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18 records

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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 OSTI2024

Bilevel Nodal Behind-the-meter Solar Disaggregation Under Unexpected Extreme Weather Conditions

As the power grid undergoes significant paradigm shift due to the increasing penetration of renewable generation, the ever-growing installation of behind-the-meter (BTM) solar generation in the power grid also has a significant impact on nodal loads, posing challenges on transmission operators. Furthermore, increasing frequent and severe extreme weather events intertwine with ubiquitous BTM solar generations and have amplified the challenges of accurately model nodal load profiles, especially under the lack of ground-truth information for verification. To tackle these challenges, this paper introduces a bilevel model that utilizes year-long data (e.g., proxy solar, zonal load, and individual node load profiles) to disaggregate metered profiles into actual demand and BTM solar generation at each transmission node. The proxy solar not only scales the BTM solar generation of individual nodes but also create a compensation term for enhancing performance on days with unexpected extreme weather events. The proposed algorithm is validated with real-world PJM Interconnection data during unexpected events like the recent Winter Storm Elliott. For quantitative evaluations, a novel Score error is introduced, which is based on mean percentages and load scales and offers a universal assessment method suitable for all nodes and different data formats (e.g., normalized or raw values).

behind-the-meter solar, load disaggregations, load↗

DOE OSTI2023

Semi-Supervised, Non-Intrusive Disaggregation of Nodal Load Profiles With Significant Behind-the-Meter Solar Generation

It is of imperative interests for regional transmission organizations (RTOs) to effectively extract actual load profiles at transmission nodes with significant behind-the-meter solar generation, which remains a gap in the existing technology paradigm. This paper proposes an explicit yet efficient linear estimator to disaggregate actual load profiles at transmission buses with significant behind-the-meter (BTM) solar generations. The proposed estimator is based on disaggregating (i.e., extracting) at locations close to transmission buses under consideration. Further, to overcome the lack of “ground truth” and validate the performance of the proposed algorithms, we first propose semi-supervised mechanisms with parameter tuning as well as unsupervised clustering and leverage the unique characteristics of zero-crossing points in BTM solar peaking behaviors, which we refer to as “Zone-to-Node (Z2N)” methods. Next, we further propose a bi-level Node-to-Node (N2N) framework that improves the overall disaggregation performances compared to Z2N. Numerical results are presented using real-world data at PJM Interconnection.

14 SOLAR ENERGY↗

DOE OSTI2024

A Contextually Supervised Optimization-Based HVAC Load Disaggregation Methodology

This paper presents a novel contextually supervised optimization-based approach for disaggregating heating, ventilation, and air-conditioning (HVAC) loads using smart meter or Supervisory Control and Data Acquisition data. To disaggregate the load into HVAC loads, large and infrequently used loads (LIUL), and base loads, we formulate an optimization problem to minimize a set of five loss terms, consisting of the reconstruction errors of the overall load profile, the ramp rate losses, and three distinct loss functions linked with the HVAC load, base load, and LIUL, respectively. To enhance accuracy, we incorporate two forms of contextual information into the problem formulation. First, we utilize mutual information to estimate HVAC energy consumption. Second, we employ a base load dictionary to constrain HVAC load estimation errors. The obtained HVAC load profiles are fine-tuned by abnormal ramp detection followed by binary hypothesis testing. Here, the proposed method is developed and tested using sub-metered residential and commercial building data. Simulation results show that the proposed method outperforms existing methods across various data resolutions and load aggregation levels, showing excellent transferability and generalizability.

24 POWER TRANSMISSION AND DISTRIBUTION↗

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 OSTI2025

West Coast Offshore Wind Transmission Study

As a primary federal investigation of the utility of transmission to support offshore wind emergence in the Western Interconnect, the West Coast Offshore Wind Transmission study evaluates five distinct West Coast-wide transmission scenarios and trades the economic benefits and costs of six development pathways from 2035 to 2050. In this time, 33 GW of OSW generation and supporting transmission are explored within the context of 350 additional GW of generation to support 100% clean electricity. Geospatial analysis informed plausible transmission topologies and cost estimates. All pathways to intraregional or interregional coordination transmission builds showed more than $\$$14 billion in present value cost savings, with a maximum of $\$$25 billion of savings in the interregional case. These savings are primarily driven by fuel costs and increased availability of power flows enabled by new transmission networks. Key considerations of clean, dispatchable generation options, geographic disaggregation of costs, system reliability reinforcements, resilience, and community values are also presented and discussed.

