Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “distributed storage”

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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Postdisaster Routing of Movable Energy Resources for Enhanced Distribution System Resilience: A Deep Reinforcement Learning-Based Approach

The deployment of movable energy resources (MERs) can be an effective strategy to restore critical loads to enhance power system resilience when no other energy sources are available after the occurrence of an extreme event. Since the optimal locations of MERs following an extreme event are dependent on system operating states (e.g., the loads at each node, on/off status of system branches, and so on), existing analytical and population-based approaches must repeat the entire analysis and calculation when the system operating states change. On the contrary, if deep reinforcement learning (DRL)-based algorithms are sufficiently trained with a wide range of scenarios, they can quickly find optimal or near-optimal locations irrespective of changes in system states. A deep Q-learning-based approach is proposed for optimal MER deployment to enhance power system resilience. MERs can be also utilized to complement other types of resources, if available. The proposed approach operates in two stages after the occurrence of extreme events. In the first stage, the distribution network is represented as a graph, and the network is then reconfigured using tie switches by using Kruskal’s spanning forest search algorithm (KSFSA). To maximize critical load recovery, the optimal or near-optimal locations of MERs are chosen in the second stage. Further, case studies on a 33-node distribution system and a modified IEEE 123-node system demonstrate the effectiveness of the proposed approach for postdisaster routing of MERs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Short-Term Load Forecasting Considering EV Charging Loads with Prediction Interval Evaluation

Short-term load forecasting plays a critical role in power system planning and operation. Along with the electrification of various loads, electricity demands are becoming increasingly hard to predict. Notably, the recent rise in electric vehicles (EVs) has further contributed to this unpredictability. To address this issue, this paper proposes a probabilistic load forecasting strategy utilizing Gaussian process regression, structured in a day-ahead manner. While many works focus on deterministic prediction, probabilistic forecasting offers additional insights into variability and uncertainty, enabling more flexible and reliable operation for power systems. To enhance the accuracy of the load forecasting model, the inputs include features related to EV charging habits as well as commonly used weather information. The load forecasting results are evaluated using various metrics, including conventional ones that assess the accuracy of point forecasts, as well as additional metrics that test the reliability of prediction intervals. The proposed load forecasting method is finally tested on real residential power consumption data and EV charging data sampled from real-world sources. The results prove that the new features can greatly improve the performance of the load forecasting method.

electrical vehicle↗
DOE OSTI2020

Next-generation geospatial-temporal information technologies for disaster management

Traditional geographic information systems (GIS) have been disrupted by the emergence of Big Data in the form of geo-coded raster, vector, and time-series Internet-of-Things data. This article discusses the application of new scalable technologies that go far beyond relational databases and file-based storage on spinning disk or tape to incorporate both storage and processing data in the same platform. The roles of the Apache Hadoop Distributed File Systems and NoSQL key-value stores such as the Apache Hbase are discussed, along with indexing schemes that optimally support geospatial-temporal use. Here, we highlight how this new approach can rapidly search multiple GIS data layers to obtain insights in the context of early warning, impact evaluation, response, and recovery to earthquake and wildfire disasters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Reinforcement Learning for Distribution Grid Optimization (PyCIGAR) v0.1

PyCIGAR is a python software package that merges off-the-shelf reinforcement learning libraries (RLLib and Ray) with electric power distribution system simulation tools (OpenDSS and a custom power flow solver built by LBL). PyCIGAR enables the training of neural networks to optimize the behavior of different components in the electric distribution grid, such as control systems in photovoltaic rooftop solar inverters and electric battery storage systems. The software package has been used to train neural networks to update settings in photovoltaic rooftop solar inverter control systems to mitigate cyber attacks on other solar photovoltaic rooftop devices.

Arnold, Daniel↗
DOE OSTI2022

Local Power: Comparing County-Level Renewable Energy Potential to Consumption Using the SLOPE Platform

Wide-scale deployment of renewable energy technologies has the potential to significantly reduce greenhouse gas emissions and mitigate the effects of climate change. Many communities have ambitious clean energy goals with targets for locally generated renewable energy. To inform state and local clean energy planning, analysts from the Joint Institute for Strategic Energy Analysis (JISEA) and National Renewable Energy Laboratory (NREL) used data from NREL's State and Local Planning for Energy (SLOPE) platform to compare annual technical generation potential of renewable energy technologies to modeled electricity consumption in every county of the contiguous United States. Annual costs were calculated to produce a 20% share of electricity consumed annually from each technology to examine localized cost effectiveness of a diversified mix of generation sources. For example, combining distributed and utility-scale wind and solar generation can offset the need for storage and nonintermittent fossil resources to achieve high deployment of renewables. This county-level analysis provides insight into where localized renewable energy generation could cost-effectively match annual electricity consumption.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Uncertainties in Local Intense Precipitation Flood Modeling

