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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Grid-Integrated Electric Mobility Model (GEM) v1.0

Transportation is the fastest-growing source of greenhouse gas (GHG) emissions and energy consumption globally. The convergence of shared mobility, vehicle automation, and electrification has the potential to drastically reduce transportation impacts, but requires careful integration with rapidly evolving electricity systems. We have developed the GEM Model (Grid-Integrated Electric Mobility) to examine these interactions with a U.S.-wide simulation framework encompassing private electric vehicles (EVs); shared automated EVs (SAEVs); charging infrastructure; controlled EV charging; and a grid economic dispatch model to simulate mobility futures exclusively using EVs. We find that an SAEV fleet 9% the size of today's active vehicles can satisfy trip demand with only 2.6 million chargers (0.2 per EV). Controlled EV charging can also reduce electricity demand variability, significantly reducing GHG emissions and decreasing solar curtailment by about one-third. While private EVs with uncontrolled charging would reduce GHG emissions by 53% compared to gasoline vehicles, SAEVs could achieve a 70% reduction.

Sheppard, Colin↗
DOE OSTI2022

Hybrid-RL-MPC4CLR (Hybird-Reinforcement-Learning-Model-Predictive-Control-for-Reserve-Policy-Assisted-Critical-Load-Restoration-in-Distribution-Grids)

Hybrid-RL-MPC4CLR was developed as a hybrid controller for active distribution grid critical load restoration, combining deep reinforcement learning (RL) and model predictive control (MPC) aiming at maximizing total restored load following an extreme event. The RL determines a policy for quantifying operating reserve requirements, thereby hedging against uncertainty, while the MPC models grid operations incorporating the RL policy actions (i.e., reserve requirements), renewable (wind and solar) power predictions, and load demand forecasts. The developers formulated the reserve requirement determination problem as a sequential decision-making problem based on the Markov Decision Process (MDP) and design an RL learning environment based on the OpenAI Gym framework and MPC simulation. The RL agent reward and MPC objective function aim to maximize and monotonically increase total restored load and minimize load shedding and renewable power curtailment. The software is developed using various software packages in Python. The MPC's optimal power flow (OPF) model is implemented using the Pyomo package, the RL simulation environment is implemented using the MPC simulation with various scenarios of renewable energy and load demand profiles and power outage beginning times, based on the OpenAI Gym framework. The RL agent training is performed using the RLlib Ray package. The RL algorithm is trained offline using historical forecasts of renewable generation and load demand profiles. Simulation analysis and performance tests are conducted using a modified IEEE 13-bus distribution test feeder containing wind turbine, photovoltaic, microturbine, and battery.

Eseye, Abinet Tesfaye↗
DOE OSTI2024

Inhibition of the eukaryotic initiation factor-2α kinase PERK decreases risk of autoimmune diabetes in mice

Preventing the onset of autoimmune type 1 diabetes (T1D) is feasible through pharmacological interventions that target molecular stress–responsive mechanisms. Cellular stresses, such as nutrient deficiency, viral infection, or unfolded proteins, trigger the integrated stress response (ISR), which curtails protein synthesis by phosphorylating eukaryotic translation initiation factor-2α (eIF2α). In T1D, maladaptive unfolded protein response (UPR) in insulin-producing β cells renders these cells susceptible to autoimmunity. We found that inhibition of the eIF2α kinase PKR-like ER kinase (PERK), a common component of the UPR and ISR, reversed the mRNA translation block in stressed human islets and delayed the onset of diabetes, reduced islet inflammation, and preserved β cell mass in T1D-susceptible mice. Single-cell RNA-Seq of islets from PERK-inhibited mice showed reductions in the UPR and PERK signaling pathways and alterations in antigen-processing and presentation pathways in β cells. Spatial proteomics of islets from these mice showed an increase in the immune checkpoint protein programmed death-ligand 1 (PD-L1) in β cells. Golgi membrane protein 1, whose levels increased following PERK inhibition in human islets and EndoC-βH1 human β cells, interacted with and stabilized PD-L1. Collectively, our studies show that PERK activity enhances β cell immunogenicity and that inhibition of PERK may offer a strategy for preventing or delaying the development of T1D.

