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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 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Phase identification using statistical analysis

A device includes at least one processor configured to determine, based on (i) a first plurality of time-varying electrical measurements corresponding to a feeder head of a power distribution network having a plurality of phases and (ii) a second plurality of time-varying electrical measurements corresponding to a node in the power distribution network, and using statistical analysis, a predicted phase, from the plurality of phases, that corresponds to the node. The processor may be configured to use ranked correlation coefficients (such as the Kendall rank correlation coefficient) to determine the predicted phase and may use principle component analysis. The processor is also configured to cause at least one device of the power distribution network to modify operation based at least on part on the predicted phase.

Veda, Santosh Sambamoorthy↗
DOE OSTI2025

Strategic Placement and Sizing of Distributed Generation for Resilience Enhancement of Distribution Grids With Microgrid Formation

The rise in frequency and severity of extreme weather events highlights the need for resilient power distribution networks. Microgrids can help improve the resilience of distribution grids by providing continuous power supply using local distribution generation (DG) when the distribution grid fails. In this paper, we propose an approach for optimal placement and sizing of DG to form multiple microgrids throughout the distribution network by restoration actions such as switching operations in case of distribution grid outages caused by extreme weather events. Considering the randomness of damaged distribution lines, the DG placement and sizing problem is formulated as a two-stage stochastic mixed-integer program, with the first stage determining the placement and size of DG, and the second stage focusing on minimizing the amount of load shedding through network restoration and microgrid formations for each scenario. Due to the large number of scenarios, the sample average approximation (SAA) method is employed to solve the problem. The results of case studies on a modified IEEE 33 bus distribution grid demonstrate the effectiveness of the proposed DG placement and sizing strategy in improving the resilience of distribution grids by allowing the formation of multiple microgrids. In addition, the robustness and accuracy of the SAA method are validated through various case studies.

Distributed generation planning↗
DOE OSTI2024

Intelligent, grid-friendly, modular extreme fast charging system with solid-state DC protection

The development of electric vehicle (EV) charging infrastructure is crucial for the widespread adoption of electric transportation. However, implementing such infrastructure is a complex task that requires consideration of factors such as space limitations, adherence to industry standards, grid capacity, and other technical and policy issues. This project seeks to create a framework for the efficient design of compact medium voltage (MV) extreme fast charging (XFC) stations for EVs. The station design involves the use of a solid-state transformer (SST) that connects to the MV distribution network, delivering power to a shared DC bus. This innovative approach eliminates the need for a step-down transformer to provide low-voltage service by connecting directly to the MV distribution network. Eliminating the low-frequency transformer not only reduces the system footprint and losses but also eliminates inrush currents during grid black-start. Additionally, placing power electronics directly on the distribution system allows for high-bandwidth filtering and power factor correction. The inclusion of a shared DC bus enables multiple charging dispensers and DC storage/generation units to connect, forming a DC microgrid. This setup facilitates power sharing with minimal conversion stages. The project showcases a DC distribution network protected by intelligent solid-state (SS) DC circuit breakers (DCCB) capable of isolating the smallest section of the faulted circuit much faster than existing mechanical solutions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Development of Steady-State and Dynamic Mass and Energy Constrained Neural Networks for Distributed Chemical Systems Using Noisy Transient Data

The paper presents the development of algorithms for mass and energy constrained neural network models that can exactly conserve the overall mass and energy of distributed chemical process systems, even though the noisy transient data used for optimal model training violate the same. In contrast to approximately satisfying mass and energy balance constraints of a system by soft penalization of objective function, algorithms have been developed for solving equality-constrained nonlinear optimization problems, thus providing the guarantee of exactly satisfying the system mass and energy conservation laws. For developing dynamic mass-energy constrained network models for distributed systems, hybrid series and parallel dynamic-static neural networks have been leveraged. The developed algorithms for solving both the training and forward problems are validated using both steady-state and dynamic data in the presence of various noise characteristics. The developed data-driven algorithms are flexible to exactly satisfy mass and energy balance constraints for dynamic chemical processes if the system holdup information is available. The proposed network structures and algorithms are applied to the development of data-driven lumped and distributed models of an adiabatic superheater/reheater system, a nonisothermal continuous stirred tank reactor, as well as an electrically heated plug-flow reactor system where one form of energy gets transformed to another. It has been observed that the mass-energy constrained neural networks yield a root mean squared error of <1% with respect to the system truth for the case studies evaluated in this work.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Cooperative Management of a Lithium-Ion Battery Energy Storage Network: A Distributed MPC Approach

