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DOE OSTI2025

Transforming Energy Through Computational Excellence: A View From NREL

At the National Renewable Energy Laboratory (NREL)—a U.S. Department of Energy laboratory—computational science, high-performance computing, applied mathematics, advanced computer science, visualization, and data play a pivotal role in advancing energy abundance, affordability, security, and reliability. From fundamental scientifc discovery to systems engineering and analysis, NREL researchers tackle market-relevant challenges to develop solutions for an independent energy system that is reliable, resilient and secure. Collaborative partnerships with industry, government, and academia ensure that our research remains cutting edge, impactful, applicable, and aligned with real-world energy needs. This special issue of Computing in Science & Engineering highlights exemplary NREL projects where computational tools and methodologies drive discovery and accelerate innovation in scalable and integrated energy systems. The featured articles explore the role of computational modeling, high-performance computing, generative AI, and adaptive computing in advancing independent energy solutions, optimizing sustainability research, and enhancing decision-making for energy solutions using a broad mix of energy technologies. Here, these contributions demonstrate how NREL’s computational research bridges the gap between theoretical advancements and practical implementation, emphasizing interdisciplinary collaboration and a commitment to innovation, with a focus on translating computational excellence into real-world impact, thus accelerate progress toward national energy goals. By showcasing cutting-edge research at the intersection of computational science and energy systems, this issue aims to inspire and inform researchers, practitioners, and policymakers dedicated to shaping a more reliable energy future.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Impacts of Spatial Resolution in a High-Fidelity Capacity Expansion Model: An ERCOT Case Study

Capacity expansion models are important tools in examining the evolution of the electric power sector. Embedded in these tools are many modeling choices with consequential impacts on computational burden and associated analysis. In this study, we adjust the spatial resolution of the Regional Energy Deployment System (ReEDS) to understand the implications of higher-fidelity modeling on energy system projections and model solve times. The native ReEDS regions capture the contiguous United States in 134 balancing areas whereas the regions in the higher-resolution version are defined by over 3,000 U.S. counties. Using both resolutions, we conduct a case study of the Texas Interconnection (The Electric Reliability Council of Texas [ERCOT]) to explore differences in model projections and to inform appropriate applications of high spatial resolution in a large-scale, applied capacity expansion model.

county↗

DOE OSTI2026

Flash electropolishing for TEM: Reducing FIB‐induced defects in tungsten with protocols for new materials

Focused ion beam (FIB) milling has become the dominant approach for site-specific transmission electron microscopy (TEM) specimen preparation; however, FIB damage remains a critical limitation for reliable microstructural characterisation, particularly in radiation effects studies. Tungsten is especially susceptible to FIB damage due to its high nuclear stopping power, which promotes the formation and strong diffraction contrast of FIB-induced ‘black spot’ defects that are indistinguishable from very fine irradiation-induced loops/defects resulting from low to intermediate temperature neutron irradiation. In this work, flash electropolishing is systematically evaluated as a post-FIB treatment for minimising preparation-induced artefacts for TEM analysis of tungsten-based alloys. Using a range of non-, ion-, and neutron-irradiated tungsten materials, the effectiveness of flash electropolishing has been assessed through direct comparison with conventional FIB and plasma-FIB preparation including low-energy Ga, Ar, Xe ion cleaning. The results demonstrate that flash electropolishing effectively removes FIB-damaged layers and ‘black spot’ defects, thereby enabling reliable observation of irradiation-induced dislocation structures. Key processing parameters governing flash electropolishing quality – including lamella thickness, applied voltage, polishing duration, electrolyte chemistry, and cathode geometry – have been systematically evaluated, and clear criteria were established for determining when flash electropolishing is required to ensure reliable microstructural analysis. This work also provides practical guidance for implementing flash electropolishing as an artefact-controlled specimen-preparation approach for TEM characterisation of FIB-produced specimens. The systematic protocol can be extended to other, non-tungsten materials.

