u003c/em\u003escenarios to the IQR of \u003cem\u003eReference\u003c/em\u003e scenarios; arrows describe the directions of change. In panel \u003cstrong\u003e(b)\u003c/strong\u003e, the IQR ratio for Chile under \u003cem\u003eLimited Coordination \u003c/em\u003eis undefined due to division by zero. Marker sizes correspond to each country’s relative size. A similar plot computing the IQR ratio between \u003cem\u003eFull \u003c/em\u003eand \u003cem\u003eLimited Coordination\u003c/em\u003e is shown in Figure S11.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6505314/v1/363cbaa5164e48baf85869bb.png"},{"id":93285036,"identity":"88c32230-2115-4c9e-a399-20bcf6def147","added_by":"auto","created_at":"2025-10-11 07:08:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1746771,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6505314/v1/78d5e26c-23d0-4f8c-9d2e-6089a845b7bb.pdf"},{"id":82780971,"identity":"e5544c23-c21e-4208-8411-4e16427e88a5","added_by":"auto","created_at":"2025-05-15 08:09:36","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":8060234,"visible":true,"origin":"","legend":"Document S1: Supplemental Information","description":"","filename":"DocumentS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6505314/v1/4b0e2f2da96e0b217404e9e7.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Regional coordination can alleviate the cost burden of a low-carbon electricity system","fulltext":[{"header":"Highlights","content":"\u003cp\u003e\u0026bull; Investing in transmission can provide three-fold savings in capacity requirements\u003c/p\u003e\u003cp\u003e\u0026bull; Coordination can hedge against technological uncertainty in the energy transition\u003c/p\u003e\u003cp\u003e\u0026bull; Regional grid planning can increase energy interdependence, especially for importers\u003c/p\u003e\u003cp\u003e\u0026bull; Winners and losers can emerge differently across scenarios with similar net benefits\u003c/p\u003e"},{"header":"Plain Language Summary","content":"\u003cp\u003eThe supply of electricity to the grid from renewable sources like wind and solar can vary substantially by geographical region. Grid planners must decide on the best combination of resources to serve an area\u0026rsquo;s electricity needs subject to many competing objectives, such as cost, reliability, security, and climate mitigation; they then make long-term capital-intensive infrastructure investments to meet these goals. However, many countries plan their electricity grids independently from their neighbors, rather than coordinating investments and trading electricity. These issues are particularly salient in the Global South. We explore the potential for a region in South America to lower electricity costs in the future through expanding international transmission lines and co-optimizing power plant investments, which allows broader access to the region\u0026rsquo;s geographically dispersed renewable resources. Our results suggest that this grid integration can reduce planning uncertainty and is more economical region-wide, but may benefit (or burden) individual countries differently.\u003c/p\u003e\n"},{"header":"1. Introduction","content":"\u003cp\u003eTransitioning to a low-carbon future is likely to strain bulk electric power systems, especially when paired with increasing demand\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e; nevertheless, decarbonizing electricity is one of several key pillars for achieving global sustainable development objectives, such as limiting climate change and its impacts\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. System planners in countries overseeing such a transition must make long-term infrastructure planning decisions in the face of uncertainty about the future\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Navigating national decarbonization pathways will require robust strategies to utilize diverse, intermittent, and unevenly distributed resources\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In many developing economies, achieving emissions reductions while balancing cost, reliability, and energy security will continue to be a critical challenge. At the same time, large-scale (international to global) decarbonization efforts will hinge on more fragmented national- and subnational-scale infrastructure investments and decisions\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. In many regions, neighboring electricity systems have natural complementarities in renewable (namely, wind and solar) and hydropower potential, built infrastructure, and demand patterns\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. In some cases, such differences can emerge as competitive advantages. Often, especially in the developing world, there is opportunity for greater regional coordination of electricity grid planning and operation, with the potential for economic, environmental, and societal co-benefits\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e (see Text S1). To fully enable these benefits, investments in future grids should explore the value of expanding interregional transmission in addition to optimizing investments in generation capacity\u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHowever, assessing the complementarity of one country\u0026rsquo;s renewable resource endowment with another\u0026rsquo;s is sensitive to numerous uncertainties, which may emerge differently across scales\u003csup\u003e\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Adopting finer-scale spatiotemporal representations of wind and solar PV buildout, for example, can lead to larger uncertainty in distributional outcomes among individual countries, even when the aggregated system\u0026rsquo;s sensitivity to uncertainty remains small\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e; this adds to the difficulty in providing robust planning decisions across scales\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Separately, uncertainty surrounding the techno-economic performance of wind and solar PV technologies can also drive future capacity expansion outcomes\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. It is important, then, to understand how uncertainty in spatially resolved modeling inputs may impact larger-scale regional infrastructure decisions and coordinated grid outcomes\u003csup\u003e\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e (see Text S2). Further, assessing the degree to which country-level burdens and benefits may be distributed within a decarbonizing regional system subject to these uncertainties is needed to justify coordination.