In the Task 1 report for the U.S. Nuclear Regulatory Commission (NRC) funded Local Intense Precipitation (LIP) Probabilistic Flood Hazard Assessment (PFHA) Pilot Study, the Pacific Northwest National Laboratory (PNNL) reviewed hydrologic and hydraulic modeling approaches for estimation of LIP flooding and their implementation in readily available simulation software packages (Prasad and Yuan 2020). NRC’s interest is in developing guidance for performing LIP PFHA at nuclear power plant (NPP) sites. This report describes the work done under Task 2 of the study that focused on describing uncertainties associated with LIP flood modeling. Two main types of uncertainties exist in LIP flood modeling: (1) aleatory uncertainties that arise from the inherent natural variability of the hydrometeorological system and (2) epistemic uncertainties that arise from the analysts’ incomplete knowledge of the hydrometeorological and site configuration. Principal sources for aleatory variability in LIP flood simulations at NPP sites include precipitation (i.e., magnitude, duration, and temporal distribution of LIP events), initial conditions (e.g., soil moisture content, stormwater drainage discharge, surface storage), boundary conditions (e.g., upstream discharge, downstream water surface elevations), and long-term temporal trends (e.g., climate change). This report describes approaches used for estimating the aleatory variability in precipitation including precipitation-frequency analysis, numerical weather prediction, and stochastic weather generation. Data sources that can be used to estimate aleatory variability in initial and boundary conditions are also described. Approaches to include effects of long-term trends like those from climate change into estimation of aleatory variability are summarized. Sources of epistemic uncertainty in LIP flood simulations at NPP sites include process representation (e.g., multiple approaches to represent runoff generation, stormwater drainage, and hydraulic routing), site configurations (e.g., site layout, flow features, status of temporary flood protection, blockage of drains), model resolution, and long-term temporal trends (e.g., known/planned site alterations, land-use changes at and in the vicinity of the site). The report describes alternative process representations (methods and models), particularly those implemented in the LIP flood simulation software packages reviewed in Task 1 report, and lists the associated model parameters. Approaches for estimating model parameters when surface and subsurface water exchanges occur are also described.

42 ENGINEERING↗
DOE OSTI2025

Microgrid Procurement Checklist Training: Part 1

Learn how to navigate the Microgrid Procurement Checklist, a key resource for federal agencies developing microgrid projects. This first session will provide a step-by-step walkthrough of the checklist, helping participants understand its structure, key tasks, and how to apply it in project planning.

25 ENERGY STORAGE↗
DOE OSTI2025

Voices from Rural Electric Cooperatives: A Call for a DER Integration Playbook for Rural and Agricultural Income & Savings from Renewable Energy (RAISE)

Rural-serving electric utilities (RSEUs)—including electric cooperatives, municipal systems, and small investor-owned utilities—are essential players in the evolving energy landscape. These community-focused entities are increasingly being asked to consider distributed energy resources (DERs) such as rooftop and ground-mounted solar, wind, battery storage, smart water heaters, and demand response programs (NRECA 2025; Lenhart et al. 2020). Yet the path to DER adoption is far from straightforward in the rural context. This report is based on in-depth interviews with managers of rural electric cooperatives across the United States. These conversations offer a grounded, unvarnished look at how DERs are perceived, what barriers exist, and what conditions might enable integration and adoption. While the utilities interviewed vary in geography, size, and DER experience, several clear and common themes emerged across electric cooperatives such that the report recommends the development of a DER Integration Runbook to help utilities assess readiness, define local use cases, engage stakeholders, pilot projects, and iterate, expanding over time.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Role of On-Site Generation in Carbon Emissions and Utility Bill Savings under Different Electric Grid Scenarios