TEM sample preparation↗

DOE OSTI2025

A self-supervised robotic system for autonomous contact-based spatial mapping of semiconductor properties

Integrating robotically driven contact-based material characterization techniques into self-driving laboratories can enhance measurement quality, reliability, and throughput. While deep learning models support robust autonomy, current methods lack reliable pixel-precision positioning and require extensive labeled data. To overcome these challenges, we propose an approach for building self-supervised autonomy into contact-based robotic systems that teach the robot to follow domain expert measurement principles at high throughputs. We demonstrate the performance of this approach by autonomously driving a 4-DOF robotic probe for 24 hours to characterize semiconductor photoconductivity at 3025 uniquely predicted poses across a gradient of drop-casted perovskite film compositions, achieving throughputs of more than 125 measurements per hour. Spatially mapping photoconductivity onto each drop-casted film reveals compositional trends and regions of inhomogeneity, valuable for identifying manufacturing defects. With this self-supervised neural network–driven robotic system, we enable high-precision and reliable automation of contact-based characterization techniques at high throughputs, thereby allowing measurement of previously inaccessible yet important semiconductor properties for self-driving laboratories.

Science & Technology - Other Topics↗

DOE OSTI2024

PV Generation and Load Forecasting for Adjuntas PR Community Microgrids

Existing frameworks to forecast time-series photovoltaic (PV) output power and consumer load for microgrid operations and controls assume a near-continuous availability of real-time input features from the field assets such as PV inverters, energy meters, and weather station. These incoming data points are used to periodically retrain models and update forecast snapshots over a moving horizon window, be it one hour-ahead, one-day ahead, or one-week ahead. However, such frameworks are not resilient to disruptions in data availability caused by losses in communications between the field sensors and data loggers. Hence, there is a need for programs that assume no availability of real-time microgrid asset data and still make reliable forecasts that can be used for decision-making. Such programs would be apt to function in extreme weather events such as hurricanes and would use lightweight recursive time-series models to independently forecast solar irradiance and ambient temperature, then compute PV power from those forecasts, as well as independently forecast consumer load. The codebase performs forecasting for the scenario of when the microgrid does not have a reliable access to forecasts or real-time observations of solar irradiance (I) and ambient temperature (AT) and load (Load) to be able to adequately forecast, in real-time, the PV power production or a business' load. In this case, using historical values of PV power and load, a univariate forecasting of generation and consumption are respectively made. The use-case in particular has two sub-scenarios: one, a normal 7-day ahead forecast where the unavailability of real-time data is assumed due to infrastructure issues such as loss of communication or sensor maintenance or service downtimes. Whereas a hurricane-caused unavailability of real-time data requires a second model trained specifically on historical hurricane days to be able to capture the extreme day behavior of generation in particular, and load if applicable. A gradient boosted regression tree comprises an ensemble of additive models that map between the input of historical values (be it irradiance, temperature, or load) and their corresponding output forecasts of a given horizon such that the individual learner predictions are summed up over the total number of such learners in the ensemble to produce an aggregate forecast. A weighting mechanism is applied to the training data in each iteration, where actual and forecast values are compared to penalize incorrect forecasts by increasing the weight and reducing it to reward correct forecasts. The code's benefits are that it: (a) accounts for a contingency where communication loss renders newly measured real-time data unavailable for model tuning and snapshot updates; (b) presents blind forecasting that recursively determines the next time-step value in a horizon using the forecast of the same attribute from a prior step; and (c) employs lightweight models that, once trained, can reliably generalize for different horizons, which make them suitable for enhancing the resilience of field microgrids prone to extreme events that encounter disruptions to data availability.

