Advanced magnetic Core Modeling for Pulsed Power Systems in LTSpice
Explore the source record for details and available documents.
SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Explore the source record for details and available documents.
Here, this study develops the sparse chronology method to enhance the representative period framework in capacity expansion models, enabling the effective integration of long-duration energy storage modeling. Traditional representative period methods cannot capture the state of charge of seasonal energy storage systems because they do not establish effective inter-day linkages to connect the state of charge between periods. The sparse chronology approach addresses this limitation by establishing inter-day linkages that allow state of charge to shift inter-seasonally. At the same time, it groups identical representative days into partitions, applying constraints sparsely and implicitly to reduce computational load further. Validation results demonstrate that this method successfully simulates long-duration energy storage patterns, achieving close alignment with a continuous yearly benchmark model, with seasonal trends and state of charge cycles clearly represented. The computational load analysis reveals that the sparse chronology method efficiently applies constraints on maximum and minimum state of charge limits within the representative day framework, eliminating the need for detailed constraints on each individual day. By partitioning representative days and constraining only the start and end of each partition, the method significantly decreases computational requirements. Simulation results show that sparse chronology closely approximates the continuous yearly method's accuracy, even with as few as 20 representative days, achieving correlation values with the benchmark of nearly 0.9 in state of charge plots. Furthermore, it maintains computational efficiency, requiring only 4 % of the solver time compared to the continuous yearly method with 20 representative days. This approach allows capacity expansion models to incorporate long-duration energy storage with high temporal, spatial, and technological resolution, enabling more detailed modeling for large-scale power systems.
The approach used is the Multi-Agent System (MAS) paradigms, where systems components are represented as agents, interacting both with each other, and with the environment in which they evolved. Agents behaviors correspond to components in the real Integrated Water-Power System. The model simulate actions and interactions of these (autonomous) agents to analyze their effects on the overall system. Agents in the water system capture components of water collection, treatment, transportation, distribution and use (e.g. pipe, canal, pump, water demands for agriculture, etc.). Agents in the power system capture components of power generation, transportation, distribution and use (electricity demands, sources, etc.).
This study compares two battery modeling approaches for capacity expansion models: discrete-duration and continuous-duration formulations. In the discrete approach, battery duration is fixed, and power capacity is optimized. In the continuous approach, both power and energy capacities are decision variables, allowing storage duration to be optimized endogenously. Although both discrete-duration and continuous-duration battery formulations are used in long-term power system planning models, the literature has provided limited direct, systematic comparisons of their implications within a common modeling framework. To address this gap, this study implements both approaches in the Regional Energy Deployment System (ReEDS TM ) capacity expansion model using two resource adequacy methods, across a range of future system conditions, and with varying battery cost projections. Results show continuous-duration and high-resolution discrete approaches produce similar capacity expansion outcomes. The continuous formulation achieves faster runtimes compared to discrete-duration runs with many discrete-duration options. However, the discrete-duration approach allows users to choose to have limited fidelity for storage duration options, which in some cases can outperform the continuous formulation. The continuous formulation has the lowest overall system costs, indicating its ability to fine-tune storage duration to better meet specific system needs. This study's findings provide a side-by-side evaluation of discrete and continuous battery modeling approaches and offer guidance for improving the representation of real-world systems, flexibility, and computational efficiency for representing energy storage in long-term power system planning models.
This study explores the air quality and health impacts of power system dispatch decisions by integrating a detailed atmospheric dispersion model and a health impact model with power system simulations.
For the U.S. Department of Energy’s (DOE) 2016 Small Business Voucher for Marine and Hydrokinetic (MHK) System, Second Round 2016, ORPC intends to work with the National Renewable Energy Laboratory (NREL) to perform dynamometer testing of the MHK generator systems and its associated controls and inverters. ORPC will provide the generator, variable frequency drives (VFD), controls, and inverter for this testing. NREL will utilize the NREL Energy Systems Integration Facility (ESIF) and dynamometer facilities at the National Wind Technology Center (NWTC) for this work. Modification 6: Additionally, NREL will conduct a feasibility study for implementing passive DC rectification at the turbine.
The South Asia Group for Energy (SAGE) is working with Grid India and the Central Electricity Authority (CEA) to determine the best methods to deploy more inverter-based resources (IBR) such as solar photovoltaic generators and utility-scale battery. To address grid strength and stability concerns, planners and operators are working with the National Renewable Energy Laboratory's (NREL) team, through SAGE, to identify potential solutions for strengthening the grid and fostering grid reliability.