Energy-efficient and sustainable technologies are necessary to lower energy and carbon footprints. Many technologies are being pursued to meet the increasing energy demand in buildings. An attractive option is efficient utilization of available energy resources, including renewables, to support current and future building energy needs while targeting grid resiliency, energy, and environmental security at an affordable cost via on-site cogeneration-based approaches. This must include energy-efficient technologies with lower greenhouse gas emissions and optimized cost, performance, and reliability. This paper presents the economic and environmental benefits associated with power technologies such as thermionics and solid oxide fuel cells. Hybrid configurations consisting of heat pumps, power systems, and renewable photovoltaics in cogeneration and trigeneration modes of operation are presented. The role of such technologies in lowering CO2 emissions while improving energy resiliency and serving the needs of underprivileged communities is discussed. The key barriers of affordability and potential solutions for large-scale implementation of these promising technologies are reviewed. Case studies demonstrating the influence of power rating, electrical efficiency, design configuration, carbon dioxide intensity of the grid, and fuel on annual greenhouse gas emissions are presented for residential and commercial buildings.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Life Cycle Emissions Factors for Electricity Generation Technologies

This dataset consists of a table containing the distribution of literature estimates of greenhouse gas emissions for the following electricity generation and storage technologies: biopower, coal, concentrating solar power, geothermal, hydrogen storage, hydropower, lithium-ion battery storage, natural gas, nuclear, ocean, oil, photovoltaic, pumped-storage hydropower, and wind. Quartile estimates of life cycle emissions factors in units of grams of carbon dioxide equivalent per kilowatt hour of generation (g CO2e/kWh) are provided for the following life cycle stages: one-time upstream, ongoing combustion, ongoing non-combustion, one-time downstream, and total. Literature estimates were compiled by the LCA Harmonization study and subsequent updates, as detailed in the factsheet which accompanies this dataset, https://www.nlr.gov/docs/fy21osti/80580.pdf .

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Effect of CO 2 -brine-rock reactions on pore architecture and permeability in dolostone: Implications for CO 2 storage and EOR

Geologic carbon sequestration (GCS) is considered a feasible technology for storing substantive volumes of greenhouse gases in subsurface geological formations. In the reservoir, far from carbon dioxide (CO 2 ) injection wells or in post-injection scenarios, diffusion dominates over advection. This condition conjoins with spatially distributed geochemical reactions to induce heterogeneous changes in pore architecture, i.e. pore body and throat sizes or surface roughness. These changes can affect CO 2 transport properties and storage capacity. In this work, we investigated mineral dissolution and precipitation in dolomite samples saturated with a CO 2 -saturated brine at 93 °C and 34.5 MPa, aged without flow. Two rock types samples, i.e. intergranular- and vuggy-dominant, were selected to investigate changes in pore size, porosity and permeability under reactive conditions. Mineral dissolution and precipitation were characterized using scanning electron microscopy. Changes in pore size were quantified via time-domain nuclear magnetic resonance (TD-NMR) transverse relaxation time (T 2 ) and diffusion coefficient (D) distributions. We show that mineral dissolution likely occurs in highly permeable pathways. These observations are confirmed through analysis of (T 2 ) and diffusion coefficient (D) distributions. In contrast to results during CO 2 -enriched brine continuous injection, mineral precipitation was observed in micropores. The leftward shift of the T 2 peaks, corresponding to micropores, also evidenced mineral precipitation in lowpermeability zones. However, microscale alterations resulted only in a subtle increase in porosity and permeability. Results in this study shed light on effects of geochemical reactions on alteration of rock properties in diffusion-dominated regions during CO 2 storage.

58 GEOSCIENCES↗
DOE OSTI2023

Outage Forecast-Based Preventative Scheduling Model for Distribution System Resilience Enhancement: Preprint

Distribution system resilience enhancement is an important topic to ensure customers have access to the power supply during extreme events. In fact, certain weather-related extreme events can be predicted ahead of time. Therefore, it is important to investigate how to predict grid outages using extreme weather forecasts, and how outage predictions can be incorporated into distribution system resilience enhancement. In this paper, a preventative scheduling model for distribution systems is proposed. The model targets at allocating resources, especially mobile responsive resources such as mobile backup generators and mobile energy storage systems, to prepare for an extreme event in the day-ahead context. To achieve efficient resource allocation and scheduling, a machine learning-based outage prediction module is developed to predict vulnerable or risky segments of the distribution system based on historical operating records and extreme weather event forecasts. By integrating the outage prediction results into the scheduling model, optimal resource allocation can be derived to help distribution systems prepare for an upcoming event and improve resilience performance. A real distribution feeder in North Carolina, U.S. is used in the case study to validate the proposed approach.

distributed energy resources↗
DOE OSTI2023

Outage Forecast-Based Preventative Scheduling Model for Distribution System Resilience Enhancement