This paper presents a study of cooperative power supply and storage for a network of Lithium-ion energy storage systems (LiBESSs). We propose to develop a distributed model predictive control (MPC) approach for two reasons. First, able to account for the practical constraints of a LiBESS, the MPC can enable a constraint-aware operation. Second, a distributed management can cope with a complex network that integrates a large number of LiBESSs over a complex communication topology. With this motivation, we then build a fully distributed MPC algorithm from an optimization perspective, which is based on an extension of the alternating direction method of multipliers (ADMM) method. A simulation example is provided to demonstrate the effectiveness of the proposed algorithm.

Fang, Huazhen↗
DOE OSTI2023

Distributed PV Hosting Capacity Evaluation Considering Equitable PV Accommodation

Distributed photovoltaic (DPV) hosting capacity is widely used by distribution system operators to evaluate how much DPV the distribution networks can accommodate without violating operational constraints. If the proliferation of DPV deployment is appropriately managed, it offers an excellent opportunity to mitigate the energy inequity issue in electric grids. Hosting capacity improvement plans that neglect energy equity can exacerbate unequal DPV access. To consider energy equity in distribution network planning to enhance DPV accommodation, hosting capacity evaluations should be improved to consider equity constraints endogenously. This paper proposes a DPV hosting capacity evaluation method that considers equitable DPV access among sub-regions of a distribution feeder partitioned by socioeconomic indicators, such as income level. The results demonstrate that considering equity in DPV hosting capacity evaluations can provide a valuable reference in the equitable energy transition. This work provides guidance for future distribution network upgrade planning for equitable DPV deployment.

distributed PV↗
DOE OSTI2023

Optimal Siting of EV Fleet Charging Station Considering EV Mobility and Microgrid Formation for Enhanced Grid Resilience

Coordinating infrastructure planning for transportation and the power grid is essential for enhanced reliability and resilience during operation and disaster management. This paper presents a two-stage stochastic model to optimize the location of electric vehicle fleet charging stations (FEVCSs) to enhance the resilience of a distribution network. The first stage of this model deals with the decision to place an FEVCS at the most favorable and optimized location, whereas the second stage aims to minimize the weighted sum of the value of lost load in multiple potential scenarios with different faults. Indeed, the second stage is a joint grid restoration scheme with network reconfiguration and microgrid formation using available distributed generators and fleet electric vehicles. The proposed model is tested on a modified IEEE-33 node distribution network and a four-node transportation network. Case studies demonstrate the effectiveness of the proposed model.

25 ENERGY STORAGE↗
DOE OSTI2021

Improved Control Strategy of Grid Interface for EV High-Power Dynamic Wireless Charging

This paper proposes a control strategy for the grid interface converter in high-power dynamic wireless charging system (DWCS) to address two issues on distribution network integration. Due to the unique pulsating load profile of DWCS, load transient response capability is critical for the grid interface to maintain the dc-bus voltage stable. Besides, the inherent unbalanced situation of distribution network would lead to 2nd-order oscillations on the dc-bus voltage, which would further affect the stable operation of the entire system. In this paper, the DWCS model is developed, and the relationship between the dc-bus voltage and the input/output power is analyzed. Based on the developed model, a control strategy based on direct power control is presented. Both simulation results and hardware-in-the-loop (HIL) results demonstrate that the proposed control strategy not only improves load transient response capability, but also eliminates the 2nd-order oscillations on the dc-bus voltage under imbalanced distribution network conditions.

