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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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At least 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Transient Simulations with a Large Penetration of Converter-Interfaced Generation: Scientific Computing Challenges and Opportunities

Current trends in energy systems point to renewable energy sources (RESs) and battery energy storage systems (BESSs) becoming prevalent in power system operations. As of writing this article, the United States has more than 37 GW of utility-scale solar capacity and an additional 112 GW under development. With the rapidly declining capital costs of many of these technologies, we can expect significant deployments in the coming years.

analytical models↗
DOE OSTI2021

A Zonal Volt/VAR Control Mechanism for High PV Penetration Distribution Systems

This paper presents a zonal Volt/VAR control scheme for regulating voltage in unbalanced 3-phase distribution systems using inverter-based resources (IBR). First, the dependency between nodal voltage changes and IBR reactive power injections is derived via voltage sensitivity studies. Then, a fast-incremental clustering method is used to divide the distribution circuit into weakly-coupled zones based on correlations between nodal voltage sensitivities. The weak-coupling allows the voltage to be regulated independently within each zone using a rule-based voltage controller to dispatch IBR for voltage corrections. Simulation results on actual distribution feeder show that the proposed zone-based Volt/VAR control method maintains system voltages within their operational limits while reducing the runtime of the Volt/VAR controller from tens of second to a couple of milliseconds compared with centralized, optimization-based Volt/VAR control methods.

Alrushoud, Asmaa↗
DOE OSTI2022

Phase Identification in Real Distribution Networks with High PV Penetration Using Advanced Metering Infrastructure Data

Many distribution network monitoring and control applications - including state estimation, volt/VAR optimization, and network reconfiguration - rely on accurate network models; however, the network models maintained by utilities can become outdated because of restoration activities, network reconfiguration, and missing data. With the widespread deployment of advanced metering infrastructure (AMI), abundant measurement data from low-voltage secondary networks are available. The AMI measurement data can be used for phase identification to improve the network models. Although the existing phase identification techniques work well in passive distribution feeders that do not have photovoltaic (PV) generation, they can fail to accurately identify the phases in the presence of PV. This paper proposes a robust phase identification algorithm based on supervised machine learning that accurately identifies the AMI meter phase connectivity in the presence of significant PV generation. The proposed algorithm does not require network topology information or feeder head measurement data. The algorithm is validated using the AMI measurement data collected in the field and the field-validated phase connectivity database on two real distribution feeders from San Diego Gas & Electric Company that have significant PV generation.

advanced metering infrastructure↗
DOE OSTI2024

Grid-Forming Inverters for Stability Improvements in Bulk Power Systems with High Inverter-Based Resources Penetration

The grid-forming inverter (GFM) has been considered recently as a means to enhance grid stability in areas with Inverter-based Resources (IBRs) concentration. This paper assesses the potential improvements the GFM can bring to Bulk-power Systems (BPS) through two distinct studies. The first study was conducted on West Texas in ERCOT with presence of more than 35GW of IBRs, the Grid-Following (GFL) batteries were replaced with GFM batteries. The results demonstrate that the GFM significantly improved the voltage profile and active power injection during and after a fault. The second study focused on a local area within ERCOT grid. In this case, a battery operating as GFL within the local study area was replaced with a GFM battery. The findings clearly illustrate that GFM can strengthen the area and allow a higher IBRs power generation and a higher power export from the local study area.

Quedan, Amro↗
DOE OSTI2023

Developing Frequency Stability Constraint for Unit Commitment Problem Considering High Penetration of Renewables

As zero-carbon electricity systems become the trend of future grid, the system inertia provided by conventional synchronous generators (SGs) keeps decreasing. The resultant lower system inertia will inevitably cause frequency stability problem, especially in the first few seconds following disturbance. To tackle this challenge, this paper proposes a frequency stability constraint for power systems unit commitment problem by considering the fast frequency responses (FFRs) from inverter-based resources (IBRs). Our developed frequency stability constraint is grounded on an analytical frequency nadir estimation framework that considers both SG and IBR dynamics. The accuracy of our frequency nadir estimation framework is validated by most severe N-1 contingency simulation result in a real island system. Then, the adaptive inertia frequency stability constraint is derived by performing sensitivity analysis with our frequency nadir estimation framework. Finally, we demonstrate the effectiveness of our developed frequency stability constraint with one year day-ahead unit commitment results of the island system.

fast frequency response↗
DOE OSTI2020

A Study of Imbalance Levels Attributed to Photovoltaic Penetration in Distribution Systems

Abstract— The number of renewable sources, namely photovoltaics (PV), continues to grow within distribution systems. How placements impact a system’s performance remains a concern within both the research and the utility communities. Beyond system node voltages and real power losses, this work looks to industry standard imbalance metrics and the changes experienced on these values with varied levels of PV. Specifically, four imbalance metrics are presented and studied for an actual 906-bus distribution system. Multiple loading conditions and photovoltaic placement schemes are explored to compare the changes seen on these metrics across a variety of scenarios experienced by the network.