17 WIND ENERGY↗

NASA NTRS2018

The Use of Gridded Fossil Fuel CO2 Emissions (FFCO2) Inventory for Climate Mitigation Applications: Errors, Uncertainties, and Current and Future Challenges

Emission Inventory (EI) is a fundamental tool to monitor global compliance of greenhouse gases (GHGs) emissions reduction actions. Inventory guidelines provide a best practice to help EI compilers to make comparable national emission estimates, in spite of the differences in data availability across countries and regions. There are a variety of sources of errors and uncertainties, however, that originate beyond what the inventory guidelines define. For example, spatially-explicit EIs, which are a key product for atmospheric modeling applications, are often developed for research purposes, and there are no specific guidelines to disaggregate emission estimates from country scale. On top of that, EIs are fundamentally prone to systematic biases due to the simple calculation methodology and thus an objective evaluation (e.g. atmospheric top-down estimates) is needed to assure the accuracy of the estimates. ODIAC is a global high-resolution (1x1 km) fossil fuel carbon dioxide (CO2) gridded EI that is now often used in atmospheric CO2 modeling. ODIAC is based on disaggregation of national emission estimates made by CDIAC, which is the well accepted standard in the community. The ODIAC emission data product is updated on an annual basis using best available statistical data. Subnational spatial emission patterns are estimated using power plant profiles and satellite-observations of nighttime lights. In addition to the conventional CDIAC gridded data product, ODIAC carries international bunker emissions (shipping and aviation), which allows flux inversion modelers to accurately impose the global total fossil fuel emissions and their horizontal and vertical distribution. We have extensively evaluated ODIAC emissions using fine-grained EIs as well as a high-resolution atmospheric model simulation across different scales (national, subnational/regional, and urban policy relevant) with a focus on the uncertainties associated with the emission disaggregation. We have examined the use of NASA's Black Marble Suomi-NPP/VIIRS nightlight data.

Oda, Tomohiro↗

NASA NTRSDate not supplied

NASA’s Goddard Space Flight Center’s Distributed Systems Missions Architecture

Space and Earth Science are being transformed by applying a distributed approach to missions, where the fusion of data from components, systems, instruments, models, and observation locations works in concert with timely responses and feedback mechanisms to multiply the knowledge obtained. Additionally, a disaggregated approach allows for a distributed cost and schedule that can be shared across multiple organizations to enable the greater mission. With the advances in reduced size, weight, and power for space-worthy components leading to the revolution in smaller spacecraft, the cost and timeliness proposition for launching multiple space assets has also greatly improved. Thus, the aerospace industry is undergoing a paradigm shift toward a proliferation of small satellites as a networked approach to meet mission objectives. This paper will describe the impetus, goal, and path to provide an openly available framework as a unifying catalyst for broad-ranging Distributed Systems Missions (DSMs) contributors.

Distributed Systems↗

NASA NTRSDate not supplied

NASA’s Goddard Space Flight Center’s Distributed Systems Missions Architecture

Distributed Systems↗

DOE OSTI2024

Geospatial characterization of low-temperature heating and cooling demand in residential, commercial, manufacturing, agricultural, and data center sectors for potential geothermal applications in the United States

Thermal demand for heating and cooling has been predominantly supplied by fossil fuel combustion in the United States, although low-carbon alternatives are extensively available including geothermal, solar thermal, and waste heat. Here, this study analyzed end-use energy consumption, fuel expenditure, and data center commissioned power data to geospatially characterize the U.S. low-temperature heating and cooling demand at the county level in residential, commercial, manufacturing, agricultural, and data center sectors and understand potential opportunities for geothermal applications. In the analysis, the regional-scale energy consumption data was incorporated with system efficiencies to address actual demand and was then disaggregated with weighting factors to the county level. The results indicated that total low-temperature heating and cooling demand is 16.7 EJ, combining heating demand of 10.8 EJ and cooling demand of 5.9 EJ. Overall, 59.9 % (10 EJ) of the low-temperature heating and cooling demand occurred in the residential sector. The heating and cooling demand visualized in maps represented that the geospatial distribution of heating and cooling demand in the residential and commercial sectors is governed by the number of housing units and climate zone designations, while heating and cooling demand in the manufacturing, agricultural, and data center sectors is dependent on the number and location of facilities. The results also demonstrated that geothermal heat pumps are broadly used in the residential and commercial sectors for heating and cooling in the U.S. Midwest, South, and Northeast regions but are limited in the West, implying great decarbonization potential in the future.

