Search NASA⌕ Search

SEARCH · Search NASA

Results for “Electricity Infrastructure”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data

Many distribution network monitoring and control applications - including state estimation, volt/VAR optimization, and network reconfiguration - rely on accurate network models; however, the network models maintained by utilities can become outdated because of restoration activities, network reconfiguration, and missing data. With the widespread deployment of advanced metering infrastructure (AMI), abundant measurement data from low-voltage secondary networks are available. The AMI measurement data can be used for phase identification to improve the network models. Although the existing phase identification techniques work well in passive distribution feeders that do not have photovoltaic (PV) generation, they can fail to accurately identify the phases in the presence of PV. This paper proposes a robust phase identification algorithm based on supervised machine learning that accurately identifies the AMI meter phase connectivity in the presence of significant PV generation. The proposed algorithm does not require network topology information or feeder head measurement data. The algorithm is validated using the AMI measurement data collected in the field and the field-validated phase connectivity database on two real distribution feeders from San Diego Gas & Electric Company that have significant PV generation.

advanced metering infrastructure↗
DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data: Preprint

Many distribution network monitoring and control applications - including state estimation, volt/VAR optimization, and network reconfiguration - rely on accurate network models; however, the network models maintained by utilities can become outdated because of restoration activities, network reconfiguration, and missing data. With the widespread deployment of advanced metering infrastructure (AMI), abundant measurement data from low-voltage secondary networks are available. The AMI measurement data can be used for phase identification to improve the network models. Although the existing phase identification techniques work well in passive distribution feeders that do not have photovoltaic (PV) generation, they can fail to accurately identify the phases in the presence of PV. This paper proposes a robust phase identification algorithm based on supervised machine learning that accurately identifies the AMI meter phase connectivity in the presence of significant PV generation. The proposed algorithm does not require network topology information or feeder head measurement data. The algorithm is validated using the AMI measurement data collected in the field and the field-validated phase connectivity database on two real distribution feeders from San Diego Gas & Electric Company that have significant PV generation.

advanced metering infrastructure↗
DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data

Many distribution network monitoring and control applications - including state estimation, Volt/VAr optimization, and network reconfiguration - rely on accurate network models; however, the network models maintained by utilities can become outdated because of restoration activities, network reconfiguration, and missing data. With the widespread deployment of advanced metering infrastructure (AMI), abundant measurement data from low-voltage secondary networks are available. The AMI measurement data can be used for phase identification to improve the network models. Although the existing phase identification techniques work well in passive distribution feeders that do not have photovoltaic (PV) generation, they can fail to accurately identify the phases in the presence of PV. This paper proposes a robust phase identification algorithm based on supervised machine learning that accurately identifies the AMI meter phase connectivity in the presence of significant PV generation. The proposed algorithm does not require network topology information or feeder-head measurement data. The algorithm is validated using the AMI measurement data collected in the field and the field-validated phase connectivity database on two real distribution feeders from San Diego Gas & Electric Company that have significant PV generation.

advanced metering infrastructure (AMI)↗
NASA NTRS1995

Building a Successful Security Infrastructure: What You Want vs. What You Need vs. What You Can Afford

With the fast growing popularity of the Internet, many organizations are racing to get onto the on-ramp to the Information Superhighway. However, with frequent headlines such as 'Hackers' break in at General Electric raises questions about the Net's Security', 'Internet Security Imperiled - Hackers steal data that could threaten computers world-wide' and 'Stanford Computer system infiltrated; Security fears grow', organizations find themselves rethinking their approach to the on-ramp. Is the Internet safe? What do I need to do to protect my organization? Will hackers try to break into my systems? These are questions many organizations are asking themselves today. In order to safely travel along the Information Superhighway, organizations need a strong security framework. Developing such a framework for a computer site, whether it be just a few dozen hosts or several thousand hosts is not an easy task. The security infrastructure for a site is often developed piece-by-piece in response to security incidents which have affected that site over time. Or worse yet, no coordinated effort has been dedicated toward security. The end result is that many sites are still poorly prepared to handle the security dangers of the Internet. This paper presents guidelines for building a successful security infrastructure. The problem is addressed in a cookbook style method. First is a discussion on how to identify your assets and evaluate the threats to those assets; next are suggestions and tips for identifying the weak areas in your security armor. Armed with this information we can begin to think about what you really need for your site and what you can afford. In this stage of the process we examine the different categories of security tools and products that are available and then present some tips for deciding what is best for your site.