14 SOLAR ENERGY↗
DOE OSTI2022

Simulating Impacts of Extreme Events on Grids with High Penetrations of Wind Power Resources

As extreme weather events become more frequent and intense, the demand for connecting grid operation and infrastructure planning with extreme event models will increase as well. We present a methodology for creating damage contingencies and scenarios for electric transmission grids during a hurricane strike. Using WIND Toolkit meteorological data in conjunction with fragility curves for various electric grid elements, we generate stochastic damage scenarios that can be used for short- and long-term planning problems, e.g., emergency asset management. Included is an example case study: Hurricane Dolly damaging a synthetic 2000-bus test system during its landing in Southern Texas. We perform statistical analysis of damages and discuss topological effects on the example synthetic grid. Also, we include a cursory evaluation of impacts using simplified operational models. Finally, we discuss how our method can be extended to use even higher-fidelity meteorological data sets and suggest directions for future work.

contingencies↗
DOE OSTI2020

Penetration Through Slots in Cylindrical Cavities Operating at Fundamental Cavity Modes

In this article, we examine the coupling into an electrically short azimuthal slot on a cylindrical cavity operating at fundamental cavity modal frequencies. We first develop a matched bound formulation through which we can gather information for maximum achievable levels of interior cavity fields. Actual field levels are below this matched bound; therefore, we also develop an unmatched formulation for frequencies below the slot resonance to achieve a better insight on the physics of this coupling. Good agreement is observed between the unmatched formulation, full-wave simulations, and experimental data, providing a validation of our analytical models. We then extend the unmatched formulation to treat an array of slots, found again in good agreement with full-wave simulations. These analytical models can be used to investigate ways to mitigate electromagnetic interference and electromagnetic compatibility effects within cavities.

42 ENGINEERING↗
DOE OSTI2025

Deep Factorization Machine Learning for Disaggregation of Transmission Load Profiles with High Penetration of Behind-The-Meter Solar

The ever-growing integration of distributed energy resources (DERs), especially behind-the-meter (BTM) solar generations, poses imperative operational challenges to system operators such as regional transmission organizations (RTOs). It is important for RTOs to effectively and accurately extract actual load profiles at the transmission level for a single node with significant BTM solar injection. This paper first illustrates the necessity of disaggregating the daily actual load profile of a single node. Furthermore, by segmenting nodes with selected timeseries features, nodes with significant BTM solar generation are identified. Lastly, a bi-level framework is proposed, comprising reference node disaggregation and DeepFM nodal disaggregation, aimed at disaggregating the nodal load profiles from which system operators require more information. By adopting a hybrid Deep Factorization Machine (DeepFM) model, the model achieve accurate results by extracting both linear and nonlinear relations between nodes in the same region and the zonal load and nodal load profile. To overcome the lack of ground truth, this paper segments the load profile into daytime, nighttime, and zero-crossing points and utilizes the latter two for evaluation purposes. The proposed disaggregation procedure is validated using real world, minute-level, normalized, and anonymized nodal data in the PJM service territory.

42 ENGINEERING↗
DOE OSTI2021

Distribution System State Estimation With High Penetration of Demand Response Enabled Loads

Demand-side operations incentivize utility customers to take part in various grid services. A demand response enabled load (DREL) is a flexible grid asset that schedules electricity consumption in response to a time-of-use (TOU) energy price. Consequently, its energy profile differs from that of a conventional load that is insensitive to price. This difference may cause new challenges for distribution system state estimation (DSSE). It is well known that DSSE often needs to use pseudo-measurements based on historic load profiles to increase system observability. However, historic profiles of conventional loads are not representative of DREL behaviors. Additionally, the inaccuracy impacts DSSE results and other DSSE-dependent operations. In this paper, we propose an online pseudo-measurement generation approach for DSSE with DRELs. We formulate an optimization model to represent DRELs self-adjusting actions. Sampling-based stochastic optimization techniques are proposed to account for uncertainties in DRELs. A set of representative DREL behavior data corresponding to the samples are used to characterize DREL pseudo-measurements. Case studies with modified IEEE 123-bus test system verify the validity of the proposed work.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Real-World Subsynchronous Oscillation Events in Power Grids with High Penetrations of Inverter-Based Resources

This paper presents a survey of real-world sub-synchronous oscillation events associated with inverter-based resources (IBR) over the past decade. The focus is on those oscillations in the subsynchronous frequency range known to be influenced by power grid characteristics, e.g., series compensation or low system strength. A brief overview of the historical events is presented followed by detailed descriptions of a series of events. This paper also examines causation mechanisms and proposes future research directions to meet grid needs worldwide.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Adaptive Deep Reinforcement Learning Algorithm for Distribution System Cyber Attack Defense With High Penetration of DERs

With grid modernization, smart inverters are increasingly used to execute advanced controls for distribution network reliability. However, this also increases the cyber-attack space. Here this paper focuses on the defense approaches to restore the system to normal operation circumstances in the presence of cyber-attacks. A unique deep reinforcement learning (DRL) method is developed to minimize voltage violations and reduce power losses for impacted feeders. The defense problem is reformulated as a Markov decision-making process to dynamically control DERs while minimizing load shedding. This is achieved via an improved soft actor-critic (SAC)-based DRL algorithm, which can govern DER set points and load-shedding scenarios in discrete and continuous modes via the auto-tune entropy and Gaussian policy features. Numerical comparison results on the modified IEEE 123-node system with other control approaches, such as Volt-VAR (VV), Volt-Watt (VW), and model predictive control (MPC) show that the proposed method can eliminate voltage violations and provide feasible control actions that perform complete mitigation of cyber-threats.

24 POWER TRANSMISSION AND DISTRIBUTION↗