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

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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Techno-Economic Analysis for a Potential Geothermal District Heating System in Tuttle, Oklahoma: Preprint

Geothermal deep direct use (DDU) has potential across a wide swath of the United States but is underutilized due to challenging project economics associated with developing a deep geothermal resource for a large-scale and variable heat demand. The National Renewable Energy Laboratory (NREL) and University of Oklahoma evaluated the feasibility of a geothermal district heating (GDH) and cooling system in two schools and 250 houses by utilizing existing oil and gas (O&G) wells in Tuttle, Oklahoma. Heating and cooling demand in the two schools and a typical single-family house were modeled using EnergyPlus building energy simulation software. The modeling results indicated that annual heating demand in two schools and 250 houses is approximately 2.61 GWhth, and cooling demand in the two schools is approximately 2.65 GWhth. In this scope, the techno-economic analysis (TEA) was conducted using the GEOPHIRES tool combined with the TOUGH2 reservoir simulator. The reservoir performance, including geothermal heat production capacity, was modeled by the reservoir simulator TOUGH2. Then, levelized cost of heat (LCOH) was calculated using GEOPHIRES version 3.0, which includes new features such as hourly heat load optimization and peak performance evaluation. Geothermal reservoir temperature was estimated as 90.5 degrees C at a total depth of 3.3 km by the regional average temperature gradient of 22.8 degrees C/km and validated by cation geothermometer calculations. Four production scenarios with two different well configurations and two different heat load profiles have been developed for well flow rates ranging between 3.1 kg/s and 9.3 kg/s. The LCOH of the district heating and cooling system was calculated between $95 and $210/MWh ($28/MMBtu to $62/MMBtu) for four different production scenarios. Typical natural gas prices for residential customers in Oklahoma have ranged from 9 to 19 $/MMBtu over the past decade, which indicates a challenge for deployment of such a GDH system.

deep direct-use↗
DOE OSTI2023

Techno-Economic Analysis for a Potential Geothermal District Heating System in Tuttle, Oklahoma

Geothermal deep direct use (DDU) has potential across a wide swath of the United States but is underutilized due to challenging project economics associated with developing a deep geothermal resource for a large-scale and variable heat demand. The National Renewable Energy Laboratory (NREL) and University of Oklahoma evaluated the feasibility of a geothermal district heating (GDH) and cooling system in two schools and 250 houses by utilizing existing oil and gas (O&G) wells in Tuttle, Oklahoma. Heating and cooling demand in the two schools and a typical single-family house were modeled using EnergyPlus building energy simulation software. The modeling results indicated that annual heating demand in two schools and 250 houses is approximately 2.61 GWhth, and cooling demand in the two schools is approximately 2.65 GWhth. In this scope, the techno-economic analysis (TEA) was conducted using the GEOPHIRES tool combined with the TOUGH2 reservoir simulator. The reservoir performance, including geothermal heat production capacity, was modeled by the reservoir simulator TOUGH2. Then, levelized cost of heat (LCOH) was calculated using GEOPHIRES version 3.0, which includes new features such as hourly heat load optimization and peak performance evaluation. Geothermal reservoir temperature was estimated as 90.5 degrees C at a total depth of 3.3 km by the regional average temperature gradient of 22.8 degrees C/km and validated by cation geothermometer calculations. Four production scenarios with two different well configurations and two different heat load profiles have been developed for well flow rates ranging between 3.1 kg/s and 9.3 kg/s. The LCOH of the district heating and cooling system was calculated between $95 and $210/MWh ($28/MMBtu to $62/MMBtu) for four different production scenarios. Typical natural gas prices for residential customers in Oklahoma have ranged from 9 to 19 $/MMBtu over the past decade, which indicates a challenge for deployment of such a GDH system.