Zeng, Rong↗
NASA NTRS2008

Distributed Observer Network

NASA s advanced visual simulations are essential for analyses associated with life cycle planning, design, training, testing, operations, and evaluation. Kennedy Space Center, in particular, uses simulations for ground services and space exploration planning in an effort to reduce risk and costs while improving safety and performance. However, it has been difficult to circulate and share the results of simulation tools among the field centers, and distance and travel expenses have made timely collaboration even harder. In response, NASA joined with Valador Inc. to develop the Distributed Observer Network (DON), a collaborative environment that leverages game technology to bring 3-D simulations to conventional desktop and laptop computers. DON enables teams of engineers working on design and operations to view and collaborate on 3-D representations of data generated by authoritative tools. DON takes models and telemetry from these sources and, using commercial game engine technology, displays the simulation results in a 3-D visual environment. Multiple widely dispersed users, working individually or in groups, can view and analyze simulation results on desktop and laptop computers in real time.

Source record↗
DOE OSTI2023

Distributed PV Hosting Capacity Evaluation Considering Equitable PV Accommodation: Preprint

In distribution systems, distributed photovoltaic (DPV) hosting capacity is widely used by system operators to evaluate how much DPV the distribution networks can accommodate without violating operational constraints. With the proliferation of DPV deployment, if managed properly, it offers a great opportunity to mitigate the persistent energy inequity issue in electric grid. Otherwise, energy inequity can be exacerbated with unequal DPV access when hosting capacity improvement plans ignore the equity factor. To consider energy equity in distribution network planning for enhancing DPV accommodation, the hosting capacity evaluation should be improved to consider equity constraints endogenously. In this paper, a DPV hosting capacity evaluation method is proposed considering equitable DPV access among sub-regions of a distribution feeder partitioned by social-economic indicators such as the income levels. The results demonstrate that considering equity in DPV hosting capacity evaluation can provide valuable reference in the equitable energy transition. This work provides a valuable guidance for future distribution network upgrade planning for equitable DPV deployment.

distributed PV↗
DOE OSTI2022

Performance Evaluation of an Advanced Distributed Energy Resource Management Algorithm

This paper presents performance evaluation of a new distributed energy resource management system (DERMS) algorithm via an advanced hardware-in-the-loop (HIL) platform. The HIL platform provides realistic testing in a laboratory environment, including the accurate modeling of sub-transmission and distribution networks, the DERMS software controller, and 84 power hardware solar photovoltaic (PV) inverters, standard communication protocols, and a capacitor bank controller. The DERMS algorithm is also called, Grid-Optimization of Solar (GO-Solar) platform which includes predictive state estimation (PSE) and online multiple objective optimization (OMOO) to dispatch the legacy devices and distributed energy resources (e.g., PV). The voltage regulation performance is evaluated under three scenarios, volt-var smart inverter (baseline), and DERMS control for 100% and 30% of PV. The results show that controlling 30% of PV systems with the GO-Solar platform may provide the best balance of control performance and implementation cost.

distributed energy resource management system (DER↗
DOE OSTI2025

Investigating the Vulnerabilities of the Direct Transfer Trip Scheme for Network Protector Units in the Secondary Networks of Electric Power Distribution Grids

Network protector units (NPUs) are crucial parts of the protection of secondary networks to effectively isolate faults occurring on the primary feeders. When a fault occurs on the primary feeder, there is a path of the fault current going through the service transformers that causes a negative flow of current on the NPU connected to the faulted feeder. Conventionally, NPUs rely on the direction of current with respect to the voltage to detect faults and make a correct trip decision. However, the conventional NPU logic does not allow the reverse power flow caused by distributed energy resources installed on secondary networks. The communication-assisted direct transfer trip logic for NPUs can be used to address this challenge. However, the communication-assisted scheme is exposed to some vulnerabilities arising from the disruption or corruption of the communicated data that can endanger the reliable operation of NPUs. This paper evaluates the impact of the malfunction of the communication system on the operation of communication-assisted NPU logic. To this end, the impact of packet modification and denial-of-service cyberattacks on the communication-assisted scheme are evaluated. The evaluation was performed using a hardware-in-the-loop (HIL) co-simulation testbed that includes both real-time power system and communication network digital simulators. This paper evaluates the impact of the cyberattacks for different fault scenarios and provides a list of recommendations to improve the reliability of communication-assisted NPU protection.

direct transfer trip↗
DOE OSTI2017

Conversion and Validation of Distribution System Model from a QSTS-Based Tool to a Real-Time Dynamic Phasor Simulator

