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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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50 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Probabilistic analysis of masked loads with aggregated photovoltaic production

In this paper we present a probabilistic analysis framework to estimate behind-the-meter photovoltaic generation in real time. We develop a forward model consisting of a spatiotemporal stochastic process that represents the photovoltaic generation and a stochastic differential equation with jumps that represents the demand. Here, we employ this model to disaggregate the behind-the-meter photovoltaic generation using net load and irradiance measurements.

14 SOLAR ENERGY↗
DOE OSTI2020

Real-time disaggregation of renewable energy generation on an electricity distribution system

Techniques are described for disaggregation of renewable energy generation on an electricity distribution system. Aggregate power measurements are identified a distribution substation. Active power load of the distribution substation and active power generated by renewable energy sites can be disaggregated from the aggregate power measurements.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Enabling DER Participation in Frequency Regulation Markets

Distributed energy resources (DERs) are playing an increasing role in ancillary services for the bulk grid, particularly in frequency regulation. In this article, we propose a framework for collections of DERs, combined to form microgrids and controlled by aggregators, to participate in frequency regulation markets. Our approach covers both the identification of bids for the market clearing stage and the mechanisms for the real-time allocation of the regulation signal. The proposed framework is hierarchical, consisting of a top layer and a bottom layer. The top layer consists of the aggregators communicating in a distributed fashion to optimally disaggregate the regulation signal requested by the system operator. The bottom layer consists of the DERs inside each microgrid whose power levels are adjusted so that the tie line power matches the output of the corresponding aggregator in the top layer. The coordination at the top layer requires the knowledge of cost functions, ramp rates, and capacity bounds of the aggregators. We develop meaningful abstractions for these quantities respecting the power flow constraints and taking into account the load uncertainties and propose a provably correct distributed algorithm for optimal disaggregation of regulation signals among the microgrids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Highly Resolved Projections of Passenger Electric Vehicle Charging Loads for the Contiguous United States: Results From and Methods Behind Bottom-Up Simulations of County-Specific Household Electric Vehicle Charging Load (Hourly 8760) Profiles Projected Through 2050 for Differentiated Household and Vehicle Types

This report documents enhancements made to the TEMPO (Transportation Energy & Mobility Pathway Options TM ) model to project spatially, demographically, and temporally resolved national-scale EV charging load profiles and describes three scenarios and corresponding datasets created for the NREL demand-side grid (dsgrid) project in support of bulk power systems modeling. In brief, TEMPO was enhanced to disaggregate national and annual energy demand projections into household and county-level projections of passenger electric vehicle (EV) hourly charging load profiles (8760 profiles), accounting for consumer, travel, and temperature variations that impact EV energy demand. In alignment with NREL's forward-looking grid modeling, three scenarios for EV adoption covering 2020-2050 were created-- Annual Energy Outlook (AEO) Reference Case, Electrification Futures Study (EFS) High Electrification, and All EV Sales by 2035 --and associated datasets have been included in the dsgrid platform for public use.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Feeder Power Disaggregation: A Data-Efficient Matrix Completion Approach: Preprint

This paper presents a data-driven algorithm for the feeder power disaggregation problem in distribution systems. Leveraging spatio-temporal power patterns in residential homes, residential power is discomposed into three components: sparse-switching loads, periodic loads, and photovoltaic generation, using two sparse matrices and a rank-one matrix. The matrix completion process is data-efficient because of the matrix sparsity and low rankness, along with the use of power system models. The proposed approach is tested using real-world residential datasets on a 33-bus distribution system, demonstrating accurate power disaggregation with efficient matrix completion.

distribution system↗
DOE OSTI2024

Situational awareness-enhancing community-level load mapping with opportunistic machine learning

