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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Load Shifting with Space Conditioning Heat Pumps During Heating Season in the Pacific Northwest

Ductless mini- and multi-split heat pumps are commonly deployed in Pacific Northwest residences, which historically have inefficient space heating and no space cooling. Heat pumps provide these residences with more energy-efficient space heating in the winter and space cooling for increasingly warm summer periods. For many of these applications, heat pumps add a new electrical load at the residence, compelling regional utilities to seek to understand their impacts on the electrical grid and opportunities for load management. In coordination with regional utilities, this study examined the winter load-shifting potential of residential multi-split heat pumps with variable capacity technology. The study included nine residential sites with a total of 20 ductless indoor units, which underwent simulated smart grid control. Grid control functionality was provided through the CTA-2045 communication protocol and included offsetting temperature setpoints for individual indoor units in the study. Field data collection included whole-house and heating, ventilation, and air conditioning electrical power consumption; indoor zone temperatures; occupant surveys; and CTA-2045 curtailment data for both baseline and simulated load-shifting events by site. This paper provides an overview of the load-shifting strategies and aggregated results from winter data collection.

ASHRAE, Heat pump, energy effciency, residential b↗
DOE OSTI2023

Engineering of Self‐Aggregation‐Resistant MnO 2 Heterostructure with A Built‐in Field for Enhanced High‐Mass‐Loading Energy Storage

Abstract Although MnO 2 has been intensively investigated for energy storage, further applications are limited by van der Waals force‐triggered self‐aggregation that always leads to poorly exposed active sites and compromised reaction dynamics, especially under high‐mass‐loading conditions. Herein, by synergistically coupling interfacial modulation with the Kirkendall effect, this work achieves in situ topological structure reorganization of MnOOH toward the high‐aspect‐ratio MnO 2 heterostructure (Heter‐MnO 2 ) with fully exposed active sites, which is ready to assemble into self‐supporting high‐mass‐loading film (30 mg cm −2 ) with restrained self‐aggregation. Theoretical calculation and dynamics analysis results demonstrate the generation of the built‐in field within the heterostructure, thus enhancing the electronic‐transfer and ionic‐adsorption/transport rates. As such, the 30 mg cm −2 Heter‐MnO 2 electrode achieves a superior areal capacitance of 4762 mF cm −2 at 1 mA cm −2 and a sound rate performance (79% at 100 mA cm −2 ) comparable to those of low‐mass‐loading/thin‐film electrodes. As a proof of concept, the fabricated planar interdigital quasi‐solid‐state symmetric micro‐supercapacitor (MSC) based on the Heter‐MnO 2 electrode can deliver a remarkable areal capacitance of 181 mF cm −2 and a considerable volumetric energy density of 10.3 mWh cm −3 . This methodology highlights the promise of surface/interface chemistry modulation for the configuration of easy‐to‐integrate hierarchical nanostructures to better meet practical energy applications.

Chemistry↗
DOE OSTI2020

Synthetic residential load models for smart city energy management simulations

The ability to control tens of thousands of residential electricity customers in a coordinated manner has the potential to enact system-wide electric load changes, such as reduce congestion and peak demand, among other benefits. To quantify the potential benefits of demand-side management and other power system simulation studies (e.g. home energy management, large-scale residential demand response), synthetic load datasets that accurately characterize the system load are required. This study designs a combined top-down and bottom-up approach for modelling individual residential customers and their individual electric assets, each possessing their own characteristics, using time-varying queueing models. The aggregation of all customer loads created by the queueing models represents a known city-sized load curve to be used in simulation studies. The three presented residential queueing load models use only publicly available data. An open-source Python tool to allow researchers to generate residential load data for their studies is also provided. The simulation results presented consider the ComEd region (utility company from Chicago, IL) and demonstrate the characteristics of the three proposed residential queueing load models, the impact of the choice of model parameters, and scalability performance of the Python tool.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

A Data-Driven Pivot-Point-Based Time-Series Feeder Load Disaggregation Method

The load profile at a feeder-head is usually known to utility engineers while the nodal load profiles are not. However, the nodal load profiles are increasingly important for conducting time-series analysis in distribution systems. Therefore, in this paper, we present a pivot-point based, two-stage feeder load disaggregation algorithm using smart meter data. The two stages are load profile selection (LPS) and load profile allocation (LPA). In the LPS stage, a random load profile selection process is first executed to meet the load diversity requirement. Then, a few pairs of pivot points are selected as the matching targets. After that, a matching algorithm will run repetitively to select one load profile at a time for matching the reference load profile at the pivot points. In the LPA stage, the LPS selected load profiles are allocated to each load node on the feeder considering distribution transformer loading limits, load composition, and square-footage. The proposed method is validated using actual data collected in a North Carolina service area. Finally, simulation results show that the proposed method can generate a unique load shape for each load node while match the shape of their aggregated profile with the actual feeder head load profile.

