Demand response for variable renewable energy integration: A proposed approach and its impacts
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McPherson, Madeleine↗
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Results for “load aggregation”
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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Effect of Background Electrolyte Composition on the Interfacial Formation of Th(IV) Nanoparticles on the Muscovite (001) Basal Plane
Understanding the impact of actinide nanoparticle (NP) formation is important to assess radionuclide mobility in the environment. We combined surface X-ray diffraction (SXRD) and in situ AFM to investigate the previously reported unusual electrolyte effects on Th uptake on mica. At low [Th] (0.1 mM), interfacial structures show a broad Th electron density (~50 Å). A linear decrease of Th uptake with decreasing hydration enthalpy of the electrolyte cation (Li+, K+, NH4+, and Cs+) indicates a competitive effect between Th and the electrolyte cation. Na+ is a clear outlier from this trend. In situ AFM imaging confirms the results. Particles show a vertical size of ~1–2 nm and larger lateral dimensions of ~10–20 nm, which is typical for particles formed at interfaces (heterogeneous nucleation). At high [Th] = 1 and 3 mM, all investigated electrolytes (ACl, A = Li+, Na+, K+) show similar Th uptake, indicating a much smaller impact of electrolyte composition. The interfacial structures are dominated by a high Th loading at a distinct distance (~6.5 Å) from the surface. Therefore, the main retention mechanism at high [Th] is suggested to be the sorption of Th NPs aggregated from Th oligomers present in solution (homogeneous nucleation).
Neumann, J↗
Market-based Co-optimization of Energy and Ancillary Services with Distributed Energy Resource Flexibilities
Energy storage systems and flexible loads have attracted significant interests due to their capabilities to provide various grid services. In this paper, the flexibilities are optimally allocated among multiple distributed energy resource aggregators with energy storage systems and flexible buildings by the utility coordinator through market-based co-optimization. An iterative market clearing algorithm is developed to determine the optimal energy and ancillary service prices, with consideration of both local dynamics and network constraints. Simulation results demonstrate the effectiveness of the proposed algorithm and the benefits of co-optimizing the energy and ancillary service markets.
Ma, Ke↗
Incentivizing Distributed Energy Resource Participation in Grid Services
The bulk power system is experiencing a dramatic shift as renewable generation growth continues to accelerate. Large-scale renewables adoption will help societies transition to a low-carbon, low-cost, and environmentally friendly electrical power system. However, the transition from a paradigm of generation following load to one where load follows generation will require large-scale interconnection and coordinated operation of Distributed Energy Resources (DERs), supported by open communication protocols. In this future grid scenario, DER aggregations will provide critical grid services that enable high penetration levels of renewable generation. This position paper presents an Energy Service Interface (ESI) that defines scope for ensuring secure, trustworthy information exchange between grid service providers and DERs. The goal of the ESI is to encourage large-scale participation of DERs in order to provide grid services through dispatch of DER aggregations. This position paper also presents an open smart energy communications protocol that allows DERs to advertise their characteristics and participate in grid services, within constraints established by the ESI. The paper presents several monetization incentives that grid service providers could use to encourage large-scale DER participation.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
A Privacy-Aware Federated Learning Framework for Distributed Energy Resource Analytics in Constrained Environments
To be resilient against extreme weather events, the rural communities in Puerto Rico are leveraging distributed energy resources (DER). However, computing frameworks sup-porting the grid in critical decision-making are still largely centralized. Sensitive consumer data are transmitted over the Internet or cellular networks to a secondary or tertiary node. It guarantees better situational awareness at the cost of a wider attack surface, jeopardizing user privacy, as more DER come online. Cloud, Edge, and Fog computing all require data aggregation at some level. This paper introduces a privacy-aware federated learning framework that leverages the Fog model by pushing analytics all the way to the DER and load assets. These local models train on individual asset data and transmit only learned parameters (such as weights) over secure communications to a global decision-maker. By abstracting personally identifiable consumer data without impacting decision optimality, this framework better aligns with distributed power generation paradigm.