\u003c/p\u003e \u003cp\u003eHere, we use an open-source optimization-based capacity expansion model (GridPath\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e) to assess the role of full regional coordination on electricity capacity expansion and deep decarbonization pathways among five countries in South America: Argentina, Brazil, Chile, Paraguay, and Uruguay (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This region is actively developing economically while simultaneously working to further decarbonize its electricity system. Its history of shared binational hydroelectric projects and regional economic integration through the Mercosur or \u0026ldquo;Southern Cone\u0026rdquo; trade bloc\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e positions these countries favorably for longer-term, coordinated electricity planning (Text S3). Historically a hydro-dominated grid, Mercosur has also adopted other forms of clean electricity to meet growing demand (and balance its hydroelectric generation) while avoiding future emissions\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrevious studies evaluating benefits of regional electricity coordination vary in their treatment of specific, isolated uncertainties and the level of detail in model structure\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e (see Text S1). As such, there are gaps in our understanding of how regionally coordinated grid infrastructure planning may be influenced by spatially explicit resource potentials under techno-economic uncertainty, and how the impacts will be distributed across scales. It is thus an open question how regional electricity systems can better utilize finer scale representations of resource potentials to cooperatively design robust, co-optimized generator fleet and transmission expansion pathways. We help address these gaps by exploring a broad range of scenarios to glean insights on the tradeoffs and synergies stemming from regional grid coordination under uncertainty, using a model with highly detailed spatial resolution of resource endowment.\u003c/p\u003e \u003cp\u003eWe explore an ensemble of 80 scenarios (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) that vary across four levers: level of regional electricity coordination, mid-century emissions policy, wind turbine characteristics, and solar PV tracking type (see Methods). For the level of regional electricity coordination, we consider a \u003cem\u003eFull Coordination\u003c/em\u003e assumption in which countries trade openly while co-optimizing international transmission investments as part of the capacity expansion problem, and a \u003cem\u003eLimited Coordination\u003c/em\u003e assumption in which trade and transmission capacity are limited to 2020 levels. For emissions policy, we consider the \u003cem\u003eMitigation\u003c/em\u003e assumption in which CO\u003csub\u003e2\u003c/sub\u003e emissions from the electricity system are reduced 90% relative to 2020 levels, and the \u003cem\u003eReference\u003c/em\u003e assumption in which emissions are assumed to be unconstrained. For wind turbine characteristics and PV tracking type, we vary the hourly capacity factors and capital costs of candidate wind and solar projects throughout the region at 0.5-degree resolution \u0026ndash; considering assumptions from ten wind turbine models and fixed vs. single-axis solar tracking.\u003c/p\u003e \u003cp\u003eOur analysis reveals insights around the ability of coordination and electricity trade to cost-effectively insulate against deep uncertainties in the energy transition, and the emergence of substantial but potentially uneven economic benefits under a range of possible futures. The modeling and experimental setup can be adapted to any region with the potential for regional grid coordination, while the insights articulated here are particularly pertinent to groups of countries with geographically variable resource endowments and diversity in size and demand patterns.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. System costs and the benefits of coordination\u003c/h2\u003e \u003cp\u003eWe start by evaluating the system-wide costs of electricity decarbonization under a range of techno-economic uncertainties. Comparing \u003cem\u003eMitigation\u003c/em\u003e and \u003cem\u003eReference\u003c/em\u003e scenarios, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(a) shows that across a range of technology assumptions and levels of coordination, decarbonization can be achieved with cost increases ranging from ~\u0026thinsp;5 to 18%. Here, scenario technology assumptions in the choice of wind turbine and solar tracking drive the range of outcomes shown, and can make a significant difference in subsequent planning decisions. \u003cem\u003eFull Coordination\u003c/em\u003e alleviates this cost burden of decarbonization (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(b)), while also slightly reducing the overall uncertainty in the cost premium. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(c) disaggregates individual components of cumulative system costs, computed as differences between pairs of \u003cem\u003eMitigation\u003c/em\u003e scenarios which differ only by the coordination policy. These cost components are broken out from the \u003cem\u003eMitigation\u003c/em\u003e box in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(b), which in turn reflects the difference (on a cost basis) between the two boxes in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(a).\u003c/p\u003e \u003cp\u003eLimiting coordination results in a net increase in total costs (sum of the individual components), primarily driven by the costs of newly installed capacity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(c)). This suggests an overbuilding of new generation capacity in limited coordination scenarios compared to allowing full and open trade (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This capacity requires over triple the investment than the costs of expanding transmission capacity in \u003cem\u003eFull Coordination\u003c/em\u003e scenarios (\u003cspan\u003e$\u003c/span\u003e14.7-22.8B vs. \u003cspan\u003e$\u003c/span\u003e3.5-7.0B). Investment in new transmission capacity enables countries to access variable renewable resources in neighboring regions, allowing for potential cost savings through more optimal placement of wind and solar projects.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe reduced investment requirement for new generating capacity under \u003cem\u003eFull Coordination\u003c/em\u003e is primarily due to the increased efficiency in grid balancing achieved by integrating a larger system of generators. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(a) shows the effect of \u003cem\u003eFull Coordination\u003c/em\u003e on capacity investments under \u003cem\u003eMitigation\u003c/em\u003e. System-wide investment in new generating capacity is lower for most technologies under most conditions, except for fossil generators built mainly in Chile (as well as wind and hydropower for a few scenarios). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e(b) shows differences in the generation mix in 2050 between \u003cem\u003eFull\u003c/em\u003e and \u003cem\u003eLimited Coordination\u003c/em\u003e scenarios, revealing a different trend: total wind generation tends to be substantially more under \u003cem\u003eFull Coordination\u003c/em\u003e, even though investments in wind capacity tend to decrease. This system-wide trend comes mainly from new wind installations in Chile under \u003cem\u003eLimited Coordination\u003c/em\u003e. These capacity additions are required to meet growing demands with zero-carbon energy and complement Chile\u0026rsquo;s diurnal solar generation, but have lower capacity factors than more productive sites in Argentina and Brazil. Even though solar PV is the dominant renewable resource in Chile, a point is reached at which the most favorable wind projects become economically viable compared with the remaining unbuilt solar PV sites (and the battery storage needed to complement them). Refer to Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-S4 for country-level outcomes.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e show that under \u003cem\u003eMitigation\u003c/em\u003e and \u003cem\u003eLimited Coordination\u003c/em\u003e, clean generating capacity is overbuilt to meet the emissions policy, which also results in more curtailment of the intermittent wind and solar (see Figure S4). This is supplemented only slightly by additional investments in battery storage, nuclear, and biomass, deployed in later periods. Higher curtailment and storage deployment reflect a relative deterioration in grid efficiency and reliability, respectively, when limiting coordination; battery storage is deployed to mitigate this impact when economically viable. The temporal mismatch between intermittent generation (which increases under \u003cem\u003eMitigation\u003c/em\u003e) and demand on sub-daily scales can be dampened through a more efficient utilization of resources across a wider region, and by aggregating load from individual countries exhibiting different diurnal load profiles (see Figure S5). This reduces reliance on grid-scale battery storage, decreases total curtailment, and lowers the use of more expensive peaking plants to prevent supply shortfalls (see Figure S6 and Figure S7).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Trade under generation portfolio uncertainty\u003c/h2\u003e \u003cp\u003eWe find that \u003cem\u003eFull Coordination\u003c/em\u003e facilitates a substantial increase in total bilateral electricity exchanges (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(a)); \u003cem\u003eMitigation\u003c/em\u003e scenarios experience the largest growth in all but one interconnection (Argentina-Uruguay). This consistently higher trade with increased variability suggests that trade has a role in dampening cost burdens that may arise from techno-economic uncertainty and emissions policy, by enabling more efficient adoption of renewables. In other words, \u003cem\u003eFull Coordination\u003c/em\u003e can facilitate emissions reduction by providing protection against techno-economic uncertainty and system-wide fluctuations in costs through investment in transmission expansion (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Figure S8). In the case of Argentina-Uruguay, higher trade in \u003cem\u003eReference\u003c/em\u003e scenarios is mainly due to fossil fuel exports from Argentina providing reliable and low-cost grid balancing for Uruguay\u0026rsquo;s intermittent renewables in a few scenarios in which the future techno-economic characteristics of wind power are unfavorable (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Under \u003cem\u003eMitigation\u003c/em\u003e, because emissions are a binding constraint, Uruguay installs more domestic renewable capacity to meet and balance its load, rather than importing it from larger countries.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(b,c) shows each country\u0026rsquo;s cumulative exports and imports, respectively, for the 2020\u0026ndash;2050 horizon. As the largest country, Brazil\u0026rsquo;s high demand and capacity additions (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) relative to other Mercosur countries proportionally drives system-level outcomes, even as it becomes increasingly reliant on imported power. Argentina and Paraguay are net exporters, with Paraguay continuing to export hydropower to Brazil. Hydropower, though shown to increase in nominal capacity over time, decreases in share in the system\u0026rsquo;s generation mix, suggesting a shift from a hydro-dominated system to a more balanced mix of complementary clean technologies (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Still, no countries switch roles from net exporter to net importer (or vice-versa) across our scenarios. Argentina plays a central role in connecting and balancing the Mercosur grid, due not only to its size, central geographical position, and wind resource potential, but also its use of natural gas in complementing the growing share of renewables across the system, even as it is phased out over time under \u003cem\u003eMitigation\u003c/em\u003e. Thus, in addition to a heterogeneity in resources (including bridge fuels), more interconnections to neighboring load zones could help position Argentina to benefit from providing ancillary services to a regional grid.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe net exporters in this system under \u003cem\u003eMitigation\u003c/em\u003e and \u003cem\u003eFull Coordination\u003c/em\u003e (Argentina and Paraguay, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) experience higher total costs, due to the additional generating capacity installed to serve export demand (Figure S3). This cost increase does not reflect the revenue from the additional exports or ancillary services. Further, allocating the costs of building new transmission capacity between countries can impact relative outcomes; here, these costs are assumed to be shared equally by the two countries represented in each expanded interconnection. Additionally, increased investment in these countries for building export capacity is a favorable outcome for bringing high-quality jobs and spurring further domestic development, the value of which is left for future work to quantify. Brazil, on the other hand, must build new generating capacity mainly to meet its own demand growth, and increases reliance on imports from Paraguay and Argentina to dampen the overall cost burden from mitigation. Thus, while increased regional coordination can lower costs system-wide through grid balancing, countries capable of outpacing domestic demand growth with clean electricity deployment may capture additional economic benefits in a decarbonization policy environment.