As part of continuing efforts to develop support, understanding, and infrastructure of data center integration onto the electric grid, our work aims to model and analyze the behavior of data center loads in a microgrid power system. Our model consists of renewable energy sources, batteries, and a nuclear reactor-steam Rankine cycle to power data center loads. The simulation studies investigate the electrical behavior of the microgrid system to assess its ability in supporting large data center electrical demands.
Here, this paper introduces a methodology for parameterizing the DER_A model using a novel smooth mathematical representation, simplifying the process and preserving accuracy in modeling inverter-based generator (IBG). The methodology employs an online parameterization process that can operate in real-time. The model parameterization process is structured into five sequential steps, each targeting a specific aspect of the DER_A model through moving horizon estimation. This approach adapts to systems with varying voltage and frequency support requirements by selectively applying each step. Simulation results on systems with both known and unknown parameters validate the methodology’s effectiveness. The online moving horizon estimation technique accurately captures the dynamics of the overall system and ensures that the parameterized DER_A model closely mirrors the real system’s voltage, current, and power dynamics. The findings highlight the potential of this methodology to substantially improve and simplify the dynamic modeling of power systems, paving the way for more reliable and robust IBG and grid integration.
In studies on complex network systems using graph theory, eigen-analysis is typically performed on an undirected graph model of the network. However, when analyzing cascading failures in a power system, the interactions among failures suggest the need for a directed graph beyond the topology of the power system to model directions of failure propagation. To accurately quantify failure interactions for effective mitigation strategies, this paper proposes a stochastic interaction graph model and associated eigen-analysis. Different types of modes on failure propagations are defined and characterized by the eigenvalues of a stochastic interaction matrix, whose absolute values are unity, zero, or in between. Finding and interpreting these modes helps identify the probable patterns of failure propagation, either local or widespread, and the participating components based on eigenvectors. Then, by lowering the failure probabilities of critical components highly participating in a mode of widespread failures, cascading can be mitigated. Here, the validity of the proposed stochastic interaction graph model, eigen-analysis and the resulting mitigation strategies is demonstrated using simulated cascading failure data on an NPCC 140-bus system.
GO WEST is an open-source power grid modeling framework for U.S. Western Interconnection, which allows users to tailor the model depending on their research study and science questions. It covers 28 balancing authorities (BA) and 12 states in U.S. Western Interconnection. GO WEST allows users to select different number of nodes and come up with a simplified network by utilizing 10,000 nodal topology of U.S. Western Interconnection created by Texas A&M University. Users can try and select different number of nodes, mathematical formulations (linear programming vs. mixed-integer linear programming), transmission line limit scaling factors, and hurdle rate scaling factors. GO WEST offers a unit commitment and economic dispatch (UC/ED) module to simulate grid operations on an hourly scale. In this sense, users can calibrate and validate their model versions by comparing model outputs to historical datasets. TEP is an open-source transmission capacity expansion model, built on GO WEST framework. It utilizes linear programming to optimize transmission capacity addition investment on existing lines within GO WEST framework. In this sense, TEP model only increases the thermal capacity of existing transmission lines and does not add new lines to the system, which leaves the topology preserved. TEP minimizes the total cost of the system which comprises the operational cost of satisfying electricity demand (i.e., generation cost), cost of loss of load (i.e., unserved energy), cost of power flow, and cost of new transmission capacity additions (i.e., investment cost). In order to use TEP model, users need to create scenarios with GO WEST framework. In this analysis, outputs from several models are used to create future inputs to GO WEST and TEP models, including GCAM-USA, TELL, CERF and reV. This dataset includes experiment inputs and outputs from three different transmission expansion scenarios (cooperative, intermediate, and individual) for 2019 and 2059. For 2019, a base scenario to illustrate the default (i.e., historical) power grid operations is also included. This study utilizes rcp45hotter_ssp3 scenario from a previous version of GCAM-USA simulations. Sources of the shapefiles in supplementary data are HIFLD Open and U.S. Energy Atlas. Please see the README file for a detailed description of the main and supplementary data.
This webinar will explore how the National Laboratory of the Rockies' REopt(R) web tool can evaluate the techno-economics of combined heat and power (CHP) technologies and systems to assess performance and financial viability. REopt can analyze both standalone CHP systems, and systems paired with other on-site energy resources. Participants will gain practical insights into using REopt to optimize site energy strategies, reduce energy costs, and improve site energy security and outage recovery.
This slide deck describes typical power systems of large datacenters along with reliability problems to bulk power systems from large-scale integration of datacenters. The slide deck covers the architecture of datacenter power systems, different power electronic converters used inside datacenters, their operation modes, and R&Dopportunities in maintaining grid stability.