42 ENGINEERING↗
DOE OSTI2021

Sulfur Tolerant Subnanometer Fe/Alumina Catalysts for Propane Dehydrogenation

A series of Al 2 O 3 -supported Fe-containing catalysts were synthesized by incipient wetness impregnation. The iron surface density was varied from 1 to 13 Fe atoms/nm 2 spanning submonolayer to above-monolayer coverage. Here, the resulting supported Fe-catalysts were characterized by N 2 physisorption, ex situ X-ray diffraction (XRD), X-ray pair distribution function (PDF), X-ray absorption spectroscopy (XAS), aberration corrected scanning transmission electron microscopy (AC-STEM) and chemically probed by hydrogen temperature-programmed reduction (H 2 -TPR). The results suggest that over this entire range of loadings, Fe was present as dispersed species, with only a very small fraction of Fe 2 O 3 aggregates, at the highest Fe loading in oxide phase. The in situ sulfidation of Fe/Al 2 O 3 resulted in the formation of a highly active and selective PDH catalyst. The highest activity with 52% propane conversion and ~99% propylene selectivity at 560 °C was obtained for the 6.4 Fe/Al 2 O 3 -S catalyst, suggesting that this is the highest amount of Fe that could be fully dispersed on the support in sulfided form. XRD and AC-STEM indicated the absence of any crystalline iron sulfide aggregates after sulfidation and reaction. H 2 -TPR results indicated that the amount of the reducible Fe sites in the sulfided catalyst remained constant above monolayer coverage, and increasing loading did not increase the number of reducible Fe sites. Consistent with these results, the reactivity per gram of catalyst showed no increase with Fe loading above monolayer coverage, suggesting that additional Fe remains conformal to the alumina surface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Building Load Control Using Distributionally Robust Chance-Constrained Programs with Right-Hand Side Uncertainty and the Risk-Adjustable Variants

Aggregation of heating, ventilation, and air conditioning (HVAC) loads can provide reserves to absorb volatile renewable energy, especially solar photo-voltaic (PV) generation. In this paper, we decide HVAC control schedules under uncertain PV generation, using a distributionally robust chance-constrained (DRCC) building load control model under two typical ambiguity sets: the moment-based and Wasserstein ambiguity sets. We derive mixed integer linear programming (MILP) reformulations for DRCC problems under both sets. Especially, for the Wasserstein ambiguity set, we use the right-hand side (RHS) uncertainty to derive a more compact MILP reformulation than the commonly known MILP reformulations with big-M constants. All the results also apply to general individual chance constraints with RHS uncertainty. Furthermore, we propose an adjustable chance-constrained variant to achieve tradeoff between the operational risk and costs. We derive MILP reformulations under the Wasserstein ambiguity set and second-order conic programming (SOCP) reformulations under the moment-based set. Using real-world data, we conduct computational studies to demonstrate the efficiency of the solution approaches and the effectiveness of the solutions. Summary of Contribution: The problem studied in this paper is motivated by a building load control problem that uses the aggregation of heating, ventilation, and air conditioning (HVAC) loads as flexible reserves to absorb uncertain solar photovoltaic (PV) generation. The problem is formulated as distributionally robust chance-constrained (DRCC) programs with right-hand side (RHS) uncertainty. In addition, we propose a risk-adjustable variant of the DRCC programs, where the risk level, instead of being predetermined, is treated as a decision variable. The paper aims to provide tractable reformulations and solution algorithms for both the (general) DRCC and the (general) adjustable DRCC models with RHS uncertainty.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Coherency-Constrained Spectral Clustering for Power Network Reduction

This paper presents a methodology for reducing the complexity of large-scale power network models using spectral clustering, aggregation of electrical components, and cost function approximation. Two approaches are explored using unconstrained and constrained spectral clustering to determine areas for effective system reduction. Once the system areas are determined, both loads and generators by type are aggregated, and their new cost function is approximated through polynomial curve-fitting or statistical methods. The performance of reduced networks is evaluated in terms of their ability to follow the true daily cost of the original system over a 24-hour period considering a set of several days. Two test systems are taken as test beds. Application of the methodology to a modified version of the IEEE 39-bus system reduces it from 17 generators to a 4-bus system and 9 generators with about 93% of accuracy. Similarly, the IEEE 118-bus system is reduced from 19 generators to a 3-bus system with three aggregated units achieving over 99% of accuracy. These findings address scalability challenges and enhance accuracy for high and mid-loading level conditions, and by aggregating thermal units with similar cost functions.