\u003c/p\u003e \u003cp\u003eFor Chile, allowing \u003cem\u003eFull Coordination\u003c/em\u003e under \u003cem\u003eMitigation\u003c/em\u003e increases newly installed fossil fuel generating capacity (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) and its overall generation share by 2050 (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). This suggests that a coordinated regional emissions policy optimizing for cost may also influence the siting of new fossil-based generators throughout the system. Such a shift in where fossil-based generation occurs, even under deep decarbonization, could have negative local environmental and health impacts not captured by the model\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Regarding the adoption of renewables, capturing Chile\u0026rsquo;s full potential for solar PV is particularly limited by the ability to integrate its diurnal variability into the larger grid. Because Chile\u0026rsquo;s wind potential is mostly concentrated in the far south of the country far from existing transmission, its capacity expansion pathway under \u003cem\u003eMitigation\u003c/em\u003e relies on producing large amounts of solar during the day, while importing wind and hydropower from Argentina at night. Under \u003cem\u003eLimited Coordination\u003c/em\u003e, Chile instead relies on battery storage to integrate its solar resources (Figure S4), and must supplement with wind capacity (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Thus, through trade, \u003cem\u003eFull Coordination\u003c/em\u003e can exploit the complementarity of diverse portfolios of renewables to smooth out seasonal and diurnal swings associated with resource intermittency (Figure S9, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e0); this in turn enables more strategic deployment of the most economic and highest quality wind and solar resources throughout the region. However, although the system is able to manage increasing levels of solar generation through \u003cem\u003eFull Coordination\u003c/em\u003e, countries deploying substantial solar PV capacity could become dependent on transnational electricity trade for balancing diurnal intermittency, especially under \u003cem\u003eMitigation\u003c/em\u003e (Text S4). Additionally, without further cost declines in battery storage, we find significant amounts of solar generation could still be curtailed even under \u003cem\u003eFull Coordination\u003c/em\u003e (Figure S4, Figure S5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Uncertainty in renewables deployment\u003c/h2\u003e \u003cp\u003eMercosur\u0026rsquo;s future electricity portfolio is strongly tied to the presence of an emissions reduction policy, which shapes the uncertainty surrounding the deployment of different technologies. Further, a fully coordinated system seeking to decarbonize has a more flexible set of country-level pathways to choose from in order to manage technoeconomic uncertainty. This, however, can heavily impact the optimal generator fleet at the country scale, adding to the relative uncertainty in a country\u0026rsquo;s infrastructure development pathway even while hedging against technological uncertainty system wide. Examining this uncertainty in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, we explore a measure of how the variability in a technology\u0026rsquo;s capacity share changes across our scenarios. We find that in almost all cases, both country-level and system-wide, \u003cem\u003eMitigation\u003c/em\u003e reduces deployment uncertainty associated with wind, solar PV, and hydropower (IQR ratio\u0026thinsp;\u0026lt;\u0026thinsp;1). In other words, a deep decarbonization policy greatly narrows the range of system-wide capacity outcomes for each of these generation technologies; however, the region-wide results do not consistently reflect any one country. Fossil plants do not exhibit this same pattern (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(d)). Although these generators are dispatched much less under \u003cem\u003eMitigation\u003c/em\u003e, new reserve capacity is still built throughout the region, subject to the demands of different wind and solar PV technology characteristics. As such, system-wide capacity mix uncertainty is higher for fossil generators under \u003cem\u003eMitigation\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eIn most cases under \u003cem\u003eFull Coordination\u003c/em\u003e, the decarbonization policy drives more modest reductions or even increases in clean energy deployment uncertainty; the flexibility afforded by a larger interconnected system allows for a broader range of outcomes in the region\u0026rsquo;s capacity mix. One notable case is wind capacity uncertainty in Argentina (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(b)), due to Argentina\u0026rsquo;s central balancing role in Mercosur increasing its sensitivity to wind technology under \u003cem\u003eMitigation\u003c/em\u003e. Uncertainty in fossil fuel capacity under \u003cem\u003eFull Coordination\u003c/em\u003e again shows less stability than other technologies, and country-level findings further diverge from the \u003cem\u003eLimited Coordination\u003c/em\u003e scenarios (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e(d)). This further illustrates how policy levers and regional coordination can cause heterogeneous subregional impacts to emerge, but that the underlying techno-economic uncertainty driving grid outcomes can also be exacerbated or ameliorated across these scales.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eUncertainty in country-level capacity expansion decisions within a larger coordinated grid helps reveal how regional decarbonization pathways may evolve under uncertainty, and if key vulnerabilities or consistent burdens are experienced by particular countries. \u003cem\u003eFull Coordination\u003c/em\u003e can allow individual countries undergoing deep decarbonization (i.e., \u003cem\u003eMitigation\u003c/em\u003e) to exploit more of the broader region\u0026rsquo;s highest quality renewable resources (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e2, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e3), while utilizing trade for balancing intermittency and load patterns. For example, Chile, a country known for its excellent solar resource potential, must install some wind capacity to meet its \u003cem\u003eMitigation\u003c/em\u003e target under \u003cem\u003eLimited Coordination\u003c/em\u003e, but deploys only solar PV under \u003cem\u003eFull Coordination\u003c/em\u003e while Argentina and Paraguay invest more in wind (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Regarding country-level emissions, the nature of the impacts of \u003cem\u003eFull Coordination\u003c/em\u003e are again dependent on the presence of an emissions target (Figure S4, bottom row). Because \u003cem\u003eMitigation\u003c/em\u003e is implemented as a region-wide emissions cap, the emissions pathways are quite narrow when aggregated to the full system; however, individual countries show some variability. Thus, by developing cooperative strategies for the larger interconnected system, countries may be able to implement emissions reduction plans that are more cost-effective and robust to techno-economic uncertainties. At the same time, \u003cem\u003eFull Coordination\u003c/em\u003e may not be able to spur emissions reduction on its own without further cost declines. Additional analysis in future work is needed to quantify and characterize the effects of different strategies.