Results for “Electricity Infrastructure” — Page 37

DOE OSTI2022

Modeling Power Plant Siting Opportunities and Constraints in the Eastern Interconnection

The electrical transmission grid is a critical part of the U.S. national infrastructure, and its modernization is a U.S. Department of Energy (DOE) priority. We describe the Energy Zones Mapping Tool (EZMT), a unique, powerful, and public web-based system with multi-criteria decision analysis (MCDA) models for more than 20 power plant technologies and many other capabilities. Through a case study on natural gas combined cycle (NGCC) power plants in the Eastern Interconnection (EI), we provide an example of incorporating an EZMT MCDA model into a larger planning context, with projections of interconnection-level capacity expansion, thermoelectric power plant retirements, and water availability. Our results provide insights on candidate NGCC site distributions and the criteria influencing them. The case study provides both an efficient methodology for performing similar analyses and hundreds of candidate NGCC power plant sites that can be studied in more detail as potential project sites.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2023

Deployment Readiness Framework Subtask 1.2 Outreach Report: Common Themes Among Clean Energy Transition Technical Assistance Projects

Island and remote coastal communities face an uncertain energy future. These communities build, operate, and maintain energy infrastructure in extremely challenging environments and pay the highest prices for electricity and fuels. Moreover, climate change increasingly affects grid reliability, resiliency, and, ultimately, the cost of energy. Marine energy could address many of the challenges faced by island and remote coastal communities, but these communities often have limited resources and capacity to invest in complex energy and coastal resilience issues. The Deployment Readiness Framework (DRF) is being developed to support community-driven energy transitions in these coastal communities and to better understand the readiness of communities for marine energy demonstration and operation. Led by the National Renewable Energy Laboratory and Pacific Northwest National Laboratory, the DRF builds on the Energy Transitions Initiative Partnership Project (ETIPP) supported in part by the U.S. Department of Energy's Water Power Technologies Office. This report details the results from the stakeholder outreach and engagement effort linked to the previously reported literature review.

16 TIDAL AND WAVE POWER↗

DOE OSTI2025

Data for "Implications of Zoning Ordinances for Rural Utility-Scale Solar Deployment and Power System Decarbonization in the Great Lakes Region".

This dataset includes the Energy Zoning Database and geographic shapefiles used to identify suitable rural areas for utility-scale solar development. Additionally, it contains the input data required for the capacity expansion model, including information on existing electric generators and their associated costs, solar resource potential, and transmission infrastructure data. These datasets collectively support the analysis presented in the paper, ensuring a comprehensive assessment of zoning regulations, land suitability, and the economic and technical feasibility of solar deployment in rural areas.

Owusu-Obeng, Papa Yaw (ORCID:0000000334385183)↗

NASA NTRS2022

Landslide Likelihood Prediction using Machine Learning Algorithms

The supply of electricity via power plants is criticalto the operation of many critical infrastructure systems in mod-ern society. Natural hazards can disrupt the power supply, causepower outages that can halt economic growth, and impede emer-gency response until power is restored. The proposed work aimsto predict the landslides likelihood in these critical infrastructurelocations in the Northeastern USA using integrated databases ofexplanatory variables and machine learning algorithms. First,data related to landslides are obtained and merged, includingtopographic, soil moisture, and precipitation-related data. Fiveregression algorithms, namely: Random Forest, Extreme Gradi-ent Boosting (XGBoost), K-Nearest Neighbor regression (KNN),Linear Support Vector Regressor (SVR), and Linear regression,are utilized to predict the landslide probability and evaluatedon the dataset. The accuracy of the models is assessed by usingstatistical metrics such as mean absolute error (MAE), meansquared error (MSE), and root mean squared error (RMSE).The study results show that Random Forest outperformed othermodels with the mutual information feature selection method.It achieved an MSE of 0.0011 with mutual information-basedfeature selection and an MSE of 0.00157 without feature selection.KNN regressor outperformed the other models with an MSEof 0.00139 with correlation-based information selection. Theproposed landslide identification model with Random Forestalgorithm shows outstanding robustness and great potential intackling the landslide likelihood prediction by employing MLalgorithms.