Sundararajan, Aditya↗
Developing and Evaluating Metrics for Demand Flexibility in Buildings: Comparing Simulations and Field Data
Building demand flexibility (DF) has attracted significant attention in recent years among researchers, technology developers and control companies, aggregators, utilities, and many others. There are numerous challenges with today’s electricity systems such as managing peak demand capacity and integrating variable renewables into the grid. Flexible building loads can provide various grid services to help reduce electricity costs, smooth out renewables intermittency and balance supply and demand. Recognizing this, the US DOE is leading the Grid-interactive Efficient Buildings (GEB) initiative which includes research to evaluate the potential, availability and timing of flexible loads. In this paper we present load shed metrics for three building types – medium office, large office and retail store – and compare prototype simulation results with measured data from 12 actual buildings that participated in hot summertime utility demand response (DR) events. The DR strategies included varying zone temperature and reducing light levels. The magnitude of a key DF metric, “demand decrease intensity” (or “shed intensity”) (W/ft2), between the simulation results and field data are similar (14-32% differences) for both mean and median values, though the field data show much larger variation among DR events. The coefficient p-values from linear regression model tests showed that outside air temperature is a significant variable for the whole building shed intensity when the resetting zone temperature strategy is deployed. These findings support the concept of using prototype building simulation to estimate building DF and expanding future simulation research to additional building types and climate zones.
Liu, Jingjing↗
Cold-Climate Packaged Heat Pumps - Resistance is Futile
The U.S. currently has a large market for room/zonal space conditioning equipment focused primarily on cooling, namely packaged terminal air conditioners (PTACs), packaged terminal heat pumps (PTHPs), and room air conditioners. This equipment largely relies on electric resistance for heating, particularly when outdoor temperatures fall to 40°F or below, presenting a compelling opportunity for energy savings and carbon reductions from reverse cycle heating. This paper provides an overview of cold climate packaged heat pump technologies in the market capable of high output down to outdoor temperatures of 5°F and identifies the technological and market barriers to adoption. For state-of-the-art cold climate packaged heat pumps sold in the retrofit market, defrost meltwater management is the principal barrier to implementation. An energy use analysis is conducted on state-of-the-art cold climate packaged heat pumps for heating and cooling loads by building vintage and climate zone. Potential solutions to defrost meltwater are proposed and modeled for their impact on heating energy efficiency. A national impact assessment estimates the aggregate energy savings and carbon reductions if cold climate packaged heat pumps were to replace existing PTACs and PTHPs in the U.S. building stock. A national, two-track approach is proposed for increasing market adoption in the replacement and new construction markets.
ACEEE Summer Study, cold climate, heat pumps, ccph↗
Microstructure-sensitive mechanical behavior of an additively manufactured psuedoelastic shape memory alloy
The additive manufacturing of shape memory alloys into complex geometries enables fabrication of advanced functional systems across a variety of fields and domains. This work presents results focused on the mechanical behavior of additively manufactured shape memory pseudoelastic NiTi. The deformation induced solid state phase transformation from austenite to martensite allows this system to accommodate large recoverable strains. This deformation behavior is fundamentally driven by crystal-scale transformation physics. Laser powder bed fusion processing reveals that the resulting microstructure, both grain morphology and crystallographic texture, is strongly dependent on the manufacturing processing history. Exhaustive mechanical testing demonstrates that these microstructural factors strongly impact both tensile and cyclic stress–strain behavior. Cyclic dissipative behavior, however, is similar across all tested microstructures following an initial transient period. Remarkably, analysis of spatial strain fields during tensile loading reveals two distinctly different localization “modes”. The first is initiation of localized deformation bands which continuously propagate through the tensile bar during loading. In the second mode localization is observed but lacks propagation; instead additional localization cites nucleate during subsequent loading. The latter phenomena is suspected to be driven by grain-scale deformation physics as the localized band morphologies coincide with grain morphologies. These phenomena strongly impact the resulting aggregate stress–strain behavior. Hence, manufacturers and designers of psuedoelastic functional components must at the very least consider the potential variability in properties when considering additive manufacturing processing. More ideally the process–structure–property relations can be used to further tailor and optimize final functional performance.