Motivated by present and forthcoming challenges in the adoption and integration of distributed renewable energy, we develop a machine learning (ML) approach that builds short-fuse mappings connecting the occasionally-unobservable true load in one target community with information-rich signals collected from relatively more instrumented reference communities. Our setting is inspired by and tailored to target communities with significant unobservable behind-the-meter solar generation, where true load (a relatively well-behaved quantity of interest to grid operators) is hard to discern during daytime due to insufficient instrumentation and/or privacy reasons, but that can be related to reference communities with low unobservable distributed variable generation or with sufficient instrumentation. The developed mapping, herein realized with Support Vector Machine regression, is built using nighttime data from all communities, when their distributed generation is low or zero. Our ML algorithm opportunistically learns to correlate signals of interest and then is operationally used the next day to shed light into target community load evolution. The mapping is subsequently rebuilt, rolling its short-fuse scope perpetually forward in time. Here, we demonstrate the efficacy of our approach on nine synthetically generated topologies and associated timeseries stemming from real-world data, on which we observe cumulative error performance that yields lower than 10% and 15% daily-averaged mean absolute percentage errors in target community load estimation on more than about 75% and 90% of days, respectively, in multiple yearly evaluations that shed light on long-term performance also under seasonal and one-off effects. The proposed ML-powered methodology can offer grid operators much-improved visibility into a previously obscure space and can also serve as an additional source of information in broader, multi-modal solar disaggregation solutions.

14 SOLAR ENERGY↗
DOE OSTI2023

Feeder Power Disaggregation: A Data-Efficient Matrix Completion Approach

This paper presents a data-driven algorithm for the feeder power disaggregation problem in distribution systems. Leveraging spatio-temporal power patterns in residential homes, residential power is discomposed into three components: sparse-switching loads, periodic loads, and photovoltaic (PV) generation, which are characterized through the design of two sparse matrices and a low-rank matrix. The matrix completion process is data-efficient because of the matrix sparsity and low rankness, along with the use of power system models. The proposed approach is tested using real-world residential data set on a 33-bus distribution system, demonstrating accurate power disaggregation with efficient matrix completion.

distribution system↗
DOE OSTI2026

A Scalable, Distribution Network-Aware, Customer Privacy-Preserving Framework for Operation of Virtual Power Plants

This poster presents a hierarchical control framework for a virtual power plant that leverages behind-the-meter resources for grid services while maintaining customer privacy during setpoint disaggregation. Unlike many existing approaches, the virtual power plant model uses a hierarchical control strategy and an iterative approach to determine the optimal set point dis-aggregation without direct load control while maintaining system-level power flow and voltage constraints. The proposed approach is numerically validated on a synthetic distribution feeder in San Francisco, demonstrating the ability of the framework to provide privacy-preserving virtual power plant services.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Fuel/Basket Degradation Modeling Summary

This report documents the activities in a preliminary phase of development for three models: 1) waste package breach model, 2) fuel/basket degradation model, and 3) dual-purpose canister (DPC) crush model. The waste package breach model describes the coupling of mass flow, heat transport, and canister shell deformation in response to a heat-generating (criticality) event. The fuel/basket degradation model describes potential weakening and disaggregation of the DPC structure from corrosion, possibly accelerated by seismic ground motion. Progressively degraded three-dimensional (3D) configurations of the fuel, basket, and shell are generated for future analysis of reactivity (with as-loaded DPC fuel characteristics). Another important application for the fuel/basket degradation model is validation of the two stylized degradation cases currently being used by other investigators for analysis of the as-loaded DPC inventory under disposal conditions. The DPC crush model investigates stability of a typical DPC after breach of the disposal overpack allows fluids from the repository near-field environment to penetrate and externally pressurize the canister shell. Preliminary results show that large deformation of the DPC could occur for external pressure on the order of 10 to 15 MPa, or the shell could be stable (not collapse) with pressure of 20 MPa or greater if the basket plates are fully welded at the connections.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

End-Use Load Profiles for the U.S. Building Stock

The End-Use Load Profiles for the U.S. Building Stock project uses New ResStock and ComStock models to statistically represent the energy use of U.S. buildings. The project's hybrid approach combines best-available ground truth data, such as submetering studies and statistical disaggregation of whole-building interval meter data, with the reach, cost-effectiveness, and granularity of physics-based and data-driven building stock modeling to deliver a nationally-comprehensive data set at a fraction of the historical cost.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Using energy storage systems to extend the life of hydropower plants