deep direct-use↗
DOE OSTI2020

Electrification Futures Study Flexible Load Profiles

This data set includes hourly profiles for flexible load developed for the Electrification Futures Study (EFS). The load profiles represent projected end-use electricity demand that is assumed to be flexible (i.e., can be shifted throughout a day) for various scenarios of flexibility (Base, Enhanced), electrification (Reference, Medium, High), and technology advancement (Slow, Moderate, Rapid), and were developed as inputs into the ReEDS model. The quantity of flexible load is estimated using assumptions on the level of flexibility and customer participation within each subsector modeled in the EFS. Detailed assumptions and modeling implementation will be documented in ongoing EFS analyses. Flexible load profiles are provided for a subset of years (2018, 2020, 2024, 2030, 2040, 2050) and are aggregated to the state and sector level. Total electricity load profiles can be found in a related EFS data set (https://dx.doi.org/10.7799/1593122). NOTE: Due to the file size, Mac users may experience issues decompressing the zip files using the Mac Archive Utility. In those cases, decompressing using the command line is recommended. - Mai, Trieu, Paige Jadun, Jeffrey Logan, Colin McMillan, Matteo Muratori, Daniel Steinberg, Laura Vimmerstedt, Ryan Jones, Benjamin Haley, and Brent Nelson. 2018. Electrification Futures Study: Scenarios of Electric Technology Adoption and Power Consumption for the United States. National Renewable Energy Laboratory. NREL/TP-6A20-71500. https://doi.org/10.2172/1459351. - Murphy, Caitlin, Trieu Mai, Yinong Sun, Paige Jadun, Matteo Muratori, Brent Nelson, Ryan Jones. Forthcoming. Electrification Futures Study: Scenarios of Power System Evolution and Infrastructure Development for the United States. National Renewable Energy Laboratory. - Sun, Yinong, Paige Jadun, Brent Nelson, Matteo Muratori, Caitlin Murphy, Jeffrey Logan, and Trieu Mai. Forthcoming. Electrification Futures Study: Methodological Approaches for Assessing Long-Term Power System Impacts of End-Use Electrification. National Renewable Energy Laboratory.

buildings↗
NASA NTRS1980

Study of fuel cell on-site, integrated energy systems in residential/commercial applications

Three building applications were selected for a detailed study: a low rise apartment building; a retail store, and a hospital. Building design data were then specified for each application, based on the design and construction of typical, actual buildings. Finally, a computerized building loads analysis program was used to estimate hourly end use load profiles for each building. Conventional and fuel cell based energy systems were designed and simulated for each building in each location. Based on the results of a computer simulation of each energy system, levelized annual costs and annual energy consumptions were calculated for all systems.

Wakefield, R. A.↗
DOE OSTI2023

Enhanced Iodine Capture Using a Postsynthetically Modified Thione–Silver Zeolitic Imidazole Framework

Efficient management of radionuclides that are released from various processes in the nuclear fuel cycle is of significant importance. Among these nuclides, radioactive iodine (mainly 129 I and 131 I) is a major concern due to the risk it poses to the environment and to human health; thus, the development of materials that can capture and safely store radioactive iodine is crucial. Herein, a novel silver-thione-functionalized zeolitic imidazole framework (ZIF) was synthesized via post-synthetic modification and assessed for its iodine uptake capabilities alongside the parent ZIF-8 and intermediate materials. A solvent-assisted ligand exchange procedure was used to replace the 2-methylimidazole linkers in ZIF-8 with 2-mercaptoimidazole, forming the intermediate compound ZIF-8=S, which was reacted with AgNO 3 to yield the ZIF-8=S-Ag + composite for iodine uptake. Despite possessing the lowest BET surface area of the derivatives, the Ag-functionalized material demonstrated superior I 2 adsorption in terms of both maximum capacity (550 g I 2 /mol) and rapid kinetics (50% loading achieved in 5 hrs, saturation in 50 hrs) compared to our pristine ZIF-8, which reached 450 g I 2 /mol after 150 hours and 50% loading in 25 hours. This improvement is attributed to the presence of the Ag + ions, which provide a strong chemical driving force to form stable Ag-I species. In conclusion, the results of this study contribute to a broader understanding of the strategies that can be employed to engineer adsorbents with robust iodine uptake behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Heuristic Dispatch Based on Price Signals for Behind-the-Meter PV-Battery Systems in the System Advisor Model

The economic potential of a behind-the-meter (BTM) PV-battery system depends greatly on how the battery is dispatched. Different utility rates, system sizes, generation and load profiles can all require different dispatch strategies. This paper presents price signals dispatch, a new algorithm for automated economic dispatch of BTM PV-battery systems, which utilizes 24-hour PV and load forecasts, degradation data, and utility rates. The algorithm is integrated with the System Advisor Model (SAM) tool and is tested with a nonlinear generic electrochemical battery model. Price signals dispatch outperforms SAM's existing algorithms in cases requiring a balance between demand charge management and energy arbitrage, and in cases where battery degradation imposes a significant cost.