Additive manufacturing↗
Electric Vehicle Managed Charging: Forward-Looking Estimates of Bulk Power System Value
When and where electric vehicle charging occurs has significant implications for power systems supporting widespread electric vehicle deployment with high shares of wind and solar generation. Numerous studies have estimated the value of scheduling or otherwise managing electric vehicle charging in such power systems. This study improves on those earlier works by leveraging detailed simulation models for electric vehicle adoption, electric vehicle use, electric vehicle charging, and bulk power system operations; and linking them with methods for describing charging flexibility at both the individual vehicle and aggregate levels. This study closely analyzes electric vehicle managed charging (EVMC) performance along the dimensions of flexibility type (within-charging session or within-week scheduling), dispatch mechanism (direct load control or one of several price-based mechanisms), and participation rate, under the assumptions of ubiquitous chargers and all trips completed on time. The study is located in a passenger light-duty vehicle adoption scenario with 100% electric vehicle sales by 2035, and in an envisioned 2038 New England power system for which within-region generation is 84% clean.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Packetized Energy Management: Coordinating Transmission and Distribution (Final Report)
The project Packetized Energy Management (PEM): Coordinating Transmission and Distribution was part of the ARPA-E NODES program from 2015 to 2023. The high-level goal of the project was to develop and demonstrate novel, scalable, and impactful technologies related to the coordination of networked distributed energy resources (DERs). By demonstrating responsive means by which fleets of DERs could be coordinated to enhance grid operation and reliability, the U.S. could accelerate renewable integration and electrification efforts and meet decarbonization goals.
25 ENERGY STORAGE↗
Scalable multiscale modeling of platelets with 100 million particles
Here, we developed the core components of the AI-aided multiple time stepping algorithm for multiscale modeling of cell dynamics. This algorithm was implemented and analyzed on two supercomputer architectures with an application of simulating the aggregation of 250 platelets, or 102 million particles. To scale on these computers with complex memory and network architectures with GPUs, we devised a biomechanics-informed task mapping scheme to optimize load imbalance, communications, and memory utilization. Our simulations, scaling well up to 192 nodes on a Summit-like supercomputer with a peak speed of 11 petaflops, achieved a rate of 423 μs/day which is 500 times faster than the conventional algorithm using static time step and this has enabled studies of record size blood clots at record spatial–temporal resolutions. Additionally, we discovered the sensitive dependence of the scalability and execution time on the methods of decomposition, CPU–GPU coupling, and task mapping.
97 MATHEMATICS AND COMPUTING↗
PV Generation and Load Forecasting for Adjuntas PR Community Microgrids
Existing frameworks to forecast time-series photovoltaic (PV) output power and consumer load for microgrid operations and controls assume a near-continuous availability of real-time input features from the field assets such as PV inverters, energy meters, and weather station. These incoming data points are used to periodically retrain models and update forecast snapshots over a moving horizon window, be it one hour-ahead, one-day ahead, or one-week ahead. However, such frameworks are not resilient to disruptions in data availability caused by losses in communications between the field sensors and data loggers. Hence, there is a need for programs that assume no availability of real-time microgrid asset data and still make reliable forecasts that can be used for decision-making. Such programs would be apt to function in extreme weather events such as hurricanes and would use lightweight recursive time-series models to independently forecast solar irradiance and ambient temperature, then compute PV power from those forecasts, as well as independently forecast consumer load. The codebase performs forecasting for the scenario of when the microgrid does not have a reliable access to forecasts or real-time observations of solar irradiance (I) and ambient temperature (AT) and load (Load) to be able to adequately forecast, in real-time, the PV power production or a business' load. In this case, using historical values of PV power and load, a univariate forecasting of generation and consumption are respectively made. The use-case in particular has two sub-scenarios: one, a normal 7-day ahead forecast where the unavailability of real-time data is assumed due to infrastructure issues such as loss of communication or sensor maintenance or service downtimes. Whereas a hurricane-caused unavailability of real-time data requires a second model trained specifically on historical hurricane days to be able to capture the extreme day behavior of generation in particular, and load if applicable. A gradient boosted regression tree comprises an ensemble of additive models that map between the input of historical values (be it irradiance, temperature, or load) and their corresponding output forecasts of a given horizon such that the individual learner predictions are summed up over the total number of such learners in the ensemble to produce an aggregate forecast. A weighting mechanism is applied to the training data in each iteration, where actual and forecast values are compared to penalize incorrect forecasts by increasing the weight and reducing it to reward correct forecasts. The code's benefits are that it: (a) accounts for a contingency where communication loss renders newly measured real-time data unavailable for model tuning and snapshot updates; (b) presents blind forecasting that recursively determines the next time-step value in a horizon using the forecast of the same attribute from a prior step; and (c) employs lightweight models that, once trained, can reliably generalize for different horizons, which make them suitable for enhancing the resilience of field microgrids prone to extreme events that encounter disruptions to data availability.