Vasundhara Acharya↗

NASA NTRSDate not supplied

Estimations of Aircraft and Airport Domestic Greenhouse Gas Emissions from 2016-2021

Data and analyses are presented on the utilization of aircraft fuel in the U.S. and the resulting greenhouse gas emissions. Commercial passenger and freight flight data and airport fuel consumption usage from 2016-2021 are captured from the Bureau of Transportation Statistics website and the Sherlock Data Warehouse managed at NASA Ames Research Center. The resulting dataset is used to determine the miles flown by major aircraft. The corresponding aircraft fuel burn is estimated based on the International Civilian Aviation Organization fuel burn tables, and carbon dioxide emissions are calculated using a fuel-burn multiplicative factor. One conclusion of this analysis is that long-haul flights (flight distances > 2485 statute miles) create a disproportionately large amount of carbon dioxide emissions in the U.S, while short flights (< 311 statute miles) contribute less than five percent of the U.S. aviation-related carbon dioxide emissions. Although these short-haul flights may not have a large impact on overall carbon dioxide emissions, they will be valuable as demonstration missions for the next generation of electric, hybrid, and hydrogen-powered vehicles and their supporting energy infrastructures. This paper discusses recent trends in short-haul missions, their associated aircraft and airport types, and extracts several key requirements for future short-haul vehicles.

Susie Go↗

NASA NTRS2023

Estimations of Aircraft and Airport Domestic Greenhouse Gas Emissions from 2016-2021

Data and analyses are presented on the utilization of aircraft fuel in the U.S. and the resulting greenhouse gas emissions. Commercial passenger and freight flight data and airport fuel consumption usage from 2016-2021 are captured from the Bureau of Transportation Statistics website and the Sherlock Data Warehouse managed at NASA Ames Research Center. The resulting dataset is used to determine the miles flown by major aircraft. The corresponding aircraft fuel burn is estimated based on the International Civilian Aviation Organization fuel burn tables, and carbon dioxide emissions are calculated using a fuel-burn multiplicative factor. One conclusion of this analysis is that long-haul flights (flight distances > 2485 statute miles) create a disproportionately large amount of carbon dioxide emissions in the U.S, while short flights (< 311 statute miles) contribute less than five percent of the U.S. aviation-related carbon dioxide emissions. Although these short-haul flights may not have a large impact on overall carbon dioxide emissions, they will be valuable as demonstration missions for the next generation of electric, hybrid, and hydrogen-powered vehicles and their supporting energy infrastructures. This paper discusses recent trends in short-haul missions, their associated aircraft and airport types, and extracts several key requirements for future short-haul vehicles.

Susie Go↗

DOE OSTI2024

Architecture for Web-Based Visualization of Large-Scale Energy Domains: Preprint

With the growing penetration of inverter-based distributed energy resources and increased loads through electrification, power systems analyses are becoming more important and more complex. Moreover, these analyses increasingly involve the combination of interconnected energy domains with data that are spatially and temporally increasing in scale by orders of magnitude, surpassing the capabilities of many existing analysis and decision-support systems. We present the architectural design, development, and application of a high-resolution web-based visualization environment capable of cross-domain analysis of tens of millions of energy assets, focusing on scalability and performance. Our system supports the exploration, navigation, and analysis of large data from diverse domains such as electrical transmission and distribution systems, mobility and electric vehicle charging networks, communications networks, cyber assets, and other supporting infrastructure. We evaluate this system across multiple use cases, describing the capabilities and limitations of a web-based approach for high-resolution energy system visualizations.

grid modernization↗

DOE OSTI2024

Impact of Electric Vehicle Charging Station Reliability, Resilience, and Location on Electric Vehicle Adoption

While the majority of electric vehicle (EV) charging events in the United States occur at home, issues with public charging stations are consistently found to be a top reason that potential EV buyers do not purchase an EV, demonstrating that both EVSE reliability and availability impacts EV adoption. This report explores multiple parameters that impact EVSE reliability and deployment, which in turn impact EV sales. These include extreme weather, codes and standards, region (urban vs. rural), and grid network type. Grid reliability was not found to impact EV adoption. The relationships between EV station reliability, station resilience, grid resilience, and EV adoption are largely outside the scope of the National Renewable Energy Laboratory's (NREL's) Automotive Deployment Options Projection Tool (ADOPT) and other vehicle adoption models, so the methodology of this report is varied. Section 2 sets the baseline for infrastructure reliability, user satisfaction, and maintenance practices. Section 3 explores the ways that electric vehicle supply equipment (EVSE) reliability impacts the relationship between EVSE and EV adoption. Section 4 shows how geographical categories such as urban, rural, large grid, off-grid, or microgrid can be helpful in EVSE deployment strategies, as well as how the relationship between EVSE and EV adoption differs among these categories. Section 5 investigates the impacts of grid reliability and infrastructure resilience on EV adoption. Finally, Section 6 reverses the perspective to examine the impact that EVs and EVSE have on grid resilience and reliability. As recent funding initiatives result in an expansion of public chargers across the United States, as well as an increase in the uptime of existing chargers, EV adoption will likely grow.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Integrated Dispatching and Charging Management of an Autonomous Electric Vehicle Ride-Hailing Fleet