batteries↗
DOE OSTI2021

Managing Solar Photovoltaic Integration in the Western United States: Resource Adequacy Considerations

This study examines the impact of reserve margin-based reliability assessment, as commonly used in capacity expansion models, on planning resource-adequate power systems under high penetrations of solar photovoltaics (PV). As a generation resource, PV is operationally different from the conventional dispatchable resources for which most capacity expansion models were designed. The question this study attempts to answer is whether large amounts of PV on a system (in this case, the Western Interconnection of North America) would bias the results of conventional reserve margin-based capacity expansion modeling towards an over- or under-provisioning of resource adequacy. This analysis used NREL’s Resource Planning Model (RPM) for capacity expansion modeling and NREL’s Probabilistic Resource Adequacy Suite (PRAS) for resource adequacy assessment. RPM uses a reserve margin requirement to enforce resource adequacy. PRAS, a collection of tools for studying the resource adequacy of power systems and the adequacy contributions of individual resources on a probabilistic basis, was used to compute multiple resource adequacy metrics across a number of simulated scenarios and system representations with differing regional detail. In all cases, including high PV penetrations (up to 33% annual generation from PV, interconnection-wide), RPM was able to produce resource-adequate systems as measured by normalized expected unserved energy and loss-of-load expectation results from PRAS. The accuracy of reserve margin approaches depends heavily on the underlying assumptions informing the capacity credit assigned to variable and energy-limited resources, particularly when such resources are abundant in the modeled system. RPM’s standard methodology for estimating variable and flexible resources’ capacity contributions, which is based on the top 100 hours of net load, did not appear to systematically undervalue or overvalue variable generation relative to a more rigorous equivalent firm capacity assessment using PRAS, although both over- and under-valuations were observed in specific scenarios. In the worst cases, the top 100 hour method underestimated the equivalent firm capacity of PV by two percentage points, and overestimated the equivalent firm capacity of PV by five percentage points. Calculating capacity contributions based on the top 10 hours of net load systematically underestimated equivalent firm capacities at more modest PV penetrations, but was often a better approximation of equivalent firm capacity than the existing 100-hour approach in scenarios with higher PV penetrations.

14 SOLAR ENERGY↗
DOE OSTI2025

IM3 + EPRI Data Center Load Projections

This dataset contains scenarios of hourly total electricity demand with and without projected loads from data centers over the period 2022-2040. The root projections without data center demands are identical to those documented in Burleyson et al. 2024. In short, those projections encompass hourly electricity demands for 54 Balancing Authorities (BAs) in the United States across a range of eight of weather and socioeconomic scenarios. Refer to the root dataset and accompanying publication, Burleyson et al. 2025, for information about how those projections were generated. For this derivative dataset we used the base loads from the following scenarios: rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 The root load projections did not reflect the drastic expansion of data centers that has occurred in the last several years to support artificial intelligence and cloud computing. To reflect growth in data center demand, a second set of load projections were created in which we layered in additional data center load projections based on the data center load growth scenarios described in a 2024 report by the Electric Power Research Institute (EPRI): "Powering Intelligence: Analyzing Artificial Intelligence and Data Center Energy Consumption". The EPRI projections from the report are included in this dataset (EPRI_2024_Projections.xlsx). That report contained annual state-level data center load projections for four year-over-year growth rates for data center demands: Low (3.71% annual growth) Moderate (5% annual growth) High (10% annual growth) Higher (15% annual growth) To homogenize the load projections with and without data centers we had to get them to a common scale. The first step was to take the EPRI annual state-level data center energy consumption values and convert them to 8760-hr loads for each year. We did that by assuming a flat (e.g., not weather- or time-sensitive) load profile and distributing the data center loads in each state evenly across all hours in a year. From there the loads were downscaled from the state-level to the county-level using 2019 county-level populations as weights. Finally, the county-level hourly data center loads were summed to the BA-level using the county-to-BA mapping underpinning the root load projections. The net result is 16 (4 weather and socioeconomic scenarios crossed with 4 data center load growth scenarios) unique load projections for the period 2022-2040. The file format follows that of the root dataset with a single additional column "Scaled_TELL_BA_Load_with_DC_MWh" that contains the hourly loads with the added data center loads for a given BA-year-scenario combination. Please refer to the readme file in the root dataset for more information on the file format.