Sundararajan, Aditya [Oak Ridge National Laborator↗

DOE OSTI2024

The circular bioeconomy: a driver for system integration

Background: Human and earth system modeling, traditionally centered on the interplay between the energy system and the atmosphere, are facing a paradigm shift. The Intergovernmental Panel on Climate Change’s mandate for comprehensive, cross-sectoral climate action emphasizes avoiding the vulnerabilities of narrow sectoral approaches. Our study explores the circular bioeconomy, highlighting the intricate interconnections among agriculture, forestry, aquaculture, technological advancements, and ecological recycling. Collectively, these sectors play a pivotal role in supplying essential resources to meet the food, material, and energy needs of a growing global population. We pose the pertinent question of what it takes to integrate these multifaceted sectors into a new era of holistic systems thinking and planning. Results: The foundation for discussion is provided by a novel graphical representation encompassing statistical data on food, materials, energy flows, and circularity. This representation aids in constructing an inventory of technological advancements and climate actions that have the potential to significantly reshape the structure and scale of the economic metabolism in the coming decades. In this context, the three dominant mega-trends—population dynamics, economic developments, and the climate crisis—compel us to address the potential consequences of the identified actions, all of which fall under the four categories of substitution, efficiency, sufficiency, and reliability measures. Substitution and efficiency measures currently dominate systems modeling. Including novel bio-based processes and circularity aspects might require only expanded system boundaries. Conversely, paradigm shifts in systems engineering are expected to center on sufficiency and reliability actions. Effectively assessing the impact of sufficiency measures will necessitate substantial progress in inter- and transdisciplinary collaboration, primarily due to their non-technological nature. In addition, placing emphasis on modeling the reliability and resilience of transformation pathways represents a distinct and emerging frontier that highlights the significance of an integrated network of networks. Conclusions: Existing and emerging circular bioeconomy practices can serve as prime examples of system integration. These practices facilitate the interconnection of complex biomass supply chain networks with other networks encompassing feedstock-independent renewable power, hydrogen, CO 2 , water, and other biotic, abiotic, and intangible resources. Elevating the prominence of these connectors will empower policymakers to steer the amplification of synergies and mitigation of tradeoffs among systems, sectors, and goals.

09 BIOMASS FUELS↗

DOE OSTI2024

Using a 1D Radially Symmetric Coda Envelope Model for Robust Moment Magnitude ( M w ) Estimation in Iraq’s Tectonically Diverse Zones

Robust estimation of moment magnitude (M w ) can be challenging for Iraq due to the strong lateral heterogeneity across diverse tectonic zones. We aim to improve moment magnitude estimation by investigating the reliability of using a 1D coda envelope model in diverse tectonic zones of different lateral effects and offer a way forward for reliable estimates of M w for small events that are difficult to waveform model. Iraq comprises two main tectonic zones: (1) the Outer platform, consisting of the northwestern Zagros fold-thrust belt and the Mesopotamian foredeep, and (2) the Inner Arabian platform which is overlain by the Iraqi desert. A simple 1D coda envelope model was used because coda waves have a low sensitivity to the source and path heterogeneity. Three separate coda calibrations were conducted to investigate the robustness of a single 1D calibration to fit the country: Whole-region calibration, Zagros calibration, and Mesopotamia calibration. In the whole-region calibration, we used stations from both the Zagros and Mesopotamia zones. In the two other calibration models, we used only stations that were in those particular zones. Ground-truth reference spectra derived from the coda spectral ratio method were used to constrain high-frequency site terms. There was no drastic difference when comparing the moment magnitudes calculated from the waveform modeling and the three calibration models. The results show that the 1D coda envelope model is a reliable method even for a region with diverse tectonic zones. Therefore, we recommend using the whole-region calibration model for moment magnitude estimation that provides more complete path coverage and avoids biases introduced by path correction failures. The proposed calibration is a fundamental step in updating the comprehensive earthquake catalog and probabilistic seismic hazard assessments for Iraq.

58 GEOSCIENCES↗

DOE OSTI2025

Packages of Distributed Energy Technologies Demonstrating Demand Flexibility at Community Scale