A software process is developed to convert distribution network models from a quasi-static time-series tool (OpenDSS) to a real-time dynamic phasor simulator (ePHASORSIM). The description of this process in this paper would be helpful for researchers who intend to perform similar conversions. The converter could be utilized directly by users of real-time simulators who intend to perform software-in-the-loop or hardware-in-the-loop tests on large distribution test feeders for a range of use cases, including testing functions of advanced distribution management systems against a simulated distribution system. In the future, the developers intend to release the conversion tool as open source to enable use by others.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Optimal Sizing of Movable Energy Resources for Enhanced Resilience in Distribution Systems: A Techno-Economic Analysis

This article introduces a techno-economic analysis aimed at identifying the optimal total size of movable energy resources (MERs) to enhance the resilience of electric power supply. The core focus of this approach is to determine the total size of MERs required within the distribution network to expedite restoration after extreme events. Leveraging distribution line fragility curves, the proposed methodology generates numerous line outage scenarios, with scenario reduction techniques employed to minimize computational burden. For each reduced multiple line outage scenario, a systematic reconfiguration of the distribution network, represented as a graph, is executed using tie-switches within the system. To evaluate each locational combination of MERs for a specific number of these resources, the expected load curtailment (ELC) is calculated by summing the load curtailment within microgrids formed due to multiple line outages. This process is repeated for all possible locational combinations of MERs to determine minimal ELC for each MER total size. For every MER total size, the minimal ELCs are determined. Finally, a techno-economic analysis is performed using power outage cost and investment cost of MERs to pinpoint an optimal total size of MERs for the distribution system. To demonstrate the effectiveness of the proposed approach, case studies are conducted on the 33-node and the modified IEEE 123-node distribution test systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Beyond DERMS: Demonstration of Automated Grid Services, Mode Transition, and Resilience

This report describes the results of more advanced use cases including Ancillary Services, Black-sky-day operation, and Mode Switching from Phase 3 of the “Beyond DERMS” project aiming to build, deploy, and demonstrate a holistic platform that supports the integrated operation and planning of future power distribution networks with bi-directional power flows, many diverse distributed energy resources (DERs), and inverter-based resources. These test results demonstrated how a Beyond DERMS platform can fuse together AMI, SCADA, and DER data to provide a utility with deep insights into distribution network operations and planning, extending the value of DERMS and BTM DER resources.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Machine Learning Based Network Parameter Estimation Using AMI Data

The expansion of distribution power system and the growing penetration of distributed energy resources present new challenges for situational awareness. Calibrating the extended system model with sensor measurements and maintaining the usability is critical for utilities. This paper presents a distribution network parameter estimation (DNPE) approach using machine learning (ML) and metering data that improve the quality of extended distribution power system modeling. The reliability model can improve the ability of endpoint data to be translated into network-level situational awareness in real time and help distribution system operators (DSOs) solve branch flow and voltage problems. In addition, a data analytic and automate processing scheme is proposed to improve the sensor data quality and prevent misleading information. The effectiveness of the proposed method is verified with actual advanced metering infrastructure (AMI) data on a real utility feeder model, while considering the higher penetration of photovoltaic power generation. The test of DNPE and study results are demonstrated in this paper.

Parameter estimation, machine learning, power dist↗
DOE OSTI2018

Deep Convolutional Neural Networks for Distribution System Fault Classification

Faults happen very frequently in distribution systems. Identifying fault types and phases are of critical importance for outage management, fault location, and service restoration. However, this task becomes very challenging due to measurement scarcity in distribution systems. This paper is among the first few that applies deep learning techniques in distribution system fault classification. Specifically, a sequential Convolutional Neural Network(CNN)-based classifier is developed to identify fault buses and phases. The input to the CNN is the steady-state voltage and current data measured at substations. The fault identification is modeled as a multi-label classification problem. Training data under various fault scenarios are obtained in OpenDSS and Gaussian noises are added to mimic measurement errors. A case study in IEEE 13-feeder test system is conducted with single and multiple bus faults scenarios. Numerical results demonstrate the high accuracy and fast computation of the proposed deep CNN-based fault classification.

Tian, Guanyu↗