Burleyson, Casey [Pacific Northwest National Labor↗
DOE OSTI2026

California Price Response Potential Study

California's energy landscape is undergoing a significant transformation, driven by the increasing integration of renewable energy sources, the increased adoption of distributed energy resources, the electrification of end-use loads, and the growing need for grid efficiency. To address these challenges, recent revisions to the State’s Load Management Standards (LMS) require all of California’s large utilities and community choice aggregators (CCAs) to offer dynamic electricity pricing options to customers by 2027. Dynamic pricing, which involves varying electricity rates based on real-time supply and demand conditions, offers a promising solution for optimizing grid operations, reducing costs, and incentivizing efficient use of grid capacity. Effective implementation of dynamic pricing requires understanding the potential impacts on customer bills, system load, and the cost-effectiveness of automation technologies. This study aims to evaluate the load response of various end-use devices to hourly dynamic prices. The end-uses studied here are space cooling, space heating, water heating, crop irrigation, pool and spa pumps, and electric vehicle (EV) charging, all for both residential and commercial applications, except for crop irrigation. In 2030, these end uses are forecasted to account for 18% of annual electricity demand in the state, but 40% of demand in the peak net load hour. By modeling possible price-responsive load dispatch algorithms and assessing the resulting impacts on both individual bills and the overall grid, we seek to inform policymakers and utilities about the potential benefits and challenges associated with dynamic pricing, and considerations for the design of dynamic pricing tariffs. Additionally, we will explore the cost effectiveness of adopting automation technologies to enable devices to respond more effectively to real-time price signals. This study considers a range of price profiles, accounting for differences across utilities and customer classes, and presents scenarios for dynamic price design via variation in the percentage of total customer electric costs that are allocated dynamically (versus constituting a fixed portion of the hourly volumetric price). We present results focused primarily on 2030, forecasting electricity prices under both low and high-cost scenarios, to inform longer-term tariff design considerations. We design tariffs by starting with 2019 prices that were calculated according to CalFUSE guidance (CPUC, 2022) and that have been used in recent studies; these prices are all-in volumetric rates that vary by utility and are revenue-neutral to each customer class. They are developed by considering six electricity cost components that are allocated hourly based on system load indicators (gross and net load, and wholesale prices). These prices are forecasted to 2030 for low and high cost scenarios, considering recent trends in total electricity costs with and without years of substantial wildfire mitigation investments. These tariffs, which allocate all costs on an hourly basis, are considered our “Full” dynamic tariff design scenario, while two additional scenarios explore allocating a portion of costs as a flat volumetric charge: the “Medium” scenario allocates 50% of revenue dynamically (and keeps 50% flat), while the “Mild” scenario allocates 20% of revenue dynamically. The 20% dynamic allocation on the Mild scenario aims to represent a case where only the marginal operating costs of the grid are included in the dynamic price.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Modulating thermal load through lightweight residential building walls using thermal energy storage and controlled precooling strategy

Precooling is a recognized technique for reducing cooling energy in buildings during peak hours by shifting load to off-peak hours. This technique is particularly effective in buildings with high thermal mass, because of their large thermal energy storage capacity, and in commercial buildings due to their variable electricity pricing based on time-of-use rates. Precooling in residential buildings has been a matter of limited interest in the past because of their low thermal mass and typically uniform electricity pricing rate. While previous studies on precooling primarily focused on cost savings, an important aspect of precooling is the thermal load modulation, which could be very effective in managing peak demand in lightweight residential buildings integrated with thermal energy storage systems. In this study, we examine different precooling strategies to manage the heat gains in lightweight building walls integrated with phase-change materials. We create nine different precooling profiles by controlling the interior temperature, and then evaluate the influence of the precooling profiles on four key building energy performance parameters: total heat gain, peak heat gain, maximum heat gain during peak hours, and time at which peak occurs. To thoroughly understand the fundamental physics, we first consider hypothetical climates and obtain the optimal precooling strategy required to achieve maximum peak shedding and shifting while minimizing the total heat gains. We then extend the model to Baltimore, Maryland, and estimate the benefits of the optimized precooling strategy under real conditions. The optimal precooling strategy proposed in this study can shift the peak heat gain by up to 14 hours, thereby reducing the heat gain during peak period by up to 95%, at the expense of a 23% increase in the total heat gains.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Spontaneous crack healing in calcite reveals the influence of dynamic strain evolution and surface chemistry