Electrification and autonomous driving are two important trends in transportation systems. The convergence of these two technologies will introduce opportunities to improve transportation systems' operation and energy efficiency. One potential application is the commercial ride-hailing fleet with autonomous electric vehicles (AEVs). In order to harvest promising benefits from introducing AEVs into ride-hailing fleets, some unavoidable challenges will need to be resolved to ensure the fleets operates functionally and efficiently. This paper discusses the challenges of dispatching AEVs and their interactions with charging infrastructure. An integrated decision making framework for dispatching and charging has been designed using a system optimization approach to study the AEVs' management within the period when they drop off passengers and pick up the next passengers. Its potential fleet-wide benefits have been illustrated by comparing operations under a heuristic approach. A simulation platform has been designed to test different decision making strategies for the ride-hailing AEV fleets operational performance. Using this platform, detailed case studies have been performed with different fleet sizes, dispatching strategies, and charging infrastructure network settings. Comprehensive analyses from various aspects have been conducted to understand the AEVs' fleet operation performance, (e.g., zero occupancy vehicle miles traveled, successfully served ratio of ride-hailing requests, fleet vehicle charging downtime, and charging infrastructure utilization). Results have provided a deep understandings on operation's dynamics under various fleet system configurations and also have demonstrated advantages of the optimization-based approach for the AEV fleet management. Studies in this paper inform better designs on the future of sophisticated management strategies and charging infrastructure to support ride-hailing AEV fleet operation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Development of a Geothermal Module in reV: Quantifying the Geothermal Potential while Accounting for the Geospatial Intersection of the Grid Infrastructure and Land Use Characteristics

The Renewable Energy Potential (reV) model is a geospatial platform for estimating technical potential and developing renewable energy supply curves, initially developed for wind and solar technologies. The model evaluates deployment constraints, considering land use, environmental, and cultural factors, and estimates the distance to existing grid features to connect future plants (Maclaurin et al., 2021). A pressing deficiency in the reV model, however, is representation of geothermal electricity generation technologies. To address this gap, we developed a novel geothermal generation module for reV that allows for representation and analysis at the same level of detail as other renewable technologies. This paper describes our process for evaluating data sources for the modeling, and presents five preliminary reV geothermal results. More specifically, we present two sets of resource data that represent upper and lower bounds for geothermal potential. We then present several sensitivity runs using the upper bound resource data; the results are encouraging that levelized cost of electricity (LCOE) can be reduced by optimizing the location and estimated capacity of the spatially diverse geothermal resource while considering the distance to existing grid infrastructure. Our preliminary supply curves and levelized cost of electricity (LCOE) results should be considered with care due to the highly uncertainty in geothermal resource potential data. We present median LCOE values for the conterminous U.S. for five scenarios: four hydrothermal (3.5km depth) and one EGS (4.5km depth). The capital and operating costs for each respective technology are modeled. We also compare results using two different resource data sources.

exclusions↗

DOE OSTI2024

Development of a Geothermal Module in reV: Quantifying the Geothermal Potential While Accounting for the Geospatial Intersection of the Grid Infrastructure and Land Use Characteristics: Preprint