Crabb, Michele D.↗
DOE OSTI2024

Rooftop Photovoltaics and Electric Vehicle Co-Adoption: Attitudes, Norms, Diffusion, and Economics

The overall objectives of the project were to (1) Identify personal and social norms, peer effects, demographics and contextual factors (such as household energy expenditures, experience with outages, charging infrastructure availability) that may facilitate or hinder Solar-EV co-adoption; (2) outline strategies for the design of programs and practices that will enable reasoned RPV and EV adoption and their co-adoption; and (3) assemble a dataset comprised of survey responses of RPV-EV co-adopters, RPV-only adopters, EV-only adopters, and non-adopters. These objectives were only partially fulfilled given the decision to terminate the project. The research team was able to complete the semi-structured interviews, the core BY1 task and summarize the results below. As well, once the team learned of the research project’s fate, it sought and obtained the DOE SETO Technical Manager’s approval to repurpose funds for BY2 tasks to the extent feasible. Rather than invest further resources in expanding the pool of potential survey respondents and having no budget to survey, the research team repurposed funds subject and undertook a more limited (geographically) survey, but also expanded to it to include contingent valuation questions. The analysis of the survey data will necessarily be delayed given the lack of budget support, although some information on the respondent sample is in the results. Given a lack of attention to co-adoption previously, the insights generated in this project should assist decision-makers and industry actors in the development of programs and practices that enable reasoned RPV adoption and EV adoption and their co-adoption.

14 SOLAR ENERGY↗
DOE OSTI2022

Joint Planning of Electricity Transmission and Hydrogen Transportation Networks

The abundance and uneven distribution of renewable energy might cause congestion and curtailment in electric power systems. Transmission expansions can potentially alleviate transmission congestion and reduce renewable energy curtailment. On the other hand, with the substantial cost reduction of electrolyzer technology and the continuing rise of hydrogen demand, converting surplus renewable energy to hydrogen provides synergistic benefits to power and hydrogen systems, but consequently requires joint planning of the two systems. To this end, we propose a joint planning approach for power transmission and hydrogen transportation networks to coordinately optimize the investment and operation of power and hydrogen infrastructure. In our proposed model, detailed truck routing, pipeline, and hydrogen storage are formulated to quantify the flexibility of hydrogen transportation system. A robust joint planning approach is also proposed to address various uncertainties from renewable energy, electric load, and hydrogen demand. Furthermore, our numerical simulations show that the proposed joint planning model can save the total system cost, reduce renewable energy curtailment, and increase the utilization level of transmission lines.

08 HYDROGEN↗
DOE OSTI2020

Climate Influences on Capacity Expansion Planning with Application to the Western U.S