The mechanics of fracture healing in calcite remain poorly constrained yet are fundamental to managing fluid transport in geothermal reservoirs and hydrocarbon systems. Here, we apply microfocused synchrotron Laue X-ray diffraction and infrared spectroscopy to investigate subcritical crack healing in a 1 mm-thick calcite crystal subjected to controlled loading in a double-torsion device. Over a 44-hour period following load removal, we map the evolution of residual strain fields surrounding the crack tip and observe a progressive increase in compressive strain perpendicular to the crack plane accompanied by infrared spectroscopic signatures that reveal enhanced accumulation of water at the healed interface. The correlation between strain evolution and surface chemistry suggests that spontaneous crack healing in calcite is driven by dynamic anelastic relaxation coupled with irreversible fluid-mineral interactions. These findings offer insight into time-dependent crack closure processes in carbonates and highlight the role of chemically-mediated plasticity in subsurface fracture evolution.

materials science↗
NASA NTRS2009

Assessment of Composite Delamination Self-Healing Under Cyclic Loading

Recently, the promise of self-healing materials for enhanced autonomous durability has been introduced using a micro-encapsulation technique where a polymer based healing agent is encapsulated in thin walled spheres and embedded into a base polymer along with a catalyst phase. For this study, composite skin-stiffener flange debonding specimens were manufactured from composite prepreg containing interleaf layers with a polymer based healing agent encapsulated in thin-walled spheres. Constant amplitude fatigue tests in three-point bending showed the effect of self-healing on the fatigue response of the skin-stiffener flange coupons. After the cycling that created debonding, fatigue tests were held at the mean load for 24 hours. For roughly half the specimens tested, when the cyclic loading was resumed a decrease in compliance (increase in stiffness) was observed, indicating that some healing had occurred. However, with continued cycling, the specimen compliance eventually increased to the original level before the hold, indicating that the damage had returned to its original state. As was noted in a prevoius study conducted with specimens tested under monotonically increasing loads to failure, healing achieved via the micro-encapsulation technique may be limited to the volume of healing agent available relative to the crack volume.

O'Brien, T. Kevin↗
DOE OSTI2020

Considerations for Medium-Term Load Forecasting in Morocco

There are many factors that determine how demand for electricity may change over time. Medium-term load forecasting is a subset of load forecasting that focuses on the next year. This presentation summarizes analysis performed by NREL on medium-term load forecasting performed for the Moroccan energy system. This analysis includes hourly regressions and load clustering. This work also describes potential next steps that can be implemented by ONEE to improve this medium-term load forecasting.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1972

Fatigue experience from tests carried out with forged beam and frame structures in the development of the Saab aircraft Viggen

A part of the lower side of the main wing at the joint of the main spar with the fuselage frame was investigated. This wing beam area was simulated by a test specimen consisting of a spar boom of AZ 74 forging (7075 aluminum alloy modified with 0.3 percent Ag) and a portion of a honeycomb sandwich panel attached to the boom flange with steel bolts. The cross section was reduced to half scale. However, the flange thickness, the panel height, and the bolt size were full scale. Further, left and right portions of the fuselage frame intended to carry over the bending moment of the main wing were tested. Each of these frame halves consisted of a forward and a rear forging (7079 aluminum alloy, overaged) connected by an outer and inner skin (Alclad 7075) creating a box beam. These test specimens were full scale and were constructed principally of ordinary aircraft components. The test load spectrum was common to both types of specimens with regard to percentage levels. It consisted of maneuver and gust loads, touchdown loads, and loads due to ground roughness. A load history of 200 hours of flight with 15,000 load cycles was punched on a tape. The loads were randomized in groups according to the flight-by-flight principle. The highest positive load level was 90 percent of limit load and the largest negative load was -27 percent. A total of 20 load levels were used. Both types of specimens were provided with strain gages and had a nominal stress of about 300 MN/sq m in some local areas. As a result of the tests, steps were taken to reduce the risk of fatigue damage in aircraft. Thus stress levels were lowered, radii were increased, and demands on surface finish were sharpened.