The Renewable Energy Potential (reV) model is a geospatial platform for estimating technical potential and developing renewable energy supply curves, initially developed for wind and solar technologies. The model evaluates deployment constraints, considering land use, environmental, and cultural factors, and estimates the distance to existing grid features to connect future plants (Maclaurin et al., 2021). A pressing deficiency in the reV model, however, is representation of geothermal electricity generation technologies. To address this gap, we developed a novel geothermal generation module for reV that allows for representation and analysis at the same level of detail as other renewable technologies. This paper describes our process for evaluating data sources for the modeling, and presents five preliminary reV geothermal results. More specifically, we present two sets of resource data that represent upper and lower bounds for geothermal potential. We then present several sensitivity runs using the upper bound resource data; the results are encouraging that levelized cost of electricity (LCOE) can be reduced by optimizing the location and estimated capacity of the spatially diverse geothermal resource while considering the distance to existing grid infrastructure. Our preliminary supply curves and levelized cost of electricity (LCOE) results should be considered with care due to the highly uncertainty in geothermal resource potential data. We present median LCOE values for the conterminous U.S. for five scenarios: four hydrothermal (3.5km depth) and one EGS (4.5km depth). The capital and operating costs for each respective technology are modeled. We also compare results using two different resource data sources.

exclusions↗

DOE OSTI2021

Integrated U.S. nationwide corridor charging infrastructure planning for mass electrification of inter-city trips

This study introduces an integrated modeling framework to evaluate long-term national corridor charging infrastructure requirements in the United States to support the growing inter-city charging demand with the rapid growth in the battery electric vehicle (BEV) market. The core model is an optimization model that considers spatial and temporal dimensions and models heterogeneous behaviors between travelers. The model also introduces the travelers’ inconvenience cost function by linking travelers’ acceptance of the charging infrastructure with exogenous technology and social factors. The inconvenience cost function simulates mode choice between BEVs and alternative modes by heterogenous travelers. We applied the framework to assess the inter-regional charging infrastructure requirements for the entire U.S. mainland interstate highway network. We evaluated impacts on the infrastructure design and its public acceptance with changes in policy, technology, and demographic characteristics, and we also quantified the importance of modeling full-scale inter-regional charging infrastructure requirements compared to the conventional regional level analyses.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Regional Analysis for an Economically and Environmentally Viable Transition to Heavy-Duty Vehicles with Alternative Powertrains

The transportation sector is responsible for a significant portion of greenhouse gas emissions. Within the sector, truck freight is responsible for a third of the associated emissions. Alternative powertrains are seen as a viable approach to significantly reduce these emissions. Prior to making a large-scale transition, it is important to consider the following questions: will the power grid support a transition to alternative powertrains?; will the transition truly reduce carbon emissions?; and will the transition impose an unnecessary economic burden on companies within the industry? The answer to these questions, however, can vary by geography, maturity/capacity of the energy distribution network or predicted vehicle load. We focus on the latter two questions, investigating the variation in estimated total cost of ownership and carbon emissions across the United States at the zip code level for both heavy-duty battery electric vehicles and heavy-duty fuel cell electric vehicles. As a benchmark, we compare estimated emissions and costs of alternative powertrain vehicles to that of conventional heavy-duty vehicles powered by diesel internal combustion engines. This work highlights areas with electric grids primed for a transition to alternative powertrain vehicles, such as the Pacific Northwest, and areas that require further infrastructure investment in renewables, such as many of the Mountain states, Missouri, and Florida. Additionally, this work illustrates the current advantages in carbon emissions of battery electric vehicles compared to fuel-cell electric vehicles, while providing insights into required regional investments for narrowing the gap.

Goulet, Nate [ORNL] (ORCID:0000000237314965)↗

DOE OSTI2025

Business Models for Scaling Demand Flexibility Volume III – Stakeholder ecosystem management challenges, and lessons learned from U.S. programs

Load growth at the grid edge is driving increased attention to the distribution system and its ability to enable customer technology adoption in an affordable and timely manner. Key industry stakeholders, including electric utilities and regulators, can benefit from strategies to manage and balance customer needs with infrastructure investments, such as demand flexibility. This report focuses on demand flexibility—the ability to reduce, shift, shed, generate, or modulate loads in response to building and grid needs—to reduce the need for costly grid upgrades by deferring investment needs and increase system reliability by shifting electricity usage during periods of high risk. Specifically, we focus on the emerging characteristics of business models for demand flexibility as a framework to understand how demand flexibility programs generate value. In this report, we focus on demand flexibility program stakeholder ecosystem management strategies, which provide information on value delivery and describe how program implementers leverage partner capabilities and collaborate to deliver customer and grid benefits. This report discusses the role of stakeholder ecosystem management in demand flexibility programs, identifies existing challenges to effective stakeholder management, and describes lessons learned. This report is part of a series that includes reports on value propositions, customer relationship management strategies, and program life cycle.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Business Models for Scaling Demand Flexibility Volume II – Customer relationship management strategies, challenges, and lessons learned from U.S. programs