Sundararajan, Aditya [Oak Ridge National Laborator↗
Integrating Electric Vehicle Charging Infrastructure into Commercial Buildings and Mixed-Use Communities: Design, Modeling, and Control Optimization Opportunities: Preprint
This paper discusses modeling and field studies of controlled EV charging that have been performed with the goal of minimizing requirements for infrastructure upgrades, minimizing building peak demand charges, and maximizing the use of on-site generation. We present a large-scale workplace charging pilot of a demand-controlled scheduled EV charging system with over 250 active daily commuters, successfully demonstrating management of aggregate charging power to avoid new infrastructure investments, mitigate peak demand charges, and provide cost-effective workplace charging to users. In addition to understanding opportunities for demand management, integrating these controllable loads into the energy modeling process for new buildings will also be necessary. This paper then presents an example energy modeling process that evaluates the potential effects of EV charging on building load profiles and infrastructure requirements for a mixed-use community. Finally, we discuss an illustration of how EV charging can be controlled to be synergistic with other building loads and distributed generation.
buildings↗
Community-Scale Energy Efficiency Assessment for Zero Net Energy Using the URBANopt Simulation Platform: Preprint
This project explored an innovative modeling approach for assessing the energy efficiency potential of the existing low-income Oceano neighborhood, in unincorporated San Luis Obispo County, and its effects on renewable generation requirements for achieving zero net energy (ZNE). One particular challenge faced was ascertaining the technical energy savings potential across a large neighborhood in a timely manner. The project overcame this challenge through the use of the URBANopt simulation platform, developed at the National Renewable Energy Laboratory. URBANopt manages the automated model creation, simulation, and result aggregation for different design scenarios of existing or candidate buildings in a large neighborhood. Customized energy efficiency packages considered for the residential and commercial sector in the neighborhood included lighting, HVAC, plug load, water heating, envelope, and energy education measures. As a result of the analysis for the Oceano community, energy efficiency measures that are available through incentive programs may provide a 7.4% reduction in the required distributed energy resource (DER) size, corresponding to a 25,000 ft2 decrease in PV array area, to achieve ZNE. Through this work, a computational tool was developed and is being leveraged by the County of San Luis Obispo (CoSLO). The tool has enabled the CoSLO staff to perform similar analysis of the technical potential for energy efficiency, and an understanding of the DER asset requirements and potential savings in other communities within the county.