Larsson, S. E.↗
DOE OSTI2022

Feasibility Study of Real-Time Carbon Emission Responsive Electric Vehicle Charging Control in Buildings: Preprint

With the progressing electrification of the transportation sector, the source of carbon emissions is gradually shifting from fossil fuel to grid electricity because of electric vehicles (EVs). The carbon intensity of the grid can fluctuate significantly within hours due to the time-varying power generation mix. Therefore, shifting EV charging loads to cleaner hours in response to the carbon intensity signals can reduce carbon emissions. Existing EV charging control methods typically consider the electricity price or the available generation by distributed energy resources (e.g., photovoltaics) to inform decision-making. Such methods tend to reduce energy costs but may neglect the environmental impact of EV charging activities. We propose and compare four carbon emission responsive EV charging controllers with various control rules. The proposed controllers are evaluated based on simulation experiments using metrics such as carbon emission reduction potential, state of charge (SOC) at departure, and peak demand. We found that the need of EV owners to have full batteries at departure could lead to an emission increase when the curtailed EV charge was compensated before departure. Further, up to 12.7% of carbon emission reduction can be achieved if the EV owners reduce the target SOC at departure by less than 15%.

carbon emission↗
DOE OSTI2021

Heavy-Duty Electric Fleet Depot Charging Load Profiles & Substation Load Integration Assessment Results

This data set includes the 24-hour fleet depot charging load profiles (15-min. average demand) and substation load integration assessment results produced for the study, "Heavy-Duty Truck Electrification and the Impacts of Depot Charging on Electricity Distribution Systems", published in 2021 (https://doi.org/10.1038/s41560-021-00855-0). The code developed to generate these load profiles is publicly available at https://github.com/NREL/hdev-depot-charging-2021. Please cite as: Borlaug, B., Muratori, M., Gilleran, M., Woody, D., Muston, W., Canada, T., Ingram, A., Gresham, H., and McQueen, C., (2021). "Heavy-Duty Truck Electrification and the Impacts of Depot Charging on Electricity Distribution Systems". https://doi.org/10.1038/s41560-021-00855-0.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Open Building Operating System: An Open-Source Grid Responsive Control Platform for Buildings

Grid-interactive efficient buildings (GEBs) with flexible loads are a promising method to decarbonize buildings, shift loads during peak hours, and lower energy use and electricity costs. Despite the promising benefits of GEBs, automation systems that manage flexible loads in response to energy prices or other grid signals are still uncommon in small and medium commercial buildings. Recent literature demonstrates such control solutions, but they often rely on custom integrations lacking the tools and drivers needed for scalability. To address these gaps, our team has created a fully open-source software stack capable of integrating heterogeneous flexible building loads and implementing integrated portable control applications called the Open Building Operating System (OpenBOS). The software can be deployed over existing control architecture with a small capital cost. OpenBOS leverages semantic models, which have been the subject of recent investigations to facilitate application portability. The use of semantic data reduces the labor and expense required to deploy and update smart control applications, increasing scalability. In this paper, the semantic modeling schema "Brick" was used, but the proposed approach can also be applied to ASHRAE standard 223P, when released. This paper describes the methodology and software components of OpenBOS and demonstrates its functionality with a rule-based demand flexibility control application configured using a semantic model. This application was tested at a real building in NY that uses a dual-fuel heating system made up of five ductless heat pump mini-splits and a central furnace serving a single zone. The demonstration reduced electricity costs at the site by 27%, demand during a shed event by 49%, and furnace usage by 35%.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Predicting peak day and peak hour of electricity demand with ensemble machine learning

Battery energy storage systems can be used for peak demand reduction in power systems, leading to significant economic benefits. Two practical challenges are 1) accurately determining the peak load days and hours and 2) quantifying and reducing uncertainties associated with the forecast in probabilistic risk measures for dispatch decision-making. In this study, we develop a supervised machine learning approach to generate 1) the probability of the next operation day containing the peak hour of the month and 2) the probability of an hour to be the peak hour of the day. Guidance is provided on preparation and augmentation of data as well as selection of machine learning models and decision-making thresholds. The proposed approach is applied to the Duke Energy Progress system and successfully captures 69 peak days out of 72 testing months with a 3% exceedance probability threshold. On 90% of the peak days, the actual peak hour is among the 2 h with the highest probabilities.

25 ENERGY STORAGE↗