42 ENGINEERING↗
DOE OSTI2025

A generalizable machine learning-assisted fast Fourier transform algorithm to simulate the large strain phenomena in polycrystalline materials

Machine learning methods have shown initial promise in constitutive modeling for single crystals or homogenized polycrystals, delivering notable computational efficiency. However, existing machine learning-based constitutive models often lack generalizability, limiting their application across diverse boundary value problems. This study introduces a thermodynamics-informed artificial neural network model to accelerate rate-tangent crystal plasticity fast Fourier transform simulations for cross-scale deformation behaviors of polycrystals under complex loading. Our model integrates microstructural variability and local interactions effectively. To address local effects in each grain, we employ K-means clustering to group Gauss points within the microstructure into clusters assumed to be in similar mechanical states. This approach, based on self-clustering analysis, extends model scope from macroscopic stress response to the granular level, capturing mechanical responses and orientation evolution across grains. This reduces the number of nonlinear problems to solve, with cluster responses propagated throughout each group. The thermodynamics-based artificial neural network-extracted features are further processed using local material state clusters to account for history-dependent deformation and evolving microstructures. Additionally, representative volume element simulations with rate-tangent crystal plasticity fast Fourier transform provide reliable datasets for model training. The proposed model demonstrates high efficiency, accuracy, self-consistency, and enhanced generalizability in predicting strain–stress responses and orientation evolution at both individual grain and aggregate scales under complex loading conditions, such as biaxial tension and arbitrary loading scenarios.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Large-scale scenarios of electric vehicle charging with a data-driven model of control

Transportation electrification is forecast to bring millions of new electric vehicles to roads worldwide this decade. Planning to support those vehicles depends on detailed scenarios of their electricity demand in both uncontrolled and controlled or smart charging scenarios. In this work, we present a novel modeling approach to enable rapid generation of demand estimates that represent the impact of controlled charging for large-scale scenarios with millions of individual drivers. To model the effect of load modulation control on aggregate charging profiles, we propose a novel machine learning approach that replaces traditional optimization approaches. We demonstrate its performance modeling workplace charging control under a range of electricity rate schedules, achieving small errors (2.5%–4.5%) while accelerating computations by more than 4000 times. To generate the uncontrolled charging demand for scenarios with residential, workplace, and public charging we use statistical representations of a large data set of real charging sessions. We demonstrate the methodology by generating diverse sets of scenarios for California's charging demand in 2030 which consider multiple charging segments and controls, each run locally in under 50 s. We further demonstrate support for rate design by modeling the large-scale impact of a new, custom rate schedule for workplace charging.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Leveraging NREL's ResStock & ComStock Dataset to Evaluate Building Stock Electrification: Preprint

Residential and commercial buildings accounted for 40% of U.S. energy consumption in 2022 and represent a significant opportunity for decarbonization through energy efficiency and electrification, and for grid planning. Building stock energy modeling is a powerful tool that can evaluate what-if scenarios as utilities, municipalities, policymakers, building owners and others work towards equitable building decarbonization and climate goals. This presentation will highlight several high-impact use cases of the National Renewable Energy Laboratory (NREL)'s highly granular, bottom-up building stock energy modeling tools, ResStock and ComStock. These use cases cover a wide range of project scale, from neighborhood electrification analysis and municipality long-term energy planning, to state energy code development and national policy evaluation. This presentation will showcase specific real-world applications for which ResStock and ComStock have been utilized across the country, including California codes and standards cost-effectiveness analysis, New York City affordable housing electrification cost gap analysis, and California targeted electrification and gas decommissioning analysis. For each use case, this presentation will illustrate how ResStock and ComStock played a crucial role in accurately characterizing regional building stocks, providing discrete and aggregated end-use load shapes, and calculating lifecycle consumption, emissions, and costs for a variety of building electrification strategies and scenarios. Finally, this presentation will demonstrate how the data provided by ResStock and ComStock can help unlock significant outcomes for these use cases, including but not limited to, customer bill impact, incentive and program design, and energy equity analyses.

