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At least 793 records · Page 44

Energy Storage Requirements for Achieving 50% Penetration of Solar Photovoltaic Energy in California

We estimate the storage required to enable PV penetration up to 50% in California (with renewable penetration over 66%), and we quantify the complex relationships among storage, PV penetration, grid flexibility, and PV costs due to increased curtailment. We find that the storage needed depends strongly on the amount of other flexibility resources deployed. With very low-cost PV (three cents per kilowatt-hour) and a highly flexible electric power system, about 19 gigawatts of energy storage could enable 50% PV penetration with a marginal net PV levelized cost of energy (LCOE) comparable to the variable costs of future combined-cycle gas generators under carbon constraints. This system requires extensive use of flexible generation, transmission, demand response, and electrifying one quarter of the vehicle fleet in California with largely optimized charging. A less flexible system, or more expensive PV would require significantly greater amounts of storage. The amount of storage needed to support very large amounts of PV might fit within a least-cost framework driven by declining storage costs and reduced storage-duration needs due to high PV penetration.

14 SOLAR ENERGY↗

Economic Dispatch for DC-Connected Battery Systems on Large PV Plants

Falling costs of energy storage combined with an increasing need to mitigate variable generation integration issues is leading to more PV projects that include batteries. Large PV plants that include batteries face challenges optimizing the value of the power they produce, especially when the batteries are DC-coupled and otherwise clipped power (due to high DC/AC ratio) is available. Multiple charging times and sources must be considered, including: grid charging, charging from PV that would otherwise be exported to the grid, and charging from clipped PV power. Then discharge schedules must be optimized against time-of-day pricing. The National Renewable Energy Laboratory in partnership with Southern Company has developed a heuristic algorithm in the System Advisor Model (SAM) which strategically charges the battery from PV and discharges the battery depending on the time-of-day prices of the PPA, the forecast PV production, and the estimated wear cost of the battery. The algorithm and case study results from a generic plant will be presented with recommendations for future work.

25 ENERGY STORAGE↗

Investigating the Correlation Between Wind and Solar Power Forecast Errors in the Western Interconnection: Preprint

Wind and solar power generations differ from conventional energy generation because of the variable and uncertain nature of their power output. This variability and uncertainty can have significant impacts on grid operations. Thus, short-term forecasting of wind and solar generation is uniquely helpful for power system operations to balance supply and demand in an electricity system. This paper investigates the correlation between wind and solar power forecasting errors.

14 SOLAR ENERGY↗

Enhancing Distribution System Resilience: A First-Order Meta-RL Algorithm for Critical Load Restoration

The increasing frequency of extreme events and the integration of distributed energy resources (DERs) into modern grids have elevated the need for resilient and efficient critical load restoration strategies in distribution systems. However, the stochastic nature of renewable DERs, limited energy resource availability and the intricate nonlinearities inherent in complex grid control problem make the problem challenging. Although reinforcement learning (RL) and warm-start RL methods have shown promising results, their performance often falls short in rapidly adapting to new, unseen situations and typically requires exhaustive problem-specific tuning. To address these gaps, we propose a First-Order Meta-based RL (FOM-RL) algorithm within an online framework for adaptive and robust critical load restoration. By harnessing local DERs as the enabling technology, FOM-RL allows the RL agent to swiftly adapt to new unseen scenarios by leveraging previously acquired knowledge of different tasks. Experimental results provide evidence that proposed algorithm learns more efficiently and showcases generalization capabilities across diverse set of operational scenarios. Moreover, a rigorous theoretical analysis yields a tight sublinear regret bound, sensitive to temporal variability, with a task-averaged optimality gap bounded by O(VM+D*/(Tsquare root(M))). These results suggest that optimality improves with task similarity and an increased number of tasks M, reaffirming the efficacy and scalability of the proposed approach in addressing the complexities of critical load restoration in distribution systems.

complexity theory↗

New Safety Feedback Control Design to Guarantee Adequate Frequency Performance in Microgrids