Research & Experimental Medicine↗
DOE OSTI2024

Two-Step Hyperparameter Optimization Method: Accelerating Hyperparameter Search by Using a Fraction of a Training Dataset

Abstract Hyperparameter optimization (HPO) is an important step in machine learning (ML) model development, but common practices are archaic—primarily relying on manual or grid searches. This is partly because adopting advanced HPO algorithms introduces added complexity to the workflow, leading to longer computation times. This poses a notable challenge to ML applications, as suboptimal hyperparameter selections curtail the potential of ML model performance, ultimately obstructing the full exploitation of ML techniques. In this article, we present a two-step HPO method as a strategic solution to curbing computational demands and wait times, gleaned from practical experiences in applied ML parameterization work. The initial phase involves a preliminary evaluation of hyperparameters on a small subset of the training dataset, followed by a reevaluation of the top-performing candidate models postretraining with the entire training dataset. This two-step HPO method is universally applicable across HPO search algorithms, and we argue it has attractive efficiency gains. As a case study, we present our recent application of the two-step HPO method to the development of neural network emulators for aerosol activation. Although our primary use case is a data-rich limit with many millions of samples, we also find that using up to 0.0025% of the data—a few thousand samples—in the initial step is sufficient to find optimal hyperparameter configurations from much more extensive sampling, achieving up to 135× speedup. The benefits of this method materialize through an assessment of hyperparameters and model performance, revealing the minimal model complexity required to achieve the best performance. The assortment of top-performing models harvested from the HPO process allows us to choose a high-performing model with a low inference cost for efficient use in global climate models (GCMs).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

The Future of Supercells in the United States

A supercell is a distinct type of intense, long-lived thunderstorm that is defined by its quasi-steady, rotating updraft. Supercells are responsible for most damaging hail and deadly tornadoes, causing billions of dollars in losses and hundreds of casualties annually. This research uses high-resolution, convection-permitting climate simulations across 15-yr epochs that span the twenty-first century to assess how supercells may change across the United States. Specifically, the study explores how late-twentieth-century supercell populations compare with their late-twenty-first-century counterparts for two—intermediate and pessimistic—anthropogenic climate change trajectories. An algorithm identifies, segments, and tracks supercells in the simulation output using updraft helicity, which measures the magnitude of corkscrew flow through a storm’s updraft and is a common proxy for supercells. Results reveal that supercells will be more frequent and intense in future climates, with robust spatiotemporal shifts in their populations. Supercells are projected to become more numerous in regions of the eastern United States, while decreasing in frequency in portions of the Great Plains. Supercell risk is expected to escalate outside of the traditional severe storm season, with supercells and their perils likely to increase in late winter and early spring months under both emissions scenarios. Conversely, the latter part of the severe storm season may be curtailed, with supercells expected to decrease midsummer through early fall. These results suggest the potential for more significant tornadoes, hail, and extreme rainfall that, when combined with an increasingly vulnerable society, may produce disastrous consequences.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A Computational Investigation of Engine Heat Transfer with Ducted Fuel Injection

Ducted fuel injection (DFI) is an innovative method that curtails or prevents soot formation in direct-injection compression-ignition engines. DFI uses a simple duct, positioned outside each injector hole, facilitating the fuel/charge gas mixing before ignition. This reduces the equivalence ratio below two, in the autoignition zone, which in turn decreases soot formation. But this method also reduces fuel-conversion efficiency. This study investigates the effects of DFI on in-cylinder heat transfer. Experiments with conventional diesel combustion (CDC) and DFI were performed at four different dilution levels. Computational fluid dynamics (CFD) simulations were carried out at conditions matching those of the experiments, and the simulations were validated by the experimental data. The CFD simulations enabled to examine of in-cylinder heat release and temperature distributions. The heat transfer to the piston, head, and cylinder was investigated. The results show that DFI increased the heat transfer to the walls compared to CDC under the same conditions. This could help explain why DFI has been observed to reduce fuel-conversion efficiency by approximately 1% (absolute) relative to CDC under certain conditions. The efficiency loss typically decreases with dilution, such that DFI can improve fuel-conversion efficiencies relative to CDC at higher dilution levels.