Distribution system resilience enhancement is an important topic to ensure customers have access to power supply during extreme events. In fact, certain weather-related extreme events can be predicted ahead of time. Therefore, it is important to investigate how to predict grid outages using extreme weather forecasts, and how outage predictions can be incorporated into distribution system resilience enhancement. In this paper, a preventative scheduling model for distribution systems is proposed. The model targets at allocating resources, especially mobile responsive resources such as mobile backup generators and mobile energy storage systems, to prepare for an extreme event in the day-ahead context. To achieve efficient resource allocation and scheduling, a machine learning-based outage prediction module is developed to predict vulnerable or risky segments of the distribution system based on historical operating records and extreme weather event forecast. By integrating the outage prediction results into the scheduling model, optimal resource allocation can be derived to help distribution systems prepare for an upcoming event and improve resilience performance. A real distribution feeder in North Carolina, U.S. is used in the case study to validate the proposed approach.

distributed energy resources↗
DOE OSTI2025

Exploring the Future Energy Value of Long-Duration Energy Storage

Long-duration energy storage is commonly viewed as a key technology for providing flexibility to the grid and broader energy systems over a multidecadal time frame. However, prior work has typically used present-day grid infrastructures to characterize the relationship between the duration and arbitrage value of storage in electricity markets. This study leverages established National Renewable Energy Laboratory grid planning and operations tools, analysis, and data to execute a price-taker model of an energy storage system for several 8760 h price series representative of current and future contiguous United States grid infrastructures with varying shares of variable renewable energy (VRE). We find that the total value of energy storage typically increases with VRE shares, but any increase in the relative value of longer storage durations over time depends on the region and grid mix. Some regions see incremental value increasing notably, up to 20–40 h in 2050, while others do not. The negative effect of lower roundtrip efficiency on value is also found to be scenario-dependent, with the energy value in higher VRE scenarios being less sensitive to roundtrip efficiency and more supportive of longer storage durations. Long-duration storage value and deployment potential are a function of evolving electricity sector infrastructure, markets, and policy, making it critical to consistently revisit potential long-duration storage contributions to the grid.

14 SOLAR ENERGY↗
DOE OSTI2020

Protection schemes used in North American microgrids

This study reviewed existing conventional and nonconventional protection schemes for grid-connected and islanded mode operations in North American microgrid projects. The microgrid projects investigated in this study used different types of distributed energy resources (DERs) and integrated hydropower/diesel generators, gas/steam/wind turbines, and photovoltaic systems with energy storage. In this work, conventional protection schemes were defined as those within the IEEE Standard C37.2-2008, whereas nonconventional schemes were those not defined within this standard. The pros and cons of conventional and nonconventional protection schemes were discussed in detail. The overvoltage, undervoltage, and frequency elements were the most common conventional protection schemes applied in microgrid projects in North America. These protection elements were used to detect the islanded conditions and faults that could not be sensed by overcurrent relays because of small fault currents contributed by low-inertia DERs and power-electronic sources. Directional overcurrent elements were used to distinguish between external (grid) and internal (microgrid) faults. Adaptive protection was the most popular nonconventional protection scheme applied to the microgrid projects. In conclusion, different types of DERs and operational modes must be considered in order to address the protection and control challenges of each microgrid and to obtain the best technical and economical solution.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Energy storage emerging: A perspective from the Joint Center for Energy Storage Research

Energy storage is an integral part of modern society. A contemporary example is the lithium (Li)-ion battery, which enabled the launch of the personal electronics revolution in 1991 and the first commercial electric vehicles in 2010. Most recently, Li-ion batteries have expanded into the electricity grid to firm variable renewable generation, increasing the efficiency and effectiveness of transmission and distribution. Important applications continue to emerge including decarbonization of heavy-duty vehicles, rail, maritime shipping, and aviation and the growth of renewable electricity and storage on the grid. This perspective compares energy storage needs and priorities in 2010 with those now and those emerging over the next few decades. The diversity of demands for energy storage requires a diversity of purpose-built batteries designed to meet disparate applications. Advances in the frontier of battery research to achieve transformative performance spanning energy and power density, capacity, charge/discharge times, cost, lifetime, and safety are highlighted, along with strategic research refinements made by the Joint Center for Energy Storage Research (JCESR) and the broader community to accommodate the changing storage needs and priorities. Innovative experimental tools with higher spatial and temporal resolution, in situ and operando characterization, first-principles simulation, high throughput computation, machine learning, and artificial intelligence work collectively to reveal the origins of the electrochemical phenomena that enable new means of energy storage. This knowledge allows a constructionist approach to materials, chemistries, and architectures, where each atom or molecule plays a prescribed role in realizing batteries with unique performance profiles suitable for emergent demands.

Trahey, Lynn↗