Safety analysis of power systems is concerned with the system's ability to maintain critical variables within specified limits following a disturbance. Frequency control adequacy has become increasingly important as the system inertia decreases due to the increase in renewable energy penetration. Various controllers for inverters have been proposed to improve the system frequency response and few are capable to ensure the safety of the response. In this article, a diesel-wind energy system is considered and modeled as a switching system between normal, faulted, and post-fault modes. Further, a safety feedback controller is designed as a supplementary signal for a wind turbine generator such that the speed of the diesel generator stays within a permissible range in the presence of a finite energy disturbance. Numerical results on the modified 33-bus microgrid system obtained of the proposed novel approach indicate that the suggested control configuration can guarantee adequate frequency response without excessive conservativeness.

barrier function↗

Design and Operation of a Sensible Heat Peaking Unit for Small Modular Reactors

Approximately 19% of the electricity produced in the United States comes from nuclear power plants. Traditionally, nuclear power plants, as well as larger coal-fired plants, operate in a baseload manner at or near steady-state for prolonged periods of time. Smaller, more maneuverable plants, such as gas-fired plants, are dispatched to match electricity supply and demand above the capacity of the baseload plants. However, air quality concerns and CO2 emission standards has made the burning of fossil fuels less desirable, despite the current low cost of natural gas. Wind and solar photovoltaic (PV) power generation are attractive options due to their lack of carbon footprint and falling capital costs. Yet, these renewable energy sources suffer from inherent intermittency. This inherent intermittency can strain electric grids, forcing carbon-based and nuclear sources of energy to operate in a load follow mode. For nuclear reactors, load follow operation can be undesirable due to the associated thermal and mechanical stresses placed on the fuel and other reactor components. Various methods of Thermal Energy Storage (TES) can be coupled to nuclear (or renewable) power sources to help absorb grid variability caused by daily load demand changes and renewable intermittency. Our previous research has shown that coupling a sensible heat TES system to a Small Modular Reactor (SMR) allows the reactor to run at effectively nominal full power during periods of variable electric demand by bypassing steam to the TES system during periods of excess capacity. In this study we demonstrate that this stored thermal energy can be recovered, allowing the TES system to act as a peaking unit during periods of high electric demand, or used to produce steam for ancillary applications such as desalination. For both applications the reactor is capable of operating continuously at approximately 100% power.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

National Climate Database (NCDB)

The National Climate Database (NCDB) is a high resolution, bias-corrected climate dataset consisting of the three most widely used variables of solar radiation- global horizontal (GHI), direct normal (DNI), and diffuse horizontal irradiance (DHI)- as well as other meteorological data. The goal of the NCDB is to provide unbiased high temporal and spatial resolution climate data needed for renewable energy modeling. The NCDB is modeled using a statistical downscaling approach with Regional Climate Model (RCM)-based climate projections obtained from the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX; linked below). Daily climate projections simulated by the Canadian Regional Climate Model 4 (CanRCM4) forced by the second-generation Canadian Earth System Model (CanESM2) for two Representative Concentration Pathways (RCP4.5 or moderate emissions scenario and RCP8.5 or highest baseline emission scenario) are selected as inputs to the statistical downscaling models. The National Solar Radiation Database (NSRDB) is used to build and calibrate statistical models.

Array↗

A review of tidal energy—Resource, feedbacks, and environmental interactions

The ocean contains a variety of renewable energy resources, little of which has been exploited. Here, we review both tidal range and tidal stream energy, with a focus on the resource, feedbacks, and environmental interactions. The review covers a wide range of timescales of relevance to tidal energy, from fortnightly (spring-neap) and semi-diurnal variability, down to array, and device-scale turbulence. When simulating the regional tidal energy resource, and to assess environmental impacts, it is necessary to account for feedbacks between the tidal array and the resource itself. We critically review various methods for simulating energy extraction, from insights gained through theoretical studies of “tidal fences” in idealized channels, to realistic three-dimensional model studies with complex geometry and arrays of turbines represented by momentum sinks and additional turbulence due to the presence of rotors and support structures. We discuss how variability can be reduced by developing multiple (aggregated) sites with a consideration of the enhanced phase diversity offered by exploiting less energetic tidal currents. This leads to future research questions that have not yet been explored in depth at first-generation tidal sites in relatively sheltered channels (e.g., the interaction of waves with currents). Such enhanced understanding of real sea conditions, including the effects of wind and waves, leads to our other identified primary future research direction—reduced uncertainties in turbulence predictions, including the development of realistic models that simulate the interaction between ambient turbulence and the turbulence resulting from multiple wakes, and changes to system-wide hydrodynamics, water quality, and sedimentation.