The combination of increased electric load growth across all sectors, deferred electrical infrastructure investment, and other factors resulting in variable electric power supply, has created technical challenges to maintaining a resilient and reliable grid. Many federal, regional, and local efforts are in play to modernize the electric grid, including advancing building technologies and distributed energy resources (DERs) that are utilizing smarter controls to become responsive to both occupant and grid needs. This report reviews ten pilot projects demonstrating how groups of buildings combined with behind-the-meter (BTM) DERs such as electric vehicle (EV) charging, battery storage, flexible HVAC and domestic hot water systems, and photovoltaic systems can reliably and cost effectively provide grid services. Each of the ten pilot projects aim to deliver both energy efficiency and demand flexibility (DF) while supporting load growth. The ten demonstration teams are piloting flexible DER packages across diverse communities of residential and commercial buildings to address a variety of regional grid needs. The outcomes of these pilot projects will be used to inform future scaling through utility program development. This paper characterizes the ten teams, showcasing the decision-making process used by each group to develop their packages (Section 2), the grid services they plan to deliver (Section 3), the types of DER packages selected for deployment within building sectors (Section 4) and trends between building sector, DER types, and grid services In order to achieve community scale benefits, the pilot projects must utilize aggregated control mechanisms for coordinating buildings and DERs together. Several types of coordinated control architectures have evolved amongst the teams, influenced by use type, existing market conditions, and integration type. Three coordinated controls architectures have been characterized, highlighting their use cases, benefits, challenges, and tradeoffs in their design. These insights can aid utilities, control vendors, and developers in scaling community-level energy systems (Paul, 2024). Ultimately, the technology packages selected by the ten teams will be coordinated to provide power system services, also known as grid services. Insights from these demonstrations will be useful for grid operators, regulators, aggregators and other stakeholders as they look to deploy demand flexible resources as grid services in the future. The grid services that each team is targeting for demonstration are described in Section 3 and Section 4. Methods for evaluating the grid services have been described in the paper Metrics for Evaluating Grid Service Provision from Communities of Grid-interactive and Efficient Buildings and other DER (MacDonald, 2023). To identify technology packages for demonstration, Section 2 shows that project teams used a range of analysis approaches, including building energy modeling, AMI data analysis, cost-benefit frameworks, and utility pilot data. Some teams emphasized technical modeling to quantify grid impacts and demand reduction potential, while others prioritized economic evaluations, stakeholder input, or exploratory pilots to inform deployment decisions. This diversity reflects the need to tailor selection methods to project goals, available data, and organizational context. Section 5 discusses trends between the DER technologies deployed and the grid service provisions from each team. Residential buildings (multifamily and single family) lean towards technologies that enhance energy efficiency (e.g. weatherization upgrades, smart thermostats) and onsite power generation integration (e.g. solar PV). Commercial building demonstrations prioritize technologies that ensure operational reliability (e.g. battery storage) and centralized energy management systems and optimization solutions. Teams that are deploying controllable storage-based technologies are more likely to provide grid services that require a near real-time response. Teams incorporating load shifting technologies like smart thermostats with HEMs are likely to include energy markets participation and customer bill management offerings. Campus demonstrations are adopting diverse sets of DERs to emphasize renewable generation, paired with centralized control. This section also describes technologies that were considered during project planning but ultimately excluded from final deployment. These demonstrations reveal that effective DER package design should be tailored to building type, customer segment, and construction vintage. Multifamily buildings benefit from centralized HVAC upgrades and supervisory controls, while single-family homes are well-suited for individualized technologies like solar, storage, and smart home energy monitors. Commercial and campus settings prioritize EMIS integration and load optimization. New construction enables cost-effective integration of DER-ready infrastructure, whereas retrofits require deployments aligned with owner and tenant value streams. For utility program planners, early coordination with developers and building owners, paired with segmented and modular program offerings, can improve adoption, scalability, and grid impact.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Closing the loop: model-predictive control for a closed-circuit reverse osmosis system

This article presents a model-predictive controller (MPC) for the maximization of the energy efficiency of a closed-circuit desalination reverse osmosis (CCRO) system. CCRO is a process for producing drinking water that is based on a cyclic operation with the following two phases: (a) filtration and (b) drain. In this article, we test model predictive control for optimal control of this process. The most important features of our approach are as follows: (a) the selection of a model structure that enables reliable forecasts of the filtration phase (up to 3 h), (b) an on-line model calibration strategy that ensures model forecast reliability, and (c) the satisfaction of equipment safety and operational constraints on the selected setpoints. We challenge this through deliberate introduction of changes in the unmeasured feed concentration and the applied constraints. Our results indicate that frequent model parameter updates are critical to maintain model reliability for MPC purposes. In addition, we illustrate that parameter identifiability is not guaranteed and that deliberate variation in flow rates is necessary even though the process never operates in steady state. Finally, MPC can compute flow rate setpoints that maximize the energy efficiency of the CCRO process while satisfying the applicable equipment and safety constraints.