building stock modeling↗
DOE OSTI2021

Nontargeted vs. Targeted vs. Smart Load Shifting Using Heat Pump Water Heaters

Deployment of CTA-2045–enabled devices is increasing in the U.S. market. These devices allow utilities or third-party aggregators to control appliance energy use in homes, and could also be applied to end uses in small commercial buildings. This study focuses on a field study using CTA-2045–enabled water heaters to shift electric load off the peak and toward periods when renewable resources are more prevalent (e.g., near noon for solar resources and near midnight for wind resources). The following load shifting strategies were compared to understand effects on the aggregate load-shifting capabilities of Heat Pump Water Heaters (HPWHs) and on consumer hot water supply: non-targeted (traditional), targeted (grouped, with different shifting schedules) and “smart” (adaptive control commands). The results of this study show that targeted and smart control strategies yield significantly more load-shifting potential from a population of water heaters than the non-targeted approach without sacrificing hot water supply to occupants. However, as control commands become more aggressive, aggregators may face challenges in meeting consumer hot water demand. Furthermore, the findings and lessons learned can benefit electric utilities and inform updates to manufacturer controls and communications standards. The data collected may also be useful for developing and validating HPWH models.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Atomically Precise Single-Site Catalysts via Exsolution in a Polyoxometalate–Metal–Organic-Framework Architecture

Single-site catalysts (SSCs) achieve a high catalytic performance through atomically dispersed active sites. A challenge facing the development of SSCs is aggregation of active catalytic species. Reducing the loading of these sites to very low levels is a common strategy to mitigate aggregation and sintering; however, this limits the tools that can be used to characterize the SSCs. Here we report a sintering-resistant SSC with high loading that is achieved by incorporating Anderson–Evans polyoxometalate clusters (POMs, MMo 6 O 24 , M = Rh/Pt) within NU-1000, a Zr-based metal–organic framework (MOF). The dual confinement provided by isolating the active site within the POM, then isolating the POMs within the MOF, facilitates the formation of isolated noble metal sites with low coordination numbers via exsolution from the POM during activation. The high loading (up to 3.2 wt %) that can be achieved without sintering allowed the local structure transformation in the POM cluster and the surrounding MOF to be evaluated using in situ X-ray scattering with pair distribution function (PDF) analysis. Notably, the Rh/Pt···Mo distance in the active catalyst is shorter than the M···M bond lengths in the respective bulk metals. Furthermore, models of the active cluster structure were identified based on the PDF data with complementary computation and X-ray absorption spectroscopy analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Rheological and microstructural characterization of aqueous suspensions of carbon black and reduced graphene oxide

Carbon black is often used as a conductivity enhancer in battery electrodes. Reduced graphene oxide is another conducting material, with a high aspect ratio sheet-like morphology, and offers the possibility of generating a conducting network at loadings that are small compared to carbon black. Suspensions of conducting carbon, active materials and binders are typically coated on current collectors during the formation of electrodes. The rheology of these suspensions is an important indicator of the connectivity of particles in the suspension and impacts the electrical conductivity as well as the thickness of the coated layer. In this work, the rheology and microstructures of aqueous suspensions of para-aminobenzoic acid terminated carbon black (CB) with and without reduced graphene oxide (RGO) were investigated. The CB loading varied between 0.05 wt.% -10 wt.%, while the samples containing RGO had an RGO loading of 0.05 wt.%. The pH of the suspension was varied from 7.5 to 3. The carboxyl groups on the CB surface are deprotonated at pH 7.5, making the particles hydrophilic. Protonation of the carboxylate groups at pH 3 makes the particles partially hydrophobic. Suspensions of CB showed Newtonian behavior at all loadings for pH 7.5, and at loadings below 1.5 wt.% for pH 3. They are shear thinning at loadings between 1.5–10 wt.%. Interestingly, the low shear viscosity is non-monotonic with CB loading, rising up to a CB loading of 7.5 wt.%, and then dropping at 10 wt.%. Cryogenic scanning electron microscopy (SEM) showed CB particles forming aggregates at pH 3 for CB loadings greater than 1.5 wt.%. At pH 3, addition of 0.05 wt.% RGO resulted in a viscosity increase for suspensions containing 0.5 wt.% CB, as the RGO formed connections with CB particles that enhanced network formation. Adding RGO had little effect on suspensions that had 1.5 wt.% CB. The combination of SEM imaging and suspension rheology provides insight into the particle connectivity for the carbon-based materials studied here. These insights will be useful in determining coating conditions for slurries used in rechargeable battery electrodes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