Environmental impacts↗

The National Solar Radiation Database (NSRDB) Fiscal Years 2019-2021(Final Report)

The National Solar Radiation Database (NSRDB) is the leading public source of high-resolution solar resource data in the United States, with more than 166,000 users annually. This database represents the state of the art in satellite-based estimation of solar resource information and uses a unique physics-based modeling approach that enables improvements in accuracy with the deployment of the next-generation geostationary satellites. Making the highest quality, state-of-the-art, regularly updated data sets available on a timely basis for users reduces costs of solar deployment by providing accurate information for siting studies and system output prediction, and thereby reduces levelized cost of energy. Also, high-resolution information from the NSRDB enables moving beyond levelized cost of energy when valuing the impact of renewables on the grid. Additionally, the NSRDB enables the integration of large amounts of solar on the grid by providing critical information about solar availability and variability that is used to enhance grid reliability and power quality.

14 SOLAR ENERGY↗

Stochastic Adaptive Droop Control in Frequency Regulation of Power Systems With Intermittent Generators

Modern power systems (MPSs), including microgrids (MGs), are increasingly incorporating multiple renewable energy sources (RESs) such as wind and solar power, as well as battery storage and controllable loads. While environmentally beneficial, these sources pose challenges for control and management due to their intermittent and stochastic nature, especially in maintaining frequency stability with multiple interconnected generators of varying capacities. Traditional droop control methods are effective in systems with generators that are dispatchable and have fixed generation capacities, but they fall short when applied to systems with RESs, where generation capacities are dynamic and affected by unpredictable environmental conditions. To address these challenges, this paper introduces a novel stochastic adaptive droop control (SADC) method for load frequency control (LFC). The proposed method adapts droop coefficients in real time, based on the measured stochastic data of power generation capacities, enabling more effective frequency regulation in systems with variable and intermittent power generation. Unlike traditional adaptive control methods, which assume constant or slowly-varying system parameters, this approach accounts for stochastic processes by modeling them as Markov chains, enabling robust performance under highly dynamic and unpredictable conditions. The key contributions of this work include the development of real-time droop coefficient adaptation algorithms, derivation of their stability and convergence properties, and the demonstration of the advantages of the method through simulations. Case studies highlight the improved performance of frequency regulation, particularly in addressing the impact of stochastic weather conditions and the benefits of reducing dependence on battery reserves in dealing with intermittency of RESs. Finally, this paper provides a comprehensive analysis of the theoretical foundations of the method, as well as practical implementation insights for future power systems with high penetration of RESs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

National Open-ocean Energy Laboratory (Final Technical Report)