closed-circuit reverse osmosis↗

DOE OSTI2024

Decarbonizing Cooking Appliances With Hydrogen

A novel hydrogen compatible heterogeneous burner prototype was engineered, fabricated, and evaluated for applications in cooking appliances. The key features targeted and accomplished under this effort included fuel flexibility, tolerance to high hydrogen concentrations (up to 85% in atmospheric design and 100% in premixed design), wide range of thermal power ratings, thermal cycling, reliability, and most importantly safety. The burner was operated in the range of 3000 – 12,000 Btu/h under all practical operating conditions (cold start, hot restart, fuel concentration variance). The heterogeneous oxidation burner technology was evaluated for overall performance targeting safety, reliability, emissions, fuel flexibility, and energy efficiency in a commercial RV and residential cooking range appliance prototypes. The burner demonstrated safe and reliable performance generating infrared heating energy without the consumption of any parasitic energy while using low-cost materials. Performance evaluation testing of the novel IR burner demonstrated fuel flexibility with hydrogen blended natural gas while significantly eliminating air pollutants like NOx and methane and increasing the energy efficiency by up to 20%.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Connected and Learning Based Optimal Freight Management for Efficiency

The management of the future heterogenous fleet is a complex decision-making problem. The heterogenous fleet is emerging as decarbonization technologies are deployed by fleets toward lowering the freight operation emissions in Medium and Heavy-duty vehicles. Traditionally, in fleets characterized by a homogeneous Diesel Internal Combustion Engine (ICE) powertrain, the process of fleet planning and operational optimization unfolds sequentially without the necessity to account for powertrain and vehicle-specific characteristics during dispatch decisions. Fleets with trucks less than 5 years old tend to maintain stable vehicle efficiency with minimal operational reliability risks for fleet managers. However, the landscape changes with the incorporation of emerging powertrain technologies, which lack extensive operational data and service experiences. This includes technologies like hybrid, Electric, Fuel Cell, or alternative fuel ICE. Operational decisions for fleets featuring heterogeneous powertrain technologies and facing limited access to alternative fueling and charging stations become intricate, requiring careful consideration and optimization at each dispatch. The difference in efficiency characteristics of emerging technologies, their range limitations, and the restricted availability of charging/alternative fueling infrastructure, coupled with sensitivity to driving conditions (e.g., EV range reduction in low temperatures) and their impact on component aging (such as batteries), become pivotal factors influencing the reliable and efficient freight transportation. To make the path toward low emission freight transportation efficient and reliable, an AI-assisted fleet management software is developed in this project to help fleet managers in optimizing both adoption of emerging powertrain decarbonization, connected and automated technologies and also operating the fleet after such technologies are deployed as schematically. Freight transportation requirements are different depending on the cargos to be shipped, customer requirements and regions of operations. This further highlights the need for software and digital solutions to tailor deployment and operation of emerging powertrain, connectivity, and automation technologies toward the specific fleet operation requirements. The fleet management optimizer was also integrated with a model of the fleet to simulate the operation of the fleet over 1 year of the baseline fleet operation (250,000+ shipments) indicating the significance of day-to-day variations on emissions and energy consumption of a freight transportation fleet. The results demonstrate ≥20% improvement in freight efficiency in terms of WTW CO2 per ton-mile of cargo shipments while all fleet operation constraints are enforced, and the cost (CapEx and OpEx) is minimized.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Wholesale Electricity Markets and Resource Adequacy with High Clean Energy Generation Targets