Şener, Ramazan↗
DOE OSTI2023

Parametric evaluation of ducted fuel injection in an optically accessible mixing-controlled compression-ignition engine with two- and four-duct assemblies

Ducted fuel injection (DFI) is a strategy to improve fuel/charge-gas mixing in direct-injection compression-ignition engines. DFI involves injecting fuel along the axis of a small tube in the combustion chamber, which promotes the formation of locally leaner mixtures in the autoignition zone relative to conventional diesel combustion. Previous work has demonstrated that DFI is effective at curtailing engine-out soot emissions across a wide range of operating conditions. This study extends previous investigations, presenting engine-out emissions and efficiency trends between ducted two-orifice and ducted four-orifice injector tip configurations. For each configuration, parameters investigated include injection pressure, injection duration, intake manifold pressure, intake manifold temperature, start of combustion timing, and intake-oxygen mole fraction. For both configurations and across all parameters, DFI reduced engine-out soot emissions compared to conventional diesel combustion, with little effect on other emissions and engine efficiency. Emissions trends for both configurations were qualitatively the same across the parameters investigated. The four-duct configuration had higher thermal efficiency and indicated-specific engine-out nitrogen oxide emissions but lower indicated-specific engine-out hydrocarbon and carbon monoxide emissions than the two-duct assembly. Both configurations achieved indicated-specific engine-out emissions for both soot and nitrogen oxides that comply with current on- and off-road heavy-duty regulations in the United States without exhaust-gas aftertreatment at an intake-oxygen mole fraction of 12%. High-speed in-cylinder imaging of natural soot luminosity shows that some conditions include a second soot-production phase late in the cycle. The probability of these late-cycle events is sensitive to both the number of ducted sprays and the operating conditions.

Yraguen, Boni Frances↗
DOE OSTI2026

Mu2e resonant extraction regulation system simulation in delivery ring

Mu2e is an upcoming experiment at Fermilab that relies on the slowly extracted 8 GeV proton beam from the Delivery Ring. The experiment imposes strong requirements on the spill uniformity. To address these requirements, the fast spill regulations system is being developed and commissioned. To inform this development and optimize the system performance we are carrying out the detailed simulations of the regulation process. The simulation includes the effect of six harmonic sextupoles that excite the third-integer resonance and three fast ramping quadrupoles that drive the horizontal tune to 29/3. The components of spill regulation system are designed to mitigate long-term drifts in the beam, ensuring stable operation over extended timescales, as well as addresses rapid variations within single spill. In this study, we review the regulation system design, simulation of the slow regulation, and the fast regulation PID regulation to curtail random variations in the extraction rate that could occur within a single spill.

Narayanan, Aakaash [Fermilab]↗
DOE OSTI2026

Loss Factors for Small Distributed Wind Turbines Based on Field Data in the United States

While wind energy production loss due to unavailability, environmental impacts, curtailment, and other causes has been studied and characterized at the utility-scale wind farm level, observation-based characterization of project loss is lacking for distributed wind energy, particularly for projects involving small wind turbines. Contemporary tools and research that support pre-construction distributed wind energy characterization present a wide range of default loss factors to convert gross energy estimates to net: 7-18%. We hypothesize that we can use generation observations from operational distributed wind projects to develop more accurate representations of loss. Using a density-based filtering technique on distributed wind power generation timeseries, we determine periods of typical performance and use them with regression algorithms in a measure-correlate-predict fashion to simulate what the generation would have been during periods of atypical or unreported performance. From there, the actual versus predicted generation leads to the establishment of observation-informed loss factors (median = 17%) for small, single turbine installation distributed wind projects.

17 WIND ENERGY↗
DOE OSTI2022

Offshore Oil and Gas Infrastructure Reuse for CCS: Opportunities and Challenges in the U.S. Gulf of Mexico