Electric power system planners utilize a variety of planning tools to inform decisions concerning generation and transmission additions to the electric grid, the need for operational changes, and to evaluate potential stressors on the system. Numerous factors contribute to the planning process including projected fuel and technology costs, policy and load profiles. There is also a growing recognition of the interdependency of the electric grid with other natural and engineered systems. Here we explore how future climate change and hydropower operability might influence decisions related to electricity capacity expansion planning and operations. To do so we assemble a multi-model framework. Specifically, water resource modeling is used to simulate climate impacts on future water supply for thermoelectric and hydropower generation. Separately, temperature impacts on electricity load are evaluated. Together, these climate factors spatially constrain a capacity expansion model that projects generation and transmission additions to the grid. The projected new capacity-builds are then evaluated on their operations, reliability, and cost under average and extreme climate conditions using production cost modeling. This coupled framework is demonstrated on the electric grid in the Western U.S., supporting capacity expansion planning by WECC, the North American Electric Reliability Corporation (NERC) regional entity responsible for reliability assurance of the Western Interconnection. This region was selected in part because the West is unique in that it has high potential for renewable penetrations and is experiencing large retirements/displacements of baseload resources, primarily coal, leading to possible operational challenges in terms of changing resource mix and the need for resource flexibility. Toward this challenge, planning scenarios encompass a range of alternative energy, climate and drought futures. In this context we explore answers to two strategic questions: 1) How does changing climate influence electricity expansion planning (generation and transmission) and future operations, including type and capacity of new builds, system reliability, cost and environmental impacts? 2) How does the representation of hydropower in the modeling framework influence the evaluation of bulk power system operations? Results indicate that climate has a measurable influence on recommendations concerning the capacity, type and location of new generation and transmission additions, with up to 17 GW additional capacity needed by 2038 to meet peak loads (~6.6% increase over capacity-builds based on historical climate). The extent of additional infrastructure needs is strongly influenced by future water availability for hydropower and the potential deployment of demand response technologies. Systems designed for future climate conditions were found to maintain high system reliability under a range of electricity and water availability scenarios (including significant drought), with minimal system curtailments. Additional capacity needs due to higher load tend to increase cumulative 20-year investment and operating costs by $\$$5-$\$$17 billion and generation costs increase by 9 to 19%. Finally, changing the representation of hydropower flexibility has a relatively small influence on capacity expansion in the Western Interconnection through 2038, but hydropower flexibility impacts generation costs to a similar extent as climate.

13 HYDRO ENERGY↗
DOE OSTI2023

A novel large-scale EV charging scheduling algorithm considering V2G and reactive power management based on ADMM

Electric vehicle aggregators (EVAs) that utilize vehicle-to-grid (V2G) technologies can function as both controllable loads and virtual power plants, providing key energy management services to the distribution system operator (DSO). EVAs can also balance the grid’s reactive power as a virtual static VAR compensator (SVC) and provide voltage stability by utilizing advanced electric vehicle (EV) chargers that are capable of four-quadrant operations to provide reactive power management. Finally, managed charging can benefit EVAs themselves by minimizing power factor penalties in their electricity bills. In this paper, we propose a novel EV charging scheduling algorithm based on a hierarchical distributed optimization framework that minimizes peak load and provides reactive power compensation for the DSO by collaboration with EVAs that manage both the active and the reactive charging and discharging power of participating EVs. Utilizing the alternative direction method of multipliers (ADMM), the proposed distributed optimization approach scales well with increased EV charging infrastructure by balancing active and reactive power while decreasing computational burden. In our proposed hierarchical approach, each EVA schedules the active and reactive EV charging and discharging power for 1) reactive power compensation in order to minimize power factor penalty and electricity cost accrued by the EVA, 2) satisfaction of each EV’s energy demand at minimal charging cost, and 3) peak shaving and load management for the DSO. When compared with an uncoordinated charging model, the efficacy of this proposed model is successfully demonstrated through a 300% decreased peak EV load for the DSO, 28% lower electricity costs for EV users, and 98.55% smaller power factor penalty, along with 17.58% lower overall electricity costs, for EVAs. The performance of our approach is validated in a case study with 50 EVs at multiple EVAs in an IEEE 13-bus test case and compared the results with uncoordinated EV charging.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

InfraSys (Infrastructure Systems) [SWR-24-42]

This package implements a data store for components and time series in support of Python-based modeling packages. While it is designed to support teams modeling transmission and distribution systems for electrical grids, it can be used by any package that needs to store components (e.g., generators and buses) that have quantities (e.g., power and voltage) which may vary over time. The package was inspired by InfrastructureSystems.jl (https://github.com/NREL-Sienna/InfrastructureSystems.jl).