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eThis study contributes a regional analysis with globally generalizable insights describing the opportunity and value of grid coordination in long-term regional electricity systems planning. We construct 80 future development pathways using GridPath-Mercosur, a five-node electricity system model co-optimizing generating capacity and transmission expansion decisions for the Mercosur region of South America. This framework extends previous analyses by combining broad spatiotemporal coverage with techno-economic uncertainty in variable renewable energy to examine implications of regional coordination (or a lack thereof) under a deep decarbonization policy target.\u003c/p\u003e \u003cp\u003eRegion-wide, we find consistent and substantial benefits in planning and operating under \u003cem\u003eFull Coordination\u003c/em\u003e, through increased electricity trade and unrestricted internodal transmission expansion. Under a deep decarbonization \u003cem\u003eMitigation\u003c/em\u003e target, a three-fold savings in new capacity costs is achieved through relatively modest investment in transmission expansion (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These savings are even greater when considering the further reduction in grid operating costs (which includes fuel costs). In addition to reducing the cost premium of decarbonizing, \u003cem\u003eFull Coordination\u003c/em\u003e also decreases uncertainty in total system costs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(a)). Through open electricity trade between countries (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Figure S9), regional coordination provides flexibility to respond to regional advantages and disadvantages in resource utilization, which hedges against technological uncertainty in the energy transition. Future work could examine the capital cost or duration of storage technologies, to explore where the economic viability of storage competes with the benefits of coordination in facilitating a low-carbon energy transition. Further, the \u003cem\u003eMitigation\u003c/em\u003e target in this work represents just one possible emissions trajectory for this system; additional modeling efforts are needed to explore alternative pathways in more detail, including countries\u0026rsquo; Nationally Determined Contributions as well as economy-wide and non-CO\u003csub\u003e2\u003c/sub\u003e emissions reductions.\u003c/p\u003e \u003cp\u003eAlthough region-wide intermittencies and uncertainty in variable renewable energy technology may be mitigated through coordination, benefits to the region can be distributed differently among the five constituent countries, due to their relative size, advantages in natural resource availability, and modeled technology characteristics. Uncertainty surrounding the relative deployment of various generation technologies (and thus the economic development) vary across countries and policy environments (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In other words, differences in installed wind and solar PV capacities in each country tend to offset region-wide, but different countries may stand to benefit or sacrifice across scenarios. These benefits may emerge as gains in both short- and long-term employment, reduction in local air pollution, and a greater ability to attract private capital investments. Thus, when planning a Mercosur-wide electricity decarbonization pathway, balancing country-level impacts must be considered in relation to the system-wide benefits of coordination and increased trade\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eInternationally coordinated grid planning, with diverse objectives beyond cost minimization, must navigate deep uncertainties and competing interests among stakeholders. Increasing overall electricity trade to target a smaller number of only the highest quality renewables sites relies on developing, maintaining, and using an expanded transmission network to realize the full benefits. A multinational electricity grid designed for cooperative international trade may experience notable negative effects if there are barriers to transmission expansion or transnational electricity trade (Text S4). These potential risks, including substantial levels of unmet demand, curtailment, and stranded assets, suggest that \u003cem\u003eFull Coordination\u003c/em\u003e could increase participating countries\u0026rsquo; energy dependence on each other. This effect may be especially troublesome for net importers but could be mitigated through a cap on imports or requiring domestic reserve margins. Future work could explore the impacts of being locked into an integrated system in terms of countries\u0026rsquo; relative bargaining power. Additionally, calculations of unmet demand and curtailment are first-order estimates, owing to the aggregated representation of the transmission network and load centers. However, though the transmission topology is simplified, a detailed map of existing lines is used as a cost adder to potential project sites (Figure S14). Using the spatial distribution of favorable wind and solar PV project sites, future work could incorporate additional detail into the transmission topology to explore occurrences of, e.g., finer-scale locational marginal prices or transmission bottlenecks\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGeopolitical relations and governance among Mercosur countries, though outside the scope of this work, could significantly impact the feasibility of full and open electricity trade on an expanded transmission network\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. The five countries considered here each operate under slightly different market structures and system operators, including a mix of public and private entities controlling the generation, transmission, and distribution systems (Table S5). Existing compensation structures, such as benefit-sharing, transaction costs, and congestion revenues, may disincentivize trade or create institutional or regulatory barriers to cooperation\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. The decision of how to allocate the costs of new transmission lines between countries could further complicate matters\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. These and other issues make the political and regulatory environment a critical driver of the feasibility of coordination\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Recent work analyzing African power pools similarly advocates to \u0026ldquo;mitigate non-cooperative strategies \u0026hellip; and incentivise a shift in national agendas to build confidence in regional trade\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. However, the institutional complexities related to cooperative planning are beyond the scope of this study, which seeks to quantify the relative benefits of coordination, and in this context characterize techno-economic uncertainties in a decarbonizing power system. Thus, we do not include institutional friction or other barriers to coordination; Table S5 and Text S4 provide additional discussion and analysis around the regional context and potential institutional vulnerabilities.