The United States Gulf of Mexico has historically been a significant oil and gas producing region and continues to produce hydrocarbons today. As such, significant investments have been made to install oil and gas infrastructure such as pipelines, platforms, and wells. As these assets reach the end of their useful life or production associated with the assets is curtailed, the assets become a liability for both the owner/operators and the U.S. and state governments responsible for regulating the assets. There are costs and risks associated with safely decommissioning the assets, and in some cases, the assets may be abandoned (e.g., the owner is no longer a going concern). In parallel, carbon capture and storage (CCS) technology has developed in the United States and interest and incentives for CCS projects have accelerated in recent years, with many public announcements of CCS projects in development in the U.S. Gulf of Mexico region. The region is particularly well-suited to CCS due to the high density of industry on the coastline (i.e., CO 2 sources) and favorable geology in the near-offshore region. CCS also requires the use of many of the same assets required for oil and gas production (pipelines, platforms, wells) and the oil and gas industry has direct experience with CO 2 transport and injection (i.e., EOR). Therefore, the combination of existing infrastructure in close proximity to potential CCS project sites appears to provide a potential “win-win” solution for re-use of existing oil and gas assets. This paper presents a high-level evaluation of oil and gas infrastructure in the Gulf of Mexico with the goals of identifying the scale of the opportunity (e.g., how much existing infrastructure is realistically re-usable), identifying gaps in knowledge/policy/information required to evaluate existing infrastructure for re-use, and proposing key actions that could be pursued to further the assessment of existing infrastructure re-use for CCS.

Infrastructure, reuse, re-purpose, oil and gas, pi↗
DOE OSTI2025

Dynamic Nanopeptide Assemblies for Trans-Tympanic Drug Delivery

Aim: Otitis media is a common otolaryngologic diagnosis worldwide. Invasive methods to curtail and treat frequent occurrences are undesirable, thus necessitating the identification and production of a non-invasive approach to treating the disease. Due to tympanic membrane thickness, ototopical drug delivery is challenging. In this preliminary study, formulations integrating nanopeptides and thermoresponsive polymeric hydrogels are utilized to improve the efficiency of trans-tympanic membrane drug delivery. Methods: Peptides were synthesized using standard Fmoc (fluorenylmethoxycarbonyl protecting group) based solid state peptide synthesis on an automated peptide synthesizer. Ciprofloxacin release was simulated using multiwell microplates with porous inserts. Rate of Ciprofloxacin release was measured over a 48-hour period using a 200 uL solution of peptide fibers and Ciprofloxacin at 1 wt% each, and the labeled peptide at 0.1 wt% in PBS at pH of 7.4. The cytotoxicity of the PA (peptide amphiphile, specifically c16-AHL 3 K 3 -CO 2 H) micelle and fiber with and without ciprofloxacin was investigated by examining epidermal keratinocyte viability in the presence of the material at various concentrations. Laser scanning confocal microscopy was performed with excitation of the calcein dye at 485 nm and the PA-TAMRA (rhodamine labeled peptide) at 515 nm. Results: We have demonstrated the potential viability of a self-assembled peptide amphiphile hydrogel capable of transitioning from a network of 1D nanoscale fibers to 0D micelles. This dissociative mechanism of action yields a peptide that is an effective cell penetrating peptide (CPP) while temporally controlling the release of the antibiotic ciprofloxacin. Conclusion: This work highlights the potential utility of the dynamic process of an engineered peptide hydrogel capable of dissociating into CPPs capable of facilitating drug delivery across the tympanic membrane.

cell culture↗
DOE OSTI2020

The Potential Impact of Offshore Wind Energy on a Future Power System in the U.S. Northeast

This study aims to understand and quantify the potential impact of offshore wind on a future electricity system in the U.S. Northeast. In this analysis, a detailed representation of the Northeast power system is adopted, using a generation portfolio for 2024 paired with offshore wind nameplate capacities of 0 gigawatts (GW), 2 GW, and 7 GW. The analysis identifies points of offshore wind interconnection in the ISO-New England and New York Independent System Operator control areas and uses hourly wind profiles from the Wind Integration National Dataset Toolkit. Hourly simulations of the 2024 power system operations show an ability to accommodate the prescribed offshore wind capacities by adapting the system's generation dispatch. Curtailment levels of offshore wind range between 4% and 5%. Offshore wind generation displaces primarily natural-gas combined-cycle generation; however, requires increased flexibility from combined cycles through more frequent start-ups. The number of hours with transmission congestion increases because of offshore wind injection, with varying impact on a subregional level. Offshore wind's capacity credit was found to be 14.5%-28.3% and is lower than estimated in other large-scale power system studies, in part because this study considered a different weather year and a power system representation with higher shares of solar PV and onshore wind. The reliability contribution of resources is a topic area identified for more research and collaboration. The 7-GW scenario shows a reduction in locational marginal price of 11%, with production cost savings of up to 18% compared to the 0-GW scenario.