Power system inertia is the inherent capability of a power system to resist changes in its frequency during disturbances. Real-time inertia estimation technology has become more important due to the low-inertia issues caused by the increasing integration levels of inverter-based resources (IBRs) from renewable energy; however, existing inertia estimation methods hardly consider multiple frequency response controls that act within the same time frame as conventional inertial response, thus making measured inertia values vary under different testing conditions. To resolve this issue, this paper proposes a novel real-time estimation method to simultaneously estimate a power system's inertia constant and frequency response droop constant using a well-designed probing signal. First, we formulate the inertia and frequency response model of a power system with IBRs. Second, through the integration and manipulation of the developed model, we propose a multivariate linear regression- based estimation method that is resilient to measurement noise. Third, we design a probing signal that can be injected by IBRs to incite the required transients for estimation. Finally, we validate the proposed estimation method through comprehensive power- hardware-in-the-loop experiments using inverter hardware and a realistic island power system model. The results demonstrate that the proposed method can accurately estimate the inertia and droop value of the power system with grid-following IBRs and grid-forming IBRs with virtual synchronous machine control.
Power system inertia is the inherent capability of a power system to resist changes in its frequency during disturbances. Real-time inertia estimation technology has become more important due to the low-inertia issues caused by the increasing integration levels of inverter-based resources (IBRs) from renewable energy; however, existing inertia estimation methods hardly consider multiple frequency response controls that act within the same time frame as conventional inertial response, thus making measured inertia values vary under different testing conditions. To resolve this issue, this paper proposes a novel real-time estimation method to simultaneously estimate a power system's inertia constant and frequency response droop constant using a well-designed probing signal. First, we formulate the inertia and frequency response model of a power system with IBRs. Second, through the integration and manipulation of the developed model, we propose a multivariate linear regression-based estimation method that is resilient to measurement noise. Third, we design a probing signal that can be injected by IBRs to incite the required transients for estimation. Finally, we validate the proposed estimation method through comprehensive power-hardware-in-the-loop experiments using inverter hardware and a realistic island power system model. The results demonstrate that the proposed method can accurately estimate the inertia and droop value of the power system with grid-following IBRs and grid-forming IBRs with virtual synchronous machine control.
The power system is expected to evolve rapidly with increasing deployment of power electronic interface and conditioning systems, microgrids, and hybrid AC/DC grids. Among power electronic systems, back-to-back (BTB) converters can be a powerful interface to integrate microgrids and networked microgrids. However, in system level dynamic simulations for synchronous machine dominated system with BTB converters, a detailed electromagnetic sinusoidal models is not warranted as these simulations are focused on electromechanical transients. This paper focus on developing a simple phasor model for back- to-back converters that can be easily integrated into powerflow solvers to facilitate large-scale power system simulations. The model is implemented using C++ language and integrated into GridLAB-D software as a potential new capability. The GridLAB-D phasor domain model is validated against the electromagnetic transient (EMT) simulation of the detailed switching model. Simulation results show that the phasor model successfully captures the dominant dynamics of the converters.
This paper introduces a model for simulating hybrid plants participating in energy and ancillary services for bulk power system studies. The model considers a hybrid plant comprised of a renewable energy source, a thermal power unit, a storage unit, fixed power loads, or any combination of these technologies. The model focuses on Production Cost Modeling (PCM) studies under the assumption of centralized dispatch. We present an example case study to illustrate the use of the model in a single-stage production cost model similar to those conducted by planning agencies. We explore the allocation of behind-the-meter ancillary services products and total energy participation to hybrid plant sub-assets and the resulting impacts on the system's ancillary service allocation. The model is implemented and simulated in a unit commitment problem in the RTS test system, which was modified to include a hybrid plant asset.
Energy models for power systems require ongoing updates to reflect advancements in equipment technology and the increasing complexity of power electronic devices. This study utilizes a Power Hardware-in-the-Loop (PHIL) experimental setup to validate custom photovoltaic (PV) inverter models, aiming to enhance and expedite the development of advanced renewable energy models. The research compares the performance of a physical inverter with generic Renewable Energy Source (RES) models recommended by the Western Electricity Coordinating Council (WECC). As inverter-based renewable energy sources become more prevalent in modern electrical grids, it is crucial that dynamic models accurately represent their real-world behavior. Accurate models improve our understanding of these energy resources and their interactions with the grid. The proposed model enhancements are designed to better reflect real inverter performance, based on insights from PHIL experiments. These models are developed using the open source Modelica language and the OpenIPSL Modelica Library, allowing integration across various simulation tools without re-implementation. The paper concludes with a thorough assessment, comparing the enhanced models with PHIL experiments on a real PV inverter in a controlled laboratory setting. As a result, the study provides the enhanced WECC RES models and validation data as open source resources, facilitating further research and development.