\u003c/p\u003e \u003cp\u003eOur results can provide insights relevant to other regions seeking to achieve emissions reduction goals with potential opportunities for regional coordination with neighboring grids, especially grids which may be early on in their energy transition or simultaneously working to expand energy access. The Mercosur region of South America, though already operating a relatively clean electricity system, will still need additional renewables deployment to reduce current emissions levels and avoid future emissions. We show that regional coordination of the electricity system provides substantial economic benefits through more efficient capacity deployment and grid operations, benefits which accrue differently to individual countries but are economically advantageous to all. If potential energy dependence can be cost-effectively mitigated, aggregating regional grids such as South America\u0026rsquo;s southern cone could become building blocks of larger synchronous grids, spanning the continent and beyond.\u003c/p\u003e"},{"header":"4. Methods","content":"\u003cp\u003e \u003cb\u003eElectricity System Modeling Framework.\u003c/b\u003e We apply an open-source modeling framework developed in \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e using the electricity system modeling platform GridPath\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, informed by spatially resolved, time-varying potentials of wind, solar, and hydropower. GridPath-Mercosur, used in this study, solves five-year time steps from 2020\u0026ndash;2050, using 2020 as a historical calibration baseline. The model co-optimizes capacity investment decisions for generation, storage, and transmission infrastructures, as well as grid operation, which is simulated for 288 representative month-hourly time-slices for each model year. We represent the Mercosur study region with a single demand node for each of the five countries, interconnected by bilateral high-voltage transmission lines. By varying model parameters and operating conditions to construct an ensemble of scenarios, this framework is used to explore the value of full regional coordination under a stringent emissions reduction target and techno-economic uncertainty in wind turbines and solar photovoltaics. We use the outputs of 80 unique model realizations to assess the resulting variability in system-wide and country-level costs, as well as the new generation and transmission capacity required to meet emissions goals. GridPath is written in python and uses Gurobi as the solver.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCapacity Expansion.\u003c/b\u003e The capacity expansion problem in GridPath is constructed as a mixed-integer linear program (MILP), which optimizes all infrastructure investments (i.e., generating capacity, storage capacity, and cross-border transmission) to meet demand at the lowest cost, subject to, e.g., reserve requirements, emissions constraints, and planned retirements. The total cost is computed as a net present value which sums both capital and operating (including fuel) costs over the model horizon. Dispatchable thermal generating capacity is selected by the model at the country scale, while wind and solar capacity is chosen from among spatially distributed project sites of different quality and potential capacity. Hydropower deployment similarly uses spatially distributed project sites but is based on a binary decision to build (or not), rather than a variable amount of capacity being chosen. Key outputs include new capacity costs, capacity mix, and transmission investments. Other input data, including historical load, demand growth rates, existing generation capacity, technology capital costs, and existing cross-border transmission capacity, are obtained following \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHourly Grid Operations.\u003c/b\u003e For each of seven model periods, electricity is cost-optimally dispatched to meet each node\u0026rsquo;s demand for 288 representative month-hourly time slices throughout a year (24 hours \u0026times; 12 months); each \u0026ldquo;time-point\u0026rdquo; contains the average demand for that month-hour and is weighted by the number of days in each month to construct a full year of operation. Similarly to country-level demand, energy supply from wind and solar PV is determined by the month-hourly average at each spatially distributed site. Peak demand is addressed via a 15% planning reserve margin, constrained to be supplied primarily by dispatchable generators. Dispatchable generators are subject to various constraints and operating characteristics; these include heat rates and ramping limits, as well as seasonal and/or daily energy availability (for hydropower and battery storage). Key outputs include total operating costs, generation mix, trade, and emissions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eWind and Solar PV.\u003c/b\u003e An added advantage of the model is the spatiotemporal resolution of variable renewable energy potential and project sites, which vary capacity factor and installation costs of candidate wind and solar projects throughout the region and according to modeled technology characteristics. Potential sites for wind and solar PV capacity deployment are estimated at 0.5-degree resolution throughout the study period and described by the technical potential available within each site\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e as well as any existing capacity located there\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. To improve computational tractability, high-quality sites were pre-selected from the full set to include in the model, resulting in a total of 978 wind and 1,650 solar PV sites, each with a minimum of 50MW capacity potential and 10% average annual capacity factor (Table S7, Figure S15, Figure S16). For each gridded site, historical hourly capacity factors were obtained using Renewables.ninja\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, which takes as inputs the tilt angle and tracking configuration for solar PV, and turbine-specific characteristics for wind. The reference scenario in \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, for example, uses single-axis tracking and a tilt angle equal to latitude for solar PV, and a 100m hub height Vestas V90 2MW turbine for wind. The historical, technology-specific hourly capacity factor data was then used to construct month-hourly time series of energy availability for use with GridPath. By replicating these steps for ten different wind turbines and two solar PV types over each potential site, we produced a suite of model inputs with which to construct our scenario ensemble. We estimate capital costs using available aggregate data (Table S6), which is scaled to the cost trajectory of the reference case in \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, as specific turbine cost data is not generally reported publicly. The combination of month-hourly resource potential, capital cost, and distance to the nearest transmission line describes the spatially resolved wind and solar PV sites available in the model. Although new transmission lines and flows within a country are not modeled, the distance from wind and solar PV projects to the nearest existing line is used to further inform site feasibility by computing a levelized cost of installing new lines, as a cost adder to the project\u0026rsquo;s overnight capital cost.