Under the authorization provided by Section 634 of the Energy Independence and Security Act of 2007 (P.L. 110-140), in 2009 FAU was awarded U.S. Congressionally Directed Program (CDP) funding through the U.S. Department of Energy (DOE) to investigate and develop technologies to harness the energy of the Florida Current as a source of clean, renewable, base-load power for Florida and the U.S. A second CDP award in 2010 provided additional funding in order to enhance and extend FAU’s activities. These two CDPs in 2009 and 2010 were combined into a single DOE grant, DE-EE0000319, and are the subject of this report. Subsequently, in July 2010 funding was made available under a separate contract, DE-EE0004200. Under that funding, DOE’s Wind and Water Power Program designated FAU’s state of Florida marine renewable energy (MRE) center as the Southeast National Marine Renewable Energy Center (SNMREC). This report discusses SNMREC activities funded by the DE-EE0000319 grant, but will make reference, as appropriate, to activities that require further investigation under the follow-on grant. The concept of extracting energy from the motions of the oceans has a long history. However, implementation on large scales of the technologies to effect renewable energy recovery from waves, tides, and open-ocean currents is relatively recent. DOE’s establishment of SNMREC recognizes a significant potential for ocean current energy recovery associated with the (relatively) high-speed Florida Current, the reach of the Gulf Stream System flowing through the Straits of Florida, between the Florida Peninsula and the Bahamas Archipelago. The proximity of the very large electrical load center of southeast Florida’s metropolitan area to the resource itself makes this potential all the more attractive. As attractive as this potential energy source is, it is not without its challenges. Although the technology is conceptually simple, its design and implementation in a commercially-viable fashion presents a variety of challenges. Beyond the technology itself (and, especially, the effects on the technology of the harsh oceanic environment), it is important to consider the possible environmental impacts of commercial-scale implementation of oceanic energy extraction. Further, because such implementation represents a completely new undertaking, the human resources required do not exist, so education and training programs are critical to eventual success. This project, establishing a national open-ocean energy laboratory, was designed to address each of these three challenges in a flexible framework allowing for adaptive management as the project proceeded. In particular: the technology challenge, including resource assessment, evolved during the project to recognize and address the need for a national testing facility in the ocean for small-scale prototype MRE systems developed by industry; the environmental challenge became formalized and expanded during the permitting process for such a testing facility; and the human resources/societal challenges, both in terms of the need for education and training and in terms of public acceptance of MRE, stimulated a robust outreach program far beyond that originally envisioned at SNMREC. While all of these activities at SNMREC are ongoing, a number of significant milestones (in addition to the contributions listed in the appendices) were achieved under the auspices of this award. These include: Planning and site selection for the first-phase test facility, offshore of Dania Beach, FL, including some equipment for the facility, submission of an Interim Policy Lease Application to the U.S. Department of Interior’s Bureau of Ocean Energy Management (BOEM), and completion of an Environmental Assessment by BOEM and a positive Consistency Determination by the State of Florida; Measurements using acoustic profilers of the current structure and variability in the vicinity of the site under a variety of weather conditions, seasons and time durations; Design and implementation of instrumentation for the first phase of the offshore testing facility, the wet- and top-side data acquisition systems, and shore-based analysis systems; Implementation of a laboratory-scale dynamometer system to test generators of up to 25 kW capacity using real-world (simulated) forcing; Completion of 24 months of (airborne) marine vertebrate surveys and associated analysis of sea turtle offshore activity, marine mammal vocalization research, and ocean current turbine hydrodynamic noise characterization; Development of a secondary-school (nominally grade 10) curriculum about hydrokinetic MRE, “Energy from the Oceans: The New Renewable”, and training of over 200 high-school teachers in its use and in how to educate their colleagues in application of the material in the classroom; Presentations to over 50 interested civic groups in the region on various aspects of MRE in SE Florida A series of public lectures to over 600 residents of south Florida to provide broader education on MRE. Development of an interactive kiosk for installation in local science museums. These, and other accomplishments detailed in this report contribute to a comprehensive ongoing program at the SNMREC to support the affordable, responsible, and achievable commercialization of MRE. Many of the tasks of this award are continued or will be verified with follow-on funding DE-EE0004200, and its goal: the installation of the world’s first offshore ocean current turbine testing and validation capability.

16 TIDAL AND WAVE POWER↗

Data-Driven Day-Ahead PV Estimation Using Autoencoder-LSTM and Persistence Model

Inherent variability in photovoltaic (PV) and associated impacts on power systems is a challenging problem for both the PV owners and the grid operators. Existing statistical and machine learning algorithms typically work well for weather conditions similar to historical data. Furthermore, uncertain weather conditions pose a great challenge to the estimation accuracy of the estimation models. With the enhanced integration of intelligent electronic devices and the realization of associated automation in the power grid, renewable energy data is becoming more accessible, which can be utilized by deep learning models and improve the PV power generation estimation accuracy. In this paper, a hybrid deep learning model driven by external weather data is proposed to do day-ahead PV output forecasting at 15-minute-interval. The proposed model is motivated by the recent advancement of Long-Short-Term-Memory (LSTM) networks and AutoEncoder (AE), which estimates uncertainties in sequence while making the prediction for complex weather conditions. Meanwhile, the persistence model (PM) is used to predict continuous sunny weather conditions. The forecasting result is validated with data from multiple locations