AGGREGATE: dAta-driven modelinG preservinG contRollable dEr for outaGe mAnagemenT and rEsiliency (Final Report)

The AGGREGATE project team successfully developed and validated various modules for outage management. Brief summaries of each module are provided to showcase their strength for outage management and restoration for a distribution system with a high penetration of connected distribution energy resources (DERs). In recent years, inverter-based DERs have been widely deployed in distribution system. A most of behind-the-meter (BTM) solar power generation is not visible to the utility. The data-driven DER and load estimation modules are using machine learning (ML) and artificial intelligence (AI) to manage this issue, which provides an opportunity for distribution system operators (DSOs) to operate systems and make decisions in real-time for a distribution system with a high penetration of DERs deployed. Also, the estimated DER and true load can be further leveraged in network aggregation and cold-load pick up estimation for reducing the computing complexity and providing for fast restoration. After load demand and DER power generations have been estimated, the information will support topology and state estimation (SE). The topology estimation module demonstrated the viability of mixed integer linear programming (MILP) formulation to estimate the most likely operational radial topology and outage sections using power flow measurements, historical/estimated load and DERs data and smart meter ping measurements. Formulation includes continuous (power flow, load and DERs data) and binary measurements (smart meter ping measurements) in a single formulation. Errors in continuous data and binary data are modeled as normal distribution and Bernoulli distribution, respectively. In the future distribution grid, the power injection from controllable DERs will be essential for efficient and resilient grid operation. However, determining the optimal DER injections and restoration actions is dependent on knowledge of the system states. State estimation (SE), already the cornerstone of transmission energy management systems, will become commonplace in distribution management systems as more measurements become available from deployment of automated metering infrastructure (AMI). Observability analysis is the first step in SE, as it determines the sufficiency of the available measurements for accurately estimating the current system states. A new type of pseudo-measurement called a Correlational Measurement (CM) is introduced in this module, to enhance the observability of the system to enable more accurate SE. CMs encapsulate knowledge of correlation between demand patterns for similar classes of loads as well as injection patterns for same-technology renewable DERs. During grid contingency scenarios, DERs have been traditionally disconnected, without any fault ride-through capabilities. However, with new regulations and better technology, it is feasible for these resources to contribute to the grid’s restoration after an adverse event and hence enhance resilience. The controllability module proposes a two-step restoration scheme for the power system restoration process by leveraging additional degrees of freedom in power electronics interfaced DERs for mitigating voltage problems. In a resilience mode without the utility system, the distribution grid relies on DERs to serve critical load. In such a severe event with multiple faults on the distribution feeders, actuation of various protective devices (PDs) divides the distribution system into electrical islands. The undetected actuated PDs due to fault current contributions from DERs can delay the restoration process, thereby reducing the system resilience. The Advanced Outage Management (AOM) and the Advanced Feeder Restoration (AFR) modules developed in this project provide improved system resilience with multiple DERs. AOM identifies the faulted sections and actuated PDs in a distribution system with DERs by incorporating smart meter data. The most credible outage scenario including fault locations, PD actuations, and fault indicator (FI) failures is identified by a set of binary integer linear programming incorporating hypotheses. The AFR module serves to restore a distribution system with available energy resources taking into consideration the availability of utility sources and DERs. By partitioning the system into islands, critical load will be served with the available generation resources within islands based on the solution of a MILP. When the utility systems become available, the optimal path will be determined by a spanning tree search algorithm that reconnects these islands back to substations and restores the remaining load. The transmission and distribution (T&D) co-simulation module was used to validate the effect of a control action performed on the distribution side assets as it propagates to the transmission side. This ensures that the control action performed results in a feasible operating point on both the transmission and the distribution system. In addition to validation, the team used the T&D co-simulation module to demonstrate how distribution system assets can be used to mitigate issues on the transmission system. Specifically, the team demonstrated that appropriate switching operations on the distribution side can alleviate the line overload condition on the transmission side without causing new operational constraint violations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

A cross-dimensional analysis of data-driven short-term load forecasting methods with large-scale smart meter data