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHydropower.\u003c/b\u003e Future expansion of hydropower is constrained to choosing only from a set of projects based on \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Like wind and solar PV, only projects with a minimum of 50MW capacity are made available to the model (leaving 201 total candidate projects), which covers over 90% of the total planned hydropower capacity in the region. The seasonality of hydropower production is incorporated following the methodology in \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e and summarized here. The inputs to GridPath are monthly hydropower availabilities estimated using the global hydrologic model Xanthos\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, which is forced with the WFDEI bias corrected reanalysis dataset\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. The resulting monthly historical hydropower simulations are used to estimate average monthly capacity factors for existing hydropower plants in the Mercosur region. These monthly capacity factors are assumed stationary and do not change over time. Note that for planned (future) hydropower projects, capacity factors are estimated using the capacity factors of the nearest existing hydropower facility within the same river basin. Hourly hydropower generation is dispatched by the model subject to these seasonal capacity factors.\u003c/p\u003e \u003cp\u003e \u003cb\u003eScenario Ensemble.\u003c/b\u003e The highest quality wind and solar resources in Mercosur do not always form the most economically attractive projects, as particular areas may be protected, inaccessible, far from existing transmission, or crowded out by existing projects\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The technical potential of these resources is also subject to the characteristics of the technologies themselves, i.e., the power curve of a wind turbine or the solar PV tracking technology. Under regional coordination of the electricity grid, the locations of the best (most economical) candidate wind and solar PV projects may change and even cross international borders due to this uncertainty, affecting the trade dynamics of the system. As more renewable electricity capacity is built to meet decarbonization plans, the uncertainty in potential capacity expansion outcomes grows. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e describes the 80-member scenario ensemble developed to explore this uncertainty, which configures GridPath-Mercosur along four dimensions: level of regional electricity coordination, mid-century emissions policy, solar PV tracking type, and wind turbine characteristics.\u003c/p\u003e \u003cp\u003eThe mid-century emissions policy achieves a 90% reduction in the annual CO\u003csub\u003e2\u003c/sub\u003e emitted by the electricity system between 2020 and 2050; this policy is compared with a \u003cem\u003eReference\u003c/em\u003e case with no CO\u003csub\u003e2\u003c/sub\u003e emissions target. The \u003cem\u003eMitigation (90% CO2 Cut)\u003c/em\u003e case represents a deep decarbonization scenario generally consistent with reaching established end-of-century mitigation targets\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, for which net-zero is achieved shortly after 2050. The level of coordination in each realization is modeled as either \u003cem\u003eFull Coordination\u003c/em\u003e (transnational interconnection expansion is co-optimized in the capacity expansion problem; electricity trade is unrestricted) or \u003cem\u003eLimited Coordination\u003c/em\u003e (no new investment in transmission interconnections; gross electricity trade cannot exceed 2020 levels). Thus, the level of coordination represents both a willingness to invest in transmission and the overcoming of institutional barriers preventing increased trade. Wind and solar PV technology characteristics are used to estimate spatially resolved hourly capacity factors of candidate wind and solar projects throughout the region at 0.5-degree resolution\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, and to estimate project-specific capital costs\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Two types of solar tracking technology are included (\u003cem\u003eFixed\u003c/em\u003e vs. \u003cem\u003e1-axis tracking\u003c/em\u003e); the tradeoff between cost, efficiency, and land requirement among tracking technologies has been broadly identified in the literature, with individual case studies differing in their optimal selection of tracking type depending on the local context\u003csup\u003e\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Similarly, wind turbine output is also technology-specific and dependent on characteristics such as hub height and the turbine\u0026rsquo;s swept area. Here, we include hourly capacity factors of ten wind turbines to explore cost-performance tradeoffs. Refer to Table S6 and Table S7 for further details.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eScenario grouping for the 80-member ensemble run using GridPath-Mercosur. Each group contains ten scenarios: one for each wind turbine type (shown in Table S6).