Thom, Daniel↗
DOE OSTI2022

Communication Requirements for Price-Based Grid Coordination

Meeting ambitious goals for carbon reduction and supporting an electric grid with high levels of renewable energy supply will require significant flexibility from the demand side. This paper outlines a system architecture and communication technology infrastructure to enable dynamic pricing to be used to finely tune coordination between the grid and its customers. Taking a cue from the success of Internet architecture, this system — Price-Based Grid Coordination — emphasizes simplicity and universality. It enables a wide variety of ways for prices and other signals to pass from the grid to individual flexible loads, including multiple possible locations for the intelligence that combines price signals with device functional needs. The paper includes a reference data model to describe how information from the utility level can be conveyed to customer devices, but independent of any particular protocol. The paper also summarizes technology standards development needs, and reviews research needs to address the full spectrum of coordination scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Power System Waveform Datasets for Machine Learning

The desire for increased visibility across the electricity grid will necessarily increase the deployment of sensing and measurement devices and associated data management needs to unprecedented levels. For the existing sensing and measurement infrastructure, there remains a great amount of “value” yet to be extracted through advanced data management and analytics. Availability of more data will not, by itself, lead to changes in grid visibility, security, and resiliency. To create the predictive and prescriptive environment required to enable new markets and transactions for customer revenue and a reliable grid, the data must be collected, organized, evaluated, and analyzed using sophisticated algorithms to provide actionable information allowing operators and customers to reliably manage an increasingly complex grid. Progress in artificial intelligence (AI) has been largely driven by large, publicly available datasets that can be used to train AI algorithms such as MNIST, a database of handwritten images of digits, and ImageNet, an image database of everyday objects. These types of publicly available databases of real-world training datasets have been largely credited for advancement of image processing, computer vision, and deep learning algorithms that these use cases deploy. However, in the power systems industry to date, there are few databases with proper event labeling, and data access to a publicly available collection of power system event waveforms that will allow users to interact with grid signature data. Publicly available datasets of power system event waveforms, such as the DOE/EPRI dataset, often lack critical metadata or contain limited examples of each event type, and data formats vary widely across these datasets.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Sustainable Port Operations: Powered by NREL

Seaports are vital economic hubs that allow the United States to compete on a global scale. But the heavy vehicles and cargo equipment that enable their operations also emit harmful air pollutants and greenhouse gas emissions. For nearly two decades, National Renewable Energy Laboratory (NREL) researchers have worked toward comprehensive seaport decarbonization. They fuse world-class analysis with deep vehicle and transportation systems knowledge to guide strategic deployment of low- and zero-emissions vehicles, charging and refueling infrastructure, and grid improvements. Together, these capabilities can enable sustainable port operations. This fact sheet outlines major seaport and airport decarbonization capabilities across the laboratory, including: fleet research, energy data, and insights for decarbonization; comprehensive hydrogen infrastructure deployment; optimized charging through grid integration; strategic blueprinting for clean, optimized technology deployment; and integrating diversity, equity, inclusion, and accessibility considerations into decarbonization efforts.

ADVANCED PROPULSION SYSTEMS,ENERGY CONSERVATION, C↗
DOE OSTI2025

Dynamic Life Cycle Assessment for Evaluating the Global Warming Potential of Geothermal Energy Production Using Inactive Oil and Gas Wells for District Heating in Tuttle, Oklahoma

Repurposing abandoned oil and gas infrastructure for geothermal energy production has great potential to reduce greenhouse gas (GHG) emissions. This study quantified the life cycle global warming potential of geothermal energy production using four inactive oil and gas wells repurposed for district heating in Tuttle, Oklahoma. A cradle-to-grave prospective life cycle assessment was performed to compare GHG emissions between the geothermal district heating system and conventional natural gas-fired heating system from 2020 to 2050. For initial implementation of the geothermal system, we investigated two approaches: 1) repurposing abandoned infrastructure from a nearby oil and gas well site, and 2) production and injection well drillings including new construction of a central heat exchange station. Environmental impacts from the geothermal system were estimated for five scenarios where a natural gas peaking boiler is incorporated to supply peak heat demand. The prospective results indicated that cumulative reduction in GHG emissions from transitioning to the geothermal district heating system increase over time as a function of future renewable resource penetration and technological advancements within electricity, fuel, and steel production. Over 30 years, the global warming potential associated with the district heating demand will have been reduced by up to 24 % with the repurposed system. These results imply that repurposing existing oil and gas infrastructure for geothermal energy systems of district heating will bring future climate benefits.