Wholesale electricity markets are intended to incentivize system generation investments and operations outcomes that meet evolving system needs. In this work, we evaluate the effectiveness of wholesale market structures, rules and policies in achieving system resource adequacy (RA) and clean energy targets in the presence of self-interested generation investors using the Electricity Markets and Investment Suite Agent-based Simulation (EMIS-AS) model. Results highlight that both capacity markets and operating reserve demand curves (ORDCs) can help achieve a reliable system but with different RA compliance timelines and distribution of generation technologies. Structures with capacity markets tend to favor more capital-intensive peaking technologies while reducing wind and solar build-outs due to suppressed energy and clean energy market prices, particularly in the absence of strong clean energy targets. Conversely, ORDCs improve the commitment of available generation units, but this comes at the expense of higher system costs and renewable generation curtailment. We also find that well-calibrated static capacity demand curves can yield similar reliability and total cost compared to capacity market demand curves informed dynamically by resource adequacy while also yielding stable annual capacity prices. Different approaches to formulating ORDC curves can also yield key trade-offs, namely that a more efficient treatment of storage chronology results in lower ORDC curves and prices, yielding less investment and cost but at the expense of reliability. Finally, the effectiveness of wholesale electricity markets in practically achieving very high clean energy generation targets highly depends on the cost-competitiveness of clean energy technologies that can support critical balancing needs across multiple timescales.

capacity expansion↗

DOE OSTI2024

Heterogeneous Integration Technologies for High-temperature, High-density, Low-profile Power Modules of Wide Bandgap Devices in Electric Drive Applications (Final Technical Report)

The goal of this project is to develop packaging technologies for making high-temperature, high-density, and low-profile wide-bandgap (WBG) power electronics modules for electric drives. These modules are aimed at enabling the DOE’s University Consortium to reach its 2025 inverter targets of ≥ 100 kW/L and ≤ 2.7 $/kW. The specific objectives are to: design and fabricate SiC half-bridge power modules with double-sided cooling and parasitic inductances < 5 nH, heat flux density > 400 W/cm 2 , and working junction temperature of 200 o C; design, fabricate, and deliver a gate driver with double-sided cooled modules for the construction of a 100 kW/L inverter at Oak Ridge National Lab; and design and prototype intelligent gate drivers with integrated current sensor and a low-profile DC-DC power supply with air-core transformer for testing power modules at 200 o C junction temperature. We followed an iterative technical approach of design, simulation, fabrication, and testing of various versions of modules, current sensors, and power supply. The state-of-the-art silicon carbide devices rated at 1.2 kV and 149 A were packaged by sintered-silver bonding on an aluminum nitride direct-bond-copper substrate for high thermal conductivity, high working temperature, and high joint reliability. Porous silver posts were used to interconnect the device’s source pads to the other direct-bond-copper substrate for low mechanical stresses, ease of manufacturing, and double-sided cooling. A current sensor based on package parasitic inductance was developed to measure switching current. A dynamic feedback scheme was developed to compensate the effect of parasitic resistance and temperature variation. A constant-current class-E dc-dc converter with air-core transformer was developed. Air-core transformer was used due to the unavailability of magnetic core at high temperatures. Gate driver and power supply were integrated with the double-side cooled, high temperature SiC power modules for testing the modules at 200 o C junction temperature. Double-pulse and continuous testing of the integrated technologies validated the design and fabrication of the three component technologies. Throughout the project, we overcame the challenge for design verification caused by low prototyping yield, which then helped train the graduate students, the future workforce, to learn the engineering know-how for low-cost manufacturing of reliable products. Below is a summary of the major accomplishments of this project: development of a prototyping process for fabricating double-side cooled (1200 V, 149 A) SiC phase-leg modules capable of working to 200 o C Tj; simulation and experimental verification of the improvement of thermo-mechanical reliability of the double-side cooled SiC phase-leg module by using rigid encapsulant; design and experimental validation of a current sensor based on package parasitic inductance and a compensation solution for eliminating the effect of parasitic resistance; design and experimental validation of a low-profile power supply with six-output air-core transformer for gate driver; functional demonstration of a SiC phase-leg module integrated with its gate driver, current sensor, and an air-core power supply at 200 o C Tj in a double-pulse switching test setup and Buck converter continuous test setup; successful completion of six PhD and two MS students who are or will work at Apple Inc., Tesla Inc., Wolfspeed Inc., Microchip Inc., Monolithic Power Systems Inc., and LG Magna Inc.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Wholesale Electricity Market Design to Support Resource Adequacy