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScenario Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel of Coordination\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmissions Policy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSolar PV Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWind Turbine Characteristics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLimited Coordination\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eReference (No CO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eTarget)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1-axis tracking\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTurbines T1-T10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLimited Coordination\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eReference (No CO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eTarget)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eFixed\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTurbines T1-T10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLimited Coordination\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMitigation (90% CO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eCut)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1-axis tracking\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTurbines T1-T10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLimited Coordination\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMitigation (90% CO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eCut)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eFixed\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTurbines T1-T10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFull Coordination\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eReference (No CO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eTarget)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1-axis tracking\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTurbines T1-T10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFull Coordination\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eReference (No CO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eTarget)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eFixed\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTurbines T1-T10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFull Coordination\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMitigation (90% CO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eCut)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e1-axis tracking\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTurbines T1-T10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFull Coordination\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMitigation (90% CO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eCut)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eFixed\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTurbines T1-T10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData and Code Availability\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eModel input data and processed model output data will be deposited at Zenodo and publicly available as of the date of publication.\u003c/li\u003e\n\u003cli\u003eModel scenario outputs have been deposited at Zenodo and will be publicly available as of the date of publication at https://doi.org/10.5281/zenodo.15096839.\u003c/li\u003e\n\u003cli\u003eGridPath is an open source model accessible at https://doi.org/10.5281/zenodo.5822994; the version utilized in this work is located at https://github.com/blue-marble/gridpath/releases/tag/v0.8.1.\u003c/li\u003e\n\u003cli\u003eAll original code for data processing, analysis, and figure generation will be deposited at Zenodo and publicly available after publication.\u003c/li\u003e\n\u003cli\u003eAny additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eSupplemental Information Files\u003c/p\u003e\n\u003cp\u003eDocument S1. Text S1-S4, Tables S1-S7, and Figures S1-S20.\u003c/p\u003e\n\u003cp id=\"_Toc180052159\"\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThis material is based upon work supported by the National Science Foundation under Grant No. 1855982. The authors acknowledge the Tufts University High Performance Compute Cluster (https://it.tufts.edu/high-performance-computing) which was utilized for the research reported in this paper. T.W. and G.I. are also affiliated with Pacific Northwest National Laboratory, which did not provide specific support for this paper.\u003c/p\u003e\n\u003cp id=\"_Toc180052160\"\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eJ.W. and A.F.M.K.C. conceptualized the study, T.W. and J.L. acquired the funding, A.F.M.K.C. and J.W. developed the methodology and model framework, J.W. and A.F.M.K.C. curated the data, J.W. conducted the formal analysis, J.W., J.L., A.F.M.K.C., T.W., G.I., and F.K. wrote and edited the paper, J.L. and A.F.M.K.C. supervised the project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eClarke, L. et al. Energy Systems. In IPCC, 2022: Climate Change 2022: Mitigation of Climate Change. Contribution of Working Group III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (2022) doi:10.1017/9781009157926.008.\u003c/li\u003e\n\u003cli\u003eSachs, J. D. et al. 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In principle, national grids benefit from greater regional integration, but the nature of these benefits is sensitive to techno-economic uncertainty and natural resource distribution. We examine the value of regional electricity coordination for a South American subregion with abundant, geographically variable renewable resources, under stringent emissions reduction targets and a range of techno-economic assumptions. Results show decarbonization is achievable with modest cost premiums which are further mitigated by international coordination. Differences in renewables deployment across scenarios tend to offset system-wide; however, country-level variability suggests national decarbonization pathways are sensitive to technology characteristics. Achieving mitigation goals without coordination requires additional generation capacity, at more than triple the added cost of coordinated planning scenarios (\u003cspan\u003e$\u003c/span\u003e14.7-22.8B vs. \u003cspan\u003e$\u003c/span\u003e3.5-7.0B). Beyond South America, these results are relevant to regions looking to meet emissions targets through greater international cooperation.\u003c/p\u003e","manuscriptTitle":"Regional coordination can alleviate the cost burden of a low-carbon electricity system","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-15 08:01:31","doi":"10.21203/rs.3.rs-6505314/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"48554f86-74e9-4aed-beed-f4fc1e156b8a","owner":[],"postedDate":"May 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":48509676,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate-change mitigation"},{"id":48509677,"name":"Physical sciences/Energy science and technology/Energy modelling"},{"id":48509678,"name":"Scientific community and society/Energy and society/Energy supply and demand"},{"id":48509679,"name":"Physical sciences/Engineering/Energy infrastructure/Energy grids and networks"}],"tags":[],"updatedAt":"2025-10-11T07:07:58+00:00","versionOfRecord":{"articleIdentity":"rs-6505314","link":"https://doi.org/10.1038/s41467-025-64093-8","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2025-10-10 04:00:00","publishedOnDateReadable":"October 10th, 2025"},"versionCreatedAt":"2025-05-15 08:01:31","video":"","vorDoi":"10.1038/s41467-025-64093-8","vorDoiUrl":"https://doi.org/10.1038/s41467-025-64093-8","workflowStages":[]},"version":"v1","identity":"rs-6505314","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6505314","identity":"rs-6505314","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}