15 GEOTHERMAL ENERGY↗

DOE OSTI2024

Nonexhaustive Taxonomy of Hydropower and Pumped Storage Hydro Facilities [Slides]

The National Renewable Energy Laboratory (NREL) has published benchmark technical reports and presentations that disaggregate major product components for a variety of technologies, including land-based and offshore wind, photovoltaic, and battery energy storage systems. NREL has partnered with staff from the Pacific Northwest National Laboratory (PNNL) and members of the U.S. Department of Energy's Water Power Technology Office (WPTO) to create a non-exhaustive taxonomy of the major products and components of hydropower and pumped storage systems.

13 HYDRO ENERGY↗

NASA NTRS2015

Decomposition of Sources of Errors in Seasonal Streamflow Forecasting over the U.S. Sunbelt

Seasonal streamflow forecasts, contingent on climate information, can be utilized to ensure water supply for multiple uses including municipal demands, hydroelectric power generation, and for planning agricultural operations. However, uncertainties in the streamflow forecasts pose significant challenges in their utilization in real-time operations. In this study, we systematically decompose various sources of errors in developing seasonal streamflow forecasts from two Land Surface Models (LSMs) (Noah3.2 and CLM2), which are forced with downscaled and disaggregated climate forecasts. In particular, the study quantifies the relative contributions of the sources of errors from LSMs, climate forecasts, and downscaling/disaggregation techniques in developing seasonal streamflow forecast. For this purpose, three month ahead seasonal precipitation forecasts from the ECHAM4.5 general circulation model (GCM) were statistically downscaled from 2.8deg to 1/8deg spatial resolution using principal component regression (PCR) and then temporally disaggregated from monthly to daily time step using kernel-nearest neighbor (K-NN) approach. For other climatic forcings, excluding precipitation, we considered the North American Land Data Assimilation System version 2 (NLDAS-2) hourly climatology over the years 1979 to 2010. Then the selected LSMs were forced with precipitation forecasts and NLDAS-2 hourly climatology to develop retrospective seasonal streamflow forecasts over a period of 20 years (1991-2010). Finally, the performance of LSMs in forecasting streamflow under different schemes was analyzed to quantify the relative contribution of various sources of errors in developing seasonal streamflow forecast. Our results indicate that the most dominant source of errors during winter and fall seasons is the errors due to ECHAM4.5 precipitation forecasts, while temporal disaggregation scheme contributes to maximum errors during summer season.

Decomposition↗

DOE OSTI2026

Enter the AHU (36th Chamber of ASHRAE): A Multi-site Field Study of ASHRAE G36

Despite being recognized as the best practice for advanced building controls, ASHRAE Guideline 36 (G36) has seen slow adoption in retrofit cases. Decisionmakers lack credible field evidence to justify the time and person-power investment. Most prior analyses have relied on software simulations, which overlook implementation challenges and fail to persuade owners to move from models to real-world deployment. This paper presents a multi-site field study of G36 performance, drawing on measured results from 17 projects across diverse building types and climate zones. The analysis disaggregates outcomes by the most widely adopted air handling unit (AHU) based G36 strategies, including trim-and-respond approaches to supply air temperature (SAT) and duct static pressure (DSP) reset, and economizer controls. Results for controls re programming implementations are encouraging, with HVAC savings ranging from 2% - 49%, with a median of 18%. The range aligns with simulation study findings showing 1% - 46% savings through these three strategies. These findings show that even without capital investment in control infrastructure upgrades, existing building owners are reaping significant benefits from updating HVAC sequences of operation to industry best-practice solutions. The results give practitioners and decisionmakers a reference point on what to expect, which strategies deliver, and how field performance may compare to simulation.

Deshpande, Reva↗

DOE OSTI2024

Simulation-Based Analysis of Feeder Operation with Different PV Smart Inverter Functions on an Actual Distribution System: Preprint

High penetration of photovoltaics (PV) in distribution feeders can cause problems, such as overvoltage, reverse power flow, and large net load changes. Traditional voltage regulation devices, such as capacitors and voltage regulators, can solve some of these problems but might have some delays. Today, smart inverters are gradually being used to provide voltage regulation and frequency support in distribution systems. Different smart inverter settings have been recommended in various rules and standards; however, the potential benefits and their impacts on distribution system operation are not well compared and studied. This paper presents a comparison of different smart inverter settings as applied to a distribution system. An actual feeder model from San Diego Gas & Electric Company is used to conduct the simulation. Additionally, a load disaggregation method is proposed to disaggregate the load and PV profile for each load location using advanced metering infrastructure net load measurements. Then, different smart inverter settings are applied to the PV systems in the feeder, and the simulation results are compared. The results show that the implementation of specific functions of smart inverters can reduce voltage exceedances, and the utility can determine the specific inverter setting based on its operational requirements.