abandoned oil and gas wells↗
DOE OSTI2026

SERA: A Hydrogen Infrastructure Capacity Expansion Model

The Scenario Evaluation and Regionalization Analysis (SERA) model is an infrastructure planning optimization model that can guide hydrogen production, delivery, and end-use investment decisions and accelerate the adoption of low-cost hydrogen at scale, whether for fuel cell electric vehicles or non-transportation applications. In this talk, we will review the SERA model objective function as well as the data inputs and outputs. We will also look at a SERA case study identifying potential dispensed costs of hydrogen along major refueling corridors throughout the United States. In addition to the SERA model, Justin will also discuss his recent work for the Office of Manufacturing and Energy Supply Chains on electrolyzer supply chain readiness, and his work for the Hydrogen Fuel Cell Technologies Office and Environmental Protection Agency on the levelized cost of dispensed hydrogen for heavy-duty trucking.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Solar Power + Electric Vehicle Charging: Capturing Synergies in Minnesota

Despite the state policy support, significant market barriers to decarbonization of transportation remain. Analysis conducted by the National Renewable Energy Laboratory (NREL) and others confirm that without routine access to charging infrastructure, both at home and in non-residential locations, bullish market forecasts are unlikely to materialize. Similarly, the expansion of EV charging and variable renewable energy deployment each have potentially disruptive and costly effects on the distribution grid. Finally, electrification of passenger vehicles is not enough to decarbonize transportation without pathways to carbon-free electrification, linking EV charging and clean energy generation. This document examines the opportunity to jointly deploy solar energy and EV charging infrastructure to capture synergistic value, mitigate for risks associated with integrating and scaling both solar and EV charging, and accelerate their mutual deployment.

14 SOLAR ENERGY↗
DOE OSTI2022

Ransomware and Today's Electric Grid

Cyberattacks come in many forms, including one of the newest and most threatening of attacks: ransomware. The rise in ransomware impacts businesses small and large -- and have the potential to significantly disrupt the operations of critical infrastructure. This presentation will explain what utilities can expect from a ransomware attack, sharing examples from specific events, provide guidance on how to prepare for the unknown, and offer possible ways to respond when an attack occurs. The presentation will also discuss cybersecurity governance as an effective method in taking preventative action to ransomware by identifying and addressing vulnerabilities through monitoring and assessment.

assessment↗
DOE OSTI2024

Control and Optimization of Energy Storage System in Power Distribution System

The widespread adoption of electric vehicles (EVs) and transportation electrification is encumbered by two chief barriers: i) the limited driving range of EVs in the market today and ii) inadequate fast-charging infrastructure for long-distance trips. Extreme fast charging (XFC) technology can recharge EVs in less than 10 minutes for 200 miles range. Firstly, a novel robust optimization-based mixed integer linear programming model is proposed to size a battery energy storage system (BESS) and PV system in an XFCS. In this part, it is assumed that the sizing and location of the XFCS are known. Secondly, the aforesaid assumption is relaxed, and a strategic multi-period coordinated planning model is proposed to optimally site and size BESS-assisted charging stations in a highway transportation network and PV systems in a power distribution network by considering the coupling between both networks. Optimal operation and control of BESS-assisted EV charging stations are vital to alleviate the adverse impact of extreme fast charging of EVs on the host power network. A joint solution is proposed to mitigate the steady state and transient impact of extremefast charging of EVs and ensure grid-friendly integration of XFCSs with the host grid. Lastly, to make the operation of the XFCS cost-effective, a multi-layered energy management framework is proposed for the XFCS by considering forecast uncertainties, monthly demand charges reduction, and BESS degradation.

24 POWER TRANSMISSION AND DISTRIBUTION↗