Despite their advantages, distributed energy resources (DERs) bring inherent uncertainty and variability into the landscape of modern power systems. As DER penetration grows, conventional generators like hydropower plants have to respond more often to arrest the imbalance in the net load. Hydropower turbines provide their best operational performance with minimal wear and tear when operating at regions of maximum efficiency. However, the current needs for hydropower plants require them to operate under varying load conditions and thus sub-optimal operating points leading to additional stress. To relieve the hydropower plants, this paper proposes a hybridization strategy where a hydropower unit is paired with an energy storage system (ESS) to increase operational flexibility and mitigate damage to the hydro plant. Models are developed to represent the operation of the hybrid system, quantify degradation, and assess economic benefits. Moreover, an innovative controller disaggregates the market dispatch signal into separate control setpoints for the ESS and hydropower unit. In case studies performed on a real-world hydropower facility, it was found that the ESS-based hybridization can extend the life of the hydropower plant by 5% on average. Notably, the economic benefits from reduced maintenance and deferred investment are estimated to be around $3.6 million.

13 HYDRO ENERGY↗
DOE OSTI2022

Deep Electrification Analysis: The Role of the U.S. Power Grid for Sustainable Transportation

This project attempts to quantify the size of electric generation for the entire nation to transition from a fossil fuel based transportation sector to a zero GHG emission-based energy source. The scope of this analysis is limited to decarbonizing the transportation sector, leaving the remaining sectors, such as power (for those that are still fossil based), industry, and building sectors, for later phases of study. The study year for this analysis is 2050, with expected escalation in transportation services and naturally occurring evolutions in the electric power sector and the entire economy. This analysis uses the projections of the Energy Information Administration’s (EIA’s) Annual Energy Outlook (AEO 2020) Reference Case for study year 2050 [EIA/AEO2020] as a base-case. The transportation sector is disaggregated by the following modes and classes: (1) on-road (divided into light-duty, medium-duty, heavy-duty vehicles), (2) aviation, (3) maritime, and (4) rail. The decarbonization case was based on only 2 pathways: (1) electrification of on-road transportation except for 30% of heavy-duty vehicles, and (2) power-to-liquid for the remaining transportation modes. The study estimated for 11 US regions what the additional wind and storage capacities requirements are to replace the fossil-based fuels with renewable wind capacity. Considered were the utilization of the existing idle capacity particularly during the load valley at night and any additional new generation capacity in EIA projections for the reference case. To balance the additional wind capacity required significant energy storage capabilities which were estimated in terms of power capacity (GW) and energy capacity (GWh). The paper further characterizes the energy requirements by a relation of power capacity to duration, allowing the analyst to gain insights into what the best technology portfolio might be to meet the new balancing or flexibility needs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

FTR for: Reducing plug-load electricity footprint of residential buildings through low-cost, non-intrusive sub-metering and personalized feedback technology

The project started in October 2016 and ended in December 2022. The project's principal goals and achievements were: (i) Measure real and reactive electric power consumption in ~400 apartments in multifamily settings of varying size and vintage and publish the data; data was published according to New York State's recommended 15x15 rule continuously from Jan 2019 through December 2022 (10 second time resolution); with the combination of large number of apartments, real and reactive power, as well as high time resolution (10-seconds), the dataset is first of its kind worldwide; because the dataset New York City apartment consumption pre, during, and post pandemic, it further offers unique insights into changes in residential electricity consumption during lockdowns and after modified work from home schedules. (ii) Measure effectiveness of different feedback types to prompt residents to lower their electricity consumption; achieved 11% (kWh-weighted) reduction versus baseline consumption; confirmed previous studies that social comparisons elicit above average responses; showed, for the first time, that the so called boomerang effect in power consumption feedback projects can be explained by a previously hypothesized norm-conforming "magnet effect" (rather than a non-conforming defiance effect), thus substantially advancing the research in the field. (iii) Disaggregate apartment-level consumption to appliance level; because the hardware for electricity consumption unexpectedly allowed only for 10-second time resolution (rather than the 1-second resolution we had planned on), the disaggregation algorithms we developed on sample data could not be applied to the actual field data we collected. The project has yielded high visibility, with a total of 17 publications, from peer reviewed journals, published data sets (free access), blog posts, NY Times, National Public Radio, and CNN.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