distribution system↗

DOE OSTI2024

Evolving Competitive Markets in SAPP: Leveraging Competitive Wholesale Electricity Markets to Drive Renewable Generation Capacity in the Southern African Power Pool (SAPP)

The SADC region has significant natural resource potential to increase renewable energy generation, improve electricity reliability, and support economic development. This research finds an apparent lack of confidence from electricity infrastructure investors in SAPP wholesale electricity markets, which increases risk perception and lowers the likelihood of capital deployment. With respect to free market fundamentals, competitive market obstacles and renewable energy development obstacles are characterized. Stakeholders identified the top obstacles to well-functioning competitive markets as insufficient transmission infrastructure for interconnection and regional movement of electricity, dominance of national single-buyer markets, and lack of or weak nation-state regulatory frameworks. Stakeholders prioritized the top three obstacles for renewable energy development as a lack of viable commercial arrangements for variable renewable energy (VRE) balancing, lack of functional and consistent nation-level regulations, and higher project costs related to reliance on imported equipment. With respect to potential solution options, stakeholders prioritized the development of new cost allocation and finance methods to facilitate new transmission expansion, training to educate new or potential new market entrants on SAPP processes, as well as modeling and analysis of regional SAPP participation benefits disaggregated to the nation-state level. From these perspectives, this research identified strategy options for consideration including transitioning SAPP to a regional transmission operator (RTO) for operation and planning of cross-border transmission facilities and market administration, shifting operations of SAPP member transmission systems to Independent System Operators (ISOs), establishing a regional regulatory authority and enhancing market data transparency. Implementing these reforms is expected to be challenging, but not insurmountable, given the domestic political, legal, and jurisdictional complexities of the SADC region.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Oak Ridge National Laboratory EAGLE-I TM : Modeling Electric Utility County Customers for Situational Awareness

During natural hazard events (hurricanes, wildfires, earthquakes, etc.) and recent man-made events (e.g., cyber attacks), the exchange of near real-time, spatially refined data within the response community is critical. The EAGLE-I$^{TM}$ platform is one tool that facilitates this data for decision makers within the energy sector. While much information can be collected and integrated into the system directly, other pertinent data must be augmented by other derived data products to enhance the information and allow for a consistent evaluation of on-the-ground conditions. One such data set that requires the addition of other derived data is the electric utility customer outage data that is aggregated to the county level within the EAGLE-I application. Without a county customer data set, outages can only be compared on total counts, which gives greater importance to higher population outages. Including an electric utility customer data set at the county level allows for these outage counts to be converted to percent outages and brings a consistent classification of outages and equal importance to all outages. To achieve this, several available data sets were combined and spatial disaggregation techniques were employed to model customer estimates at the county scale. This paper presents the approach to produce this data for the United States and lessons learned from working with these disparate data sets. Data validation is provided, where possible, and limitations of the model and possible improvements are discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2011

Daily and 3-hourly Variability in Global Fire Emissions and Consequences for Atmospheric Model Predictions of Carbon Monoxide

Attribution of the causes of atmospheric trace gas and aerosol variability often requires the use of high resolution time series of anthropogenic and natural emissions inventories. Here we developed an approach for representing synoptic- and diurnal-scale temporal variability in fire emissions for the Global Fire Emissions Database version 3 (GFED3). We disaggregated monthly GFED3 emissions during 2003.2009 to a daily time step using Moderate Resolution Imaging Spectroradiometer (MODIS) ]derived measurements of active fires from Terra and Aqua satellites. In parallel, mean diurnal cycles were constructed from Geostationary Operational Environmental Satellite (GOES) Wildfire Automated Biomass Burning Algorithm (WF_ABBA) active fire observations. Daily variability in fires varied considerably across different biomes, with short but intense periods of daily emissions in boreal ecosystems and lower intensity (but more continuous) periods of burning in savannas. These patterns were consistent with earlier field and modeling work characterizing fire behavior dynamics in different ecosystems. On diurnal timescales, our analysis of the GOES WF_ABBA active fires indicated that fires in savannas, grasslands, and croplands occurred earlier in the day as compared to fires in nearby forests. Comparison with Total Carbon Column Observing Network (TCCON) and Measurements of Pollution in the Troposphere (MOPITT) column CO observations provided evidence that including daily variability in emissions moderately improved atmospheric model simulations, particularly during the fire season and near regions with high levels of biomass burning. The high temporal resolution estimates of fire emissions developed here may ultimately reduce uncertainties related to fire contributions to atmospheric trace gases and aerosols. Important future directions include reconciling top ]down and bottom up estimates of fire radiative power and integrating burned area and active fire time series from multiple satellite sensors to improve daily emissions estimates.

Mu, M.↗