building energy modeling↗
Hourly Electricity Demand Profiles for Each County in the Contiguous United States
This dataset provides estimated hourly electricity demand for each county in the contiguous United States from 2016-2023. The demand profiles represent the sum of two components: (1) Weighted averages of reported hourly demand profiles for North American Electric Reliability Corporation balancing authority (BA) regions and subregions, scaled to match annual estimates of county-level retail sales and direct use of electricity and weighted by the estimated percentage of county load served by each BA region or subregion. (2) Weighted averages of modeled hourly, county- and sector-level distributed photovoltaic (DPV) capacity factor profiles, scaled to match annual estimates of on-site consumption of DPV-generated electricity for each county and weighted by the percentage of consumption attributable to each sector Annual county-level retail sales are estimated by aggregating utility-reported sales to the state level and allocating the results to counties according to each county's share of state population. Annual county-level direct use is calculated by aggregating power plant-reported direct use values. Annual county-level on-site consumption of DPV-generated electricity is estimated by aggregating utility-reported net metering data to determine the amount of DPV-generated electricity sold back to the grid for each state, subtracting those values from modeled state-level DPV generation estimates, and allocating the results to counties according to each county's share of statewide modeled DPV generation. The open-source Python code used to develop this dataset is available at "Historical Load Data Repository" link below.
14 SOLAR ENERGY↗
Non-equilibrium reducing flame aerosol process to create supported high-entropy alloy nanoparticles
High-entropy alloy (HEA) nanomaterials provide opportunities and property combinations for energy and electronic applications, but their practical synthesis faces challenges of elemental immiscibility, metal reducibility, and particle aggregation during their synthesis. Herein, we report a broadly applicable non-equilibrium, scalable, and in-situ reducing flame aerosol process for synthesis of supported HEA nanoparticles. This versatile process can directly load a high concentration of 2 ~ 4 nm HEA nanoparticles on various 1- to 3-dimensional supports. Notably, simultaneous formation of HEA nanoparticles and a mesoporous silica support was successfully realized in a single step. Exploration of this process demonstrates the role of kinetics and entropy on decreasing alloy particle size and altering the reducibility of elements. We propose an entropy-induced reduction mechanism to incorporate oxidizable elements into HEAs, which extends the compositional space of HEA nanoparticles. As a representative catalytic application, we present a RuPdOsIrPt/graphene electrocatalyst with high activity and stability for hydrogen oxidation reaction. Our findings open horizons for high-performance HEA design and applications in diverse fields such as catalysis, electrochemistry, and sensing.
organic↗
Defining a Platform Approach and Market Participation: Data Driven Business Models for Solid State Transformer-Based Synthetic Inertia and Voltage Stability Controls (CRADA Final Report, Project 1, Mod 1)
The primary objective of this project is to determine the incremental value created with the medium voltage solid-state transformer (MV SST) technology to different stakeholders in view of the updated DER grid regulations. This includes studying the benefits of the MV SST technology in a range of use cases for EV and DER penetration including (1) “corridor charging” for EVs and (2) solar plus storage (FERC 2222). The potential customers of this technology include utilities for EV charging, DER installers who must meet utility interconnection requirements, balancing authorities, and DER aggregators. The traditional transformers on the grid could be a limiting factor for the EV-grid integration as the distribution transformers were not designed to handle the dynamic and fluctuating EV charging loads. Thus, the issues such as voltage fluctuations, increased losses and reduced efficiency [1] can negatively impact the grid operation. To address these challenges, transformers with flexibility and adaptability become imperative to meet the evolving energy demands. In this regard, the concept of Medium Voltage Solid-State Transformers.
14 SOLAR ENERGY↗
Building-to-Grid Modeling Framework with a Case Study of Battery Systems
Coordinating electricity load shifting operations of multiple buildings has the potential to achieve significant benefits for both building owners and grid operation. In support of this research, this paper develops a novel building-to-grid modeling framework, which uses EnergyPlus to develop physics-based building energy models and GridLAB-D to develop distribution-level grid models. The data exchanges between buildings and distribution-level grid are conducted using Python script and HELICS co-simulation platform. Then, by using this modeling framework, this paper conducts a case study of battery systems in a connected community, which consists of 10 multi-family buildings with appropriately sized batteries. The three scenarios, baseline operation, tolerant to price fluctuations, and opportune usage, are studied. The result displays that, by adopting onsite batteries, both electricity costs for building owners and aggregated peak power demands are reduced.
Building-to-grid, connected community, co-simulati↗