agent based modeling↗

DOE OSTI2024

Distributed Wind Monitoring Best Practices

Accessible performance and operational data have been identified as a key enabler for distributed wind energy industry advancement. While utility-scale wind turbines benefit from reliable and continuous supervisory control and data acquisition (SCADA)-based monitoring platforms, monitoring of the U.S. fleet of distributed wind (DW) turbines has been more inconsistent, unreliable, and sometime difficult to access. Without fleet monitoring data, the industry will never understand and thus work to improve turbine under-performance and reliability issues. For the DW industry to scale up, attract investors, and boost credibility, fleetwide monitoring must be robust and reliable, select data must be made accessible to stakeholders, and the data must be in a format useful to users. To help move the industry toward a more standardized, accessible stream of monitoring data, this distributed wind monitoring best practices report attempts to cover topics including key monitoring channels, hardware, communication strategies, and accessibility. Strategic engagement with DW original equipment manufacturers (OEMs), service providers, lab and university researchers, testing organization, certification bodies, end users and solar photovoltaic (PV) monitoring experts has enabled a better understanding of the current state-of-the-art of monitoring and aided in articulating this set of best practices that will guide OEMs toward harmonized monitoring strategies, aimed at a future goal of achieving accessible performance and operational data for the entire fleet of U.S. distributed wind turbines.

17 WIND ENERGY↗

DOE OSTI2024

Bill Savings vs. Backup Power: Evaluating operational tradeoffs for home solar+storage systems [Slides]

This study explores tradeoffs between the use of home solar+storage systems for backup power versus day-to-day utility bill savings. The study focuses specifically on the “reserve setting” available with most home battery storage systems, which allow the customer to maintain some minimum level of storage in reserve in case of an unforeseen power interruption. The more capacity that is held in reserve, the greater the customer’s ability to ride-through possible power interruptions, but less capacity is then available to manage utility bills on a day-to-day basis. This study evaluates this operational tradeoff across a diverse set of locations and residential electricity tariff structures, relying on Berkeley Lab’s PRESTO model to stochastically simulate power interruption events, and exploring a range of sensitivities, including variations in customer value of lost load (VoLL), interruption frequency, and other key drivers. The results show that, in most circumstances, the opportunity cost of holding storage capacity in reserve, in terms of foregone bill saving, tends to outweigh any gains in reliability value associated with mitigated power interruptions. This finding is robust across tariff structures and across most of the sensitivities considered, including those related to rate level, customer load level, and storage sizing. There are a limited set of circumstances where raising the reserve setting improves the overall customer value (comprised of bill savings plus reliability value). Specifically, that exception occurs when all of the following conditions apply: (a) the customer resides in a location with exceptionally poor reliability, (b) the customer has exceptionally high VoLL; (c) the customer is on a net billing rate or on a TOU rate that allows grid discharging but not grid charging; and (d), depending on the location, the price arbitrage differential on that rate is relatively small. In all other circumstances analyzed, total customer value declines with reserve level.

14 SOLAR ENERGY↗

DOE OSTI2025

EV Charging Infrastructure Energization An Overview of Approaches for Simplifying and Accelerating Timelines to Processing EV Charging Load Service Requests