Load growth at the grid edge is driving increased attention to the distribution system and its ability to enable customer technology adoption in an affordable and timely manner. Key industry stakeholders, including electric utilities and regulators, can benefit from strategies to manage and balance customer needs with infrastructure investments, such as demand flexibility. This report focuses on demand flexibility—the ability to reduce, shift, shed, generate, or modulate loads in response to building and grid needs—to reduce the need for costly grid upgrades by deferring investment needs and increase system reliability by shifting electricity usage during periods of high risk. Specifically, we focus on the emerging characteristics of business models for demand flexibility as a framework to understand how demand flexibility programs generate value. In this report, we focus on demand flexibility program customer relationship management strategies, which provide information on value creation and focus on ensuring customers can navigate programs smoothly. This report discusses the role of customer relationship management strategies in demand flexibility programs, characterizes customer relationship management strategies that can be considered during program design and implementation, identifies existing challenges to customer relationship management strategies, and describes lessons learned. This report is part of a series that includes reports on value propositions, stakeholder ecosystem management, and program life cycle.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Business Models for Scaling Demand Flexibility Volume I – Value proposition characteristics, challenges, and lessons learned from U.S. programs

Load growth at the grid edge is driving increased attention to the distribution system and its ability to enable customer technology adoption in an affordable and timely manner. Key industry stakeholders, including electric utilities and regulators, can benefit from strategies to manage and balance customer needs with infrastructure investments, such as demand flexibility. This report focuses on demand flexibility—the ability to reduce, shift, shed, generate, or modulate loads in response to building and grid needs—to reduce the need for costly grid upgrades by deferring investment needs and increase system reliability by shifting electricity usage during periods of high risk. Specifically, we focus on the emerging characteristics of business models for demand flexibility as a framework to understand how demand flexibility programs generate value. In this report, we focus on demand flexibility value propositions, which provide information on value creation and describe how programs deliver clear benefits that address customer and grid needs. This report discusses the role of value propositions in demand flexibility programs, provides an overview of value propositions for a range of demand flexibility stakeholders, identifies existing challenges to establishing an effective value proposition, and describes lessons learned. This report is part of a series that includes reports on customer relationship management strategies, stakeholder ecosystem management, and program life cycle.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Business Models for Scaling Demand Flexibility Volume IV – Program life cycle challenges and lessons learned from U.S. programs

Load growth at the grid edge is driving increased attention to the distribution system and its ability to enable customer technology adoption in an affordable and timely manner. Key industry stakeholders, including electric utilities and regulators, can benefit from strategies to manage and balance customer needs with infrastructure investments, such as demand flexibility. This report focuses on demand flexibility—the ability to reduce, shift, shed, generate, or modulate loads in response to building and grid needs—to reduce the need for costly grid upgrades by deferring investment needs and increase system reliability by shifting electricity usage during periods of high risk. Specifically, we focus on the emerging characteristics of business models for demand flexibility as a framework to understand how demand flexibility programs generate value. In this report, we focus on the life cycle of demand flexibility programs, which provides information on value creation and describes the various deployment phases program implementers navigate from initial program conceptualization through to program expansion and replication to new customer segments and regions. This report characterizes the key phases of the demand flexibility program life cycle, identifies existing challenges across the program deployment phases, and describes lessons learned. This report is part of a series that includes reports on customer relationship management strategies, stakeholder ecosystem management, and program life cycle.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Final Technical Report-WestSmart EV: Western Smart Plug-in Electric Vehicle Community Partnership

The WestSmartEV (WSEV) project has accelerated adoption of plug-in electric vehicles (PEV) throughout the PacifiCorp/Rocky Mountain Power’s (RMP) service territory in the intermountain west by developing a large-scale, sustainable PEV charging infrastructure network with coordinated PEV adoption programs. The project objectives have strategically deployed 79 DC fast charging to create two primary electric interstate highway corridors along I-15 and I-80; incentivized installation of Level 2 AC chargers at workplace locations; incentivized the purchase of PEVs; provided all electric solutions for first-mile and last-mile trips, including electrified mobility service; provided centralized data collection, analysis, modeling, and tool development to inform investment and policy decisions; and developed education outreach materials and conducted workshops across the WSEV region.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