42 ENGINEERING↗

Using Supercomputers to Speed Execution of the CAISO/PLEXOS 33% RPS Study

The study's official title is 'ISO Study of Operational Requirements and Market Impacts at 33% Renewable Portfolio Standard (RPS).' The stated objectives are twofold: (1) identifying operational requirements and resource options to reliably operate the ISO-controlled grid under a 33% RPS in 2020; and (2) inform market, planning, and policy/regulatory decisions by the ISO, state agencies, market participants, and other stakeholders. The first of these objectives requires the hourly estimates of integration requirements, measured in terms of operational ramp, load following and regulation capacity and ramp rates, as well as additional capacity to resolve operational violations. It also involves consideration of other variables that affect the results, such as the impact of different mixes of renewable technologies, and the impact of forecasting error and variability. The second objective entails supporting the CPUC to identify long-term procurement planning needs, costs, and options, as well as informing other decisions made by the CPUC and state agencies. For the ISO itself this includes informing state-wide transmission planning needs for renewables up to a 33% RPS, and informing design of wholesale markets for energy and ancillary services to facilitate provision of integration capacities. The study is designed in two phases. The first (current) phase is focused on operational requirements and addressing these requirements with existing and new conventional fossil generation; for instance, gas turbines and/or combined cycle units. The second (planned) phase will address the same operational requirements with a combination of conventional fossil generation resources, new non-generation resources, and a renewable resource dispatch. There are seven different scenarios considered in the current phase: a 20% RPS reference case; four 33% RPS cases (a reference case, a high out-of-state case, a high distributed generation case, and a low load case); an alternative 27.5% RPS case; and an all-gas case (no new renewables after 2008). In addition, the CPUC is planning a new set of cases that will alter the anticipated sets of runs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

The Eastern Renewable Generation Integration Study: Insights on System Stress: Preprint

The Eastern Renewable Generation Integration Study (ERGIS) explores the operational impacts of the wide spread adoption of wind and solar photovoltaics (PV) resources in North America's Eastern and Quebec Interconnections. We explore the impact of large scale adoption of wind and solar generation on the unit commitment and economic dispatch of the largest coordinated power system in the world by simulating hourly and five-minute operations. Using NREL's high-performance computing capabilities and new methodologies to model operations, we found that the modeled system, as simulated with evolutionary change in 2026, could balance the variability and uncertainty of wind and solar PV at a five-minute level under a variety of conditions. Our simulations achieve instantaneous penetrations that exceed 50% of load while meeting an annual penetration of 30% on an energy basis. The system meets balanced load and supply in all intervals, with modest curtailment, using technologies and practices that are widely available today. However, a variety of the conditions present in these simulations deviate substantially from historical practice. In this work, we analyze potentially stressful system conditions that occur in the simulations and identify opportunities for innovation, regulatory reform, and changes in operating practices that require further analysis to enable the transition to a system with more wind and solar PV.

24 POWER TRANSMISSION AND DISTRIBUTION↗

An energy-limited model of algal biofuel production: Toward the next generation of advanced biofuels

Algal biofuels are increasingly important as a source of renewable energy. The absence of reliable thermodynamic and other property data, and the large amount of kinetic data that would normally be required have created a major barrier to simulation. Additionally, the absence of a generally accepted flowsheet for biofuel production means that detailed simulation of the wrong approach is a real possibility. This model of algal biofuel production estimates the necessary data and places it into a heuristic model using a commercial simulator that back-calculates the process structure required. Furthermore, complex kinetics can be obviated for now by putting the simulator into energy limitation and forcing it to solve for the missing design variables, such as bioreactor surface area, productivity, and oil content. The model does not attempt to prescribe a particular approach, but provides a guide towards a sound engineering approach to this challenging and important problem.

09 BIOMASS FUELS↗

Situational awareness-enhancing community-level load mapping with opportunistic machine learning

Motivated by present and forthcoming challenges in the adoption and integration of distributed renewable energy, we develop a machine learning (ML) approach that builds short-fuse mappings connecting the occasionally-unobservable true load in one target community with information-rich signals collected from relatively more instrumented reference communities. Our setting is inspired by and tailored to target communities with significant unobservable behind-the-meter solar generation, where true load (a relatively well-behaved quantity of interest to grid operators) is hard to discern during daytime due to insufficient instrumentation and/or privacy reasons, but that can be related to reference communities with low unobservable distributed variable generation or with sufficient instrumentation. The developed mapping, herein realized with Support Vector Machine regression, is built using nighttime data from all communities, when their distributed generation is low or zero. Our ML algorithm opportunistically learns to correlate signals of interest and then is operationally used the next day to shed light into target community load evolution. The mapping is subsequently rebuilt, rolling its short-fuse scope perpetually forward in time. Here, we demonstrate the efficacy of our approach on nine synthetically generated topologies and associated timeseries stemming from real-world data, on which we observe cumulative error performance that yields lower than 10% and 15% daily-averaged mean absolute percentage errors in target community load estimation on more than about 75% and 90% of days, respectively, in multiple yearly evaluations that shed light on long-term performance also under seasonal and one-off effects. The proposed ML-powered methodology can offer grid operators much-improved visibility into a previously obscure space and can also serve as an additional source of information in broader, multi-modal solar disaggregation solutions.