The United States has seen significant growth in electric vehicle (EV) adoption, leading to increased demand for EV charging infrastructure. Over the past decade, EV charging infrastructure site developers, site hosts, and electric distribution utilities have navigated the process to integrate chargers onto the electric grid. Site developers and site hosts have raised the alarm that the integration process for high-powered EV charging projects does not meet the needs of the EV market for timeliness or cost. High-powered charging stations typically require a load service request or an agreement with the local utility to connect to the grid. The process of energizing a new high-powered charging site can be complex and time-consuming, often taking up to 2 years. This timeline is the result of current utility energization processes having been designed for construction projects that take longer to build (i.e., buildings). The specific challenges stem from various factors, including compartmentalization in application processes, the integration of EV charging process approvals with other distributed energy resources (DERs), and the need to ensure grid reliability. The energization process needs to evolve to meet the growing demand for high-powered EV charging. This white paper compiles information gathered through various conversations with key stakeholders, including utilities, utility regulators, EV charging operators, site developers, and authorities having jurisdiction (AHJ) as well as through an extensive literature review. This document identifies the challenges and provides potential solutions to streamline the process of connecting EV charging infrastructure to the power grid in the United States, serving as a starting point for future conversations around these solutions. The solutions noted in this white paper require collaborative efforts among utilities, regulators, and EV charging infrastructure developers to streamline the grid connection process for EV charging infrastructure. They are broadly organized into four areas: 1. Increase data access and transparency: Develop automated load service request tools, integrate hosting capacity and load service request analyses, incorporate EV adoption forecasts, and provide transparency on the processing queue. 2. Improve energization processes and timing: Create fast-track options based on prescreening criteria, provide flexibility or phased approvals in the load service request/interconnection process, build internal knowledge within utilities about EV charging technologies, and provide standardized workforce training. 3. Promote economic efficiency: Right size distribution components to accurately reflect the load requirements of EV charging infrastructure, make proactive investments in grid infrastructure based on EV adoption forecasts and growth projections, and consider energy equity and environmental justice factors such as equitable access to EV charging when planning infrastructure. 4. Improve grid reliability and resilience: Use load management/power control systems (PCS) at EV charging stations, adopt and implement harmonized standards for communication protocols and information models between the EV charging and grid control infrastructure, and address cybersecurity considerations by implementing robust security measures and standards for EV charging infrastructure—with particular emphasis on clarifying the security requirements for the interface to the grid. The objective of the solutions proposed in this white paper is to accelerate the timeline and decrease costs associated with connecting EV charging infrastructure to the grid. Electric utilities, utility regulators, EV charging infrastructure developers, and site hosts will first need to understand which solutions are available in their service territory, and if warranted, which combination of solutions would support their specific needs. Through the successful implementations of solutions at scale detailed here, industry will demonstrate a new and innovative ecosystem where timely deployment and energization of EV charging infrastructure with greater grid resiliency and reliability is a reality.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Recommendations for Minimum Required Diagnostics Information

The rapid growth of electrified transportation, including light- and medium-duty electric vehicles (EVs) as a mobility solution requires a reliable EV-charging infrastructure. To advance charging reliability, the ChargeX Consortium reports “Recommendations for Minimum Required Error Codes for Electric Vehicle Charging Infrastructure” and “Implementation Guide for Minimum Required Error Codes in Electric Vehicle Charging Infrastructure” provided recommendations for a set of minimum required error codes (MRECs), their functional and responsibility classifications, and a guide for their implementation, using Open Charge Point Protocol (OCPP) versions 1.6J4 and 2.0.1.5 These reports outline a recommended practice for consistent error reporting and interpretation, which is essential for communicating issues uniformly across the complex and diverse EV-charging ecosystem. However, MRECs are just one part of diagnosing issues; another critical part is obtaining enough information about the current state and performance of the various charging components to identify root causes for each of the error codes. This additional diagnostics data can be used by technicians or automated systems to understand the context around an issue, allowing for timely resolution, decreased maintenance costs, and increased charging reliability. During everyday operations, data are regularly collected and analyzed across the ecosystem. Although sharing of all that available data would be great for diagnostics, concerns on data ownership, privacy, and original equipment manufacturer (OEM) intellectual property pose a challenge. To overcome this obstacle, this report proposes a set of minimum required diagnostic information (MRDI) and recommends that the industry implement these uniformly across the North American EV charging ecosystem. MRDI provides a means to exchange only data deemed necessary for root cause determination.

33 ADVANCED PROPULSION SYSTEMS↗