17 WIND ENERGY↗
DOE OSTI2020

Predicting Solar Plant Generation with Machine Learning Techniques

Accurately predicting power generation for PV sites is critical for prioritizing relevant operations & maintenance activities, thereby extending the lifetime of a system and improving profit margins. A number of factors influence power generation at PV sites, including local weather, shading and soiling losses, design of modules, DC mismatches, and degradation over time. Other external factors such as curtailment and grid outages can also have a notable impact on power generation. Machine learning techniques can be used to provide more accurate predictions of PV power production by accounting for important weather and climate information neglected by current industry methods. This article will cover the deficiencies of those methods and will show how machine learning can dramatically improve power generation predictions.

14 SOLAR ENERGY↗
DOE OSTI2020

Exploring the Grid Value Potential of Offshore Wind Energy in Oregon

The grid value of Oregon's offshore wind resource is considered through the lenses of (i) resource complementarity with the hydroelectric system and other Variable Renewable Energy resources, (ii) load complementarity with the four balancing authorities with territory in Oregon, and (iii) spatial value to regional and coastal grids as represented through a Production Cost Model of the WECC system. Capacity implications of the interactions between offshore wind and the historical east-to-west power flows of the region are reviewed. The existing system is shown to accommodate 2GW of offshore wind interconnections without curtailment.

17 WIND ENERGY↗
DOE OSTI2020

CyDER: A Cyber Physical Co-simulation Platform for Distributed Energy Resources in Smartgrids

The CyDER project aimed at developing an open-source, modular and scalable co-simulation platform for power grids with large shares of Distributed Energy Resources (DERs). The project partners are the Lawrence Berkeley National Lab (LBNL), Lawrence Livermore National Lab (LLNL), PG&E, SolarCity, and ChargePoint. The prime recipient is LBNL; SolarCity and ChargePoint were partners for the project’s first two years. Increased DER integration introduces a number of challenges in power grid operation including a more dynamic interaction between the transmission grid and distribution grids, and increased modeling complexity. Although specialized software exists to precisely model different components of the power system, it is far from trivial to integrate all various models and perform a holistic simulation. Instead of replicating all models in a common simulation program, a commonly accepted approach to tackle this model diversity is to couple third-party simulators and models through a co-simulation platform that coordinates information exchange among the various components. Following this line of research, this project’s objective was to develop a co-simulation platform based on a widely accepted industrial standard called Functional Mockup Interface (FMI). Within this process, the project developed models compliant with the FMI standard, called Functional Mockup Units (FMUs), and used them to perform various operational and planning power system analyses. Relying and building upon an industrial standard is the main differentiation of this project compared with previous or parallel efforts in the co-simulation area. Particular emphasis was put on delivering software utilities to facilitate setting up and running co-simulations by end-users. Furthermore, a strong aspect of this project is demonstrating that co-simulation techniques can be used to perform Hardware-in-the-Loop (HIL) simulations that couple software components (e.g., simulated models) with hardware components (e.g., real devices such PV systems and batteries). The long-term goal of CyDER project is to help establish FMI as a powerful standard for co-simulation and promote adoption by electric utilities and other interested stakeholders. The main accomplishments of the project include the development of several FMUs including distribution and transmission grid models, PV inverters with Volt/Var/Watt controllers, batteries, and predictive optimal controllers. Additionally, a unique software package was developed, called SimulatorToFMU, which is capable of exporting any Python-driven simulator or Python script as an FMU. This is an important contribution towards establishing FMI as one of the main co-simulation standards, because more and more third-party programs for sub-system modeling and simulation are delivered with Python APIs. The CyDER platform was used to perform PV hosting capacity analyses in real utility feeders with and without smart inverter controls, battery storage, and EV charging. Smart inverter controls include conventional Volt/Var/Watt controls for reactive power support and active power curtailment, but also predictive controls that optimize the charging and discharging profile of the battery connected on the DC side in order to minimize the customer’s economic benefit. Finally, an important result of this project is delivering an experimental setup that consists of residential-scale PV inverters with battery storage, a real-time grid simulator with an ideal voltage source as grid emulator, and micro Phasor Measurement Units (PMUs). All these components and additional software modules are coupled to one another using the FMI standard and can be co-simulated with the CyDER platform.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Screening IMS Seismic Detections using Dynamic Correlation Processor: Progress report for 2020