Electricity load forecasting is essential to utility operation and power grid stability. A wide spectrum of data-driven methods, ranging from linear regression models to more recent deep learning models have been adopted to forecast electric load over the years. However, there still lacks a holistic evaluation of the applicability of conventional statistical and machine learning based algorithms with respect to different temporal and spatial scopes, computational requirements, and sensitivity of model-tuning. Enabled by a large-scale electricity load profile dataset of over 40,000 residential customers in a utility region, we conducted a cross-dimensional analysis of data-driven load forecasting methods. Three regression-based and seven deep learning algorithms with different model configurations were evaluated in terms of their overall and peak load prediction accuracy, and training burdens, across spatial aggregation levels ranging from the transformer, feeder, substation, to neighborhood. We found, first, the load forecasting accuracy is constrained by a predictability boundary, influenced by the forecasting horizon and spatial aggregation level. Specifically, RandomForest, XGBoost, TFT, TSMixer, and TiDE models achieved less than 10 % prediction error for up to 96-h ahead forecasting for district, substation, and feeder levels, while other models struggle at long-horizon predictions; Second, for winter and summer peak load dates, most models were able to predict the peak demand timing within ± 1 h, but the prediction percentage error varied by models, with TFT and TiDE models being the top performers; Third, models with similar prediction accuracy can differ in training burden by an order of magnitude. Therefore, choosing model configurations that balance prediction performance and computational resource is an important practical consideration for large-scale deployment of the machine learning based load forecasting. The outcome of this study can guide researchers and practitioners to choose the proper load forecasting algorithms based on their problem scope, required accuracy, and available resources. The predictability boundary can serve as a benchmark for electricity load forecasting problems with new algorithms and datasets.

Li, Han↗
DOE OSTI2022

Consolidated Hydropower Data Repository: Value and Opportunities

Hydropower is one of several types of generating assets that provides energy, capacity, and services to electric power systems. It does so under rubrics and objectives—market driven and regulated, internal and external to asset and fleet owners—that address reliability, cost, price, and, increasingly, flexibility of output. The aggregation of data from multiple hydropower units can provide insights into asset operations and maintenance practices and needs and assist in meeting hydropower objectives. This paper examines the concept and potential benefits of aggregating hydropower asset data—primarily supervisory control and data acquisition (SCADA) information—with examples of insights developed from data aggregated by the Hydropower Research Institute (HRI). Data aggregation as discussed herein, and as implemented by the HRI, extends beyond multiple units in a powerhouse and beyond multiple hydropower facilities in an electric utility fleet or river system. Examples of research and analytics from such aggregated datasets range from unit load dependency analyses to modeling sensor measurements to detect and diagnose anomalies in assets. These examples showed the benefits of utilizing the entire dataset for insights into how the sensor layout of a single unit or set of units compares to the hydropower industry overall. Such insights include whether additional sensors are needed to complete analyses or to make decisions. In addition, utilizing multiple sensors of the same kind within a unit can provide an indication of possible current or upcoming problems with equipment. Although other analyses are possible, their use requires the development of complex models and, potentially, access to types of data that are currently not included with the example dataset used in this study. However, the examples studied herein confirmed the value of data aggregation in the fleet and unit contexts, and the value extends beyond multiple units in a powerhouse and beyond multiple hydropower facilities in an electric utility fleet or river system. The assessments also provided insights into potential extensions to the data aggregation concept that could further add to their value to the hydropower community, and these are included in this document as a set of recommendations.

13 HYDRO ENERGY↗
DOE OSTI2024

Aggregate data‐driven dynamic modeling of active distribution networks with DERs for voltage stability studies

Abstract Electric distribution networks increasingly host distributed energy resources based on power electronic converter (PEC) toward active distribution networks (ADN). Despite advances in computational capabilities, electromagnetic transient models are limited in scalability because of their reliance on exact data about the distribution system and each of its components. Similarly, the use of the DER_A model, which is intended to examine the combined dynamic behavior of many DERs, is limited by the difficulty in parameterization. There is a need for improved dynamic models of DERs for use in large power system simulations for stability analysis. This paper proposes an aggregate model‐free, data‐driven approach for deriving a dynamic partitioned model (DPM) of ADNs. Detailed residential distribution feeders were first developed, including PEC‐based DERs and composite load models (CMLDs), from which the aggregated DPM was derived. The performance was evaluated through various case studies and validated against the detailed ADN model and state‐of‐the‐art DER_A model with CMLD. The data‐driven DPM achieved a of over 90%, accurately representing the aggregated dynamic behavior of ADNs. Furthermore, the DPM significantly accelerated the simulation process with a computational speedup of 68 times compared to the detailed ADN and a 3.5 times speedup compared to the DER_A CMLD model.

42 ENGINEERING↗