14 SOLAR ENERGY↗

Energy storage in combined gas-electric energy transitions models: The case of California

California’s vision for a net-zero future by 2045 relies heavily on variable renewable energy systems. Thus, energy storage - particularly long-duration storage - could play a fundamental role in reliably supplying low-carbon electricity. We study energy storage using the BRIDGES model, a combined gas-electric capacity expansion model for California across multiple investment periods (2025-2045), modeled with progressively decreasing carbon emission targets to a zero emissions by 2045. This least-cost optimization model includes renewable gas production via power-to-gas, long-term storage of energy in gaseous form, electric energy storage such as through batteries and hydrogen storage, and renewable energy generation, all with capacity tracking and investment. Multiple scenarios are evaluated to examine the sensitivity of the optimal storage portfolio to system-level and sector-level parameters. The scenario results show that all electric energy storage systems - which vary in storage duration - are deployed and required in a net-zero California in 2045, amounting to around 75 GW of storage capacity. Lithium ion systems make up approximately 80% of this power capacity and supply most short-run storage needs. Hydrogen storage - in the form of a power-to-gas-to-power system - emerges as a replacement to conventional natural gas storage, comprising most of the total energy storage capacity (~ 4 TWh). This capacity is less than 5% of the current natural gas storage capacity (94 TWh), indicating sufficient room for repurposing part of the gas infrastructure. A demand-side sensitivity analysis proves that higher electricity demand correlates with more builds of Li-ion batteries, while higher industrial heat demand leads to more builds of long-duration storage systems in a net-zero economy. Furthermore, power-to-gas systems satisfy part of the industrial heat demand by locally supplying renewable gas, which overtakes the traditional centralized gas storage and transfers through pipelines, casting significant doubts on the future of the large-scale gas infrastructure.

03 NATURAL GAS↗

Modeling the value of integrated U.S. and Canadian power sector expansion

The U.S.-Canadian power system has evolved into a highly integrated grid. Cross-border transmission and coordination of system operations create an interconnected power system with combined imports and exports of electricity of greater than 77 TWh per year. Currently, more than 5 GW of new international transmission lines are in various stages of permitting and development. These transmission lines may enable even greater integration and coordination of the U.S. and Canadian systems, which can in turn increase the reliability and flexibility of North America's electricity grid and help address challenges associated with integrating high levels of variable renewables. Using a version of the National Renewable Energy Laboratory's Regional Energy Deployment System (ReEDS) model that incorporates Canada, this analysis quantifies the differences in the evolution of the power system under scenarios in which cross-border transmission capacity is restricted to today's levels, and scenarios in which new transmission is less restricted. These impacts are analyzed under a 'business-as-usual' reference scenario and a scenario in which deep cuts in power sector carbon dioxide emissions levels are achieved. A set of key impact metrics is analyzed, including 1) the composition of generating capacity by technology, 2) system costs, 3) wholesale electricity prices, 4) international electricity exports and imports, 5) transmission capacity, and 6) carbon dioxide emission levels. When new cross-border transmission is not allowed, the United States needs additional capacity (primarily natural gas and renewable energy) to meet domestic needs, while total Canadian capacity is lower because less capacity is needed to export to the United States. This effect is amplified under the carbon cap scenario. Impacts vary on a regional basis, largely due to the different relative sizes of the generation portfolio between countries and regions and the relative impact from cross-border electricity trade. The total impact from restricting cross-border trade on carbon emissions and average wholesale electricity prices is limited, due to the relative size of the domestic power systems and the cross-border trade volume. Lastly, cross-border transmission capacity is projected to more than double under the unrestricted transmission capacity scenarios, which exceeds the rate of projected domestic transmission capacity additions in each country.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