LLNL completed the FY20 workplan entailing application of the Dynamic Correlation Processor (DCP) to International Monitoring System (IMS) primary seismic data in 2019 and 2020. The effective analysis period was curtailed to January 1 through November 12, 2019 due to a data compression change that rendered later data unusable. Fixing the data compression error is a priority for our data source, and we plan to extend the period of data analysis when the fix is implemented. We found that the percentage overlap between detections generated by the International Data Centre (IDC) DFX software and detections generated by DCP varied widely by station. Overlap is 30-40% at some stations, suggesting that a large percentage of DFX detections could be screened (removed) from the detection-event association algorithm. However, detection overlap was much lower, a few percent, at many stations and the percentage overlap was low at stations known to have many mines and other local sources of seismicity. Unexpectedly low detection overlap at many stations prompted us to examine results for the ARCES station in detail. There is considerable mine activity near ARCES, but our initial FY20 analysis found only ~2% DCP/DFX detection overlap. Differences in the pre-processing (e.g. beam recipes) used by DCP and DFX appear to be the cause of low detection overlap at ARCES. DCP uses wideband filters to improve detector robustness and the IDC uses narrow-band pre-filtering to improve sensitivity. After reprocessing ARCES using the IDC beam recipe, DCP/DFX overlap increased to ~74%. This result shows that we must reprocess the IMS network using the IDC station-specific beam recipes if we are to effectively screen DFX detections and improve automatic event building performance at the IDC.

58 GEOSCIENCES↗
DOE OSTI2020

Electric Power Grid and Natural Gas Network Operations and Coordination

In this report, we present findings from three studies related to the coordination of natural gas and electricity system operations. We first propose and demonstrate a modeling platform for examining the interdependence of natural gas and electricity networks based on a direct current unit-commitment and economic dispatch model for the power system and a transient hydraulic gas model for the gas system. We use this platform to analyze the value of day-ahead coordination of power and natural gas network operations and to show the importance of considering gas system constraints when analyzing power systems operation with high penetration of gas generators and variable renewable energy sources. In the second study, we utilize our modeling platform to consider the U.S. Federal Energy Regulatory Commission (FERC) Order 809, issued in 2015 to improve day-ahead and intraday coordination of power and gas systems. Finally, in the third study we expand our modeling platform to focus on market-based coordination of electricity and natural gas system operations for a real system, namely a subset of the power and gas networks in the Front Range region of Colorado. We use real system data to evaluate the benefits of coordination operations under different conditions, including different levels of renewable penetration and the use of time-variant, shaped flow nominations. Our results indicate that coordination at various timescales can contribute to a reduction in curtailed gas in high-stress periods (such as those with large ramps in gas offtakes) and a reduction in energy consumption of gas compressor stations. We find that intraday coordination can reduce total power system production costs and natural gas deliverability constraints, yielding cost and reliability benefits. We observe these benefits for the test system as well as in the Colorado case study, where we find that coordination and shaped flows may provide additional value for systems with high penetration of variable renewable energy. Together, these three studies demonstrate a pathway for integrated gas and electricity grid modeling and for studying the benefits of coordinated operations of these increasingly interdependent energy systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Ready for Renewables: Grid Planning and Competitive Renewable Energy Zones (CREZ) in the Philippines

The Department of Energy of the Philippines launched the competitive renewable energy zones (CREZ) process to help achieve the country's goals of scaling up renewable energy generation on the power system and to ensure sustainable, secure, reliable, accessible, and affordable energy. The CREZ process identifies the most economic renewable energy resource areas so transmission planning and expansion can accelerate their development. By proactively focusing transmission expansion to these resource areas, renewable energy generation development obstacles such as transmission access, energy curtailment, land permitting (such as protected or high-slope areas), and regulatory barriers are easier to overcome thus, reducing risk for private sector renewable energy investment. This report is will cover the outputs of the CREZ process, the development of the outputs through stakeholder-driven coordination, and how this process will help the Philippines achieve their renewable energy goals.

24 POWER TRANSMISSION AND DISTRIBUTION↗