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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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DOE OSTI2020

Resiliency-Driven Proactive Distribution System Reconfiguration With Synchrophasor Data

Measuring and enabling resiliency of electric distribution systems with increasing weather and cyber events are important. Some of the extreme events (e.g. Earthquakes, Hurricanes) and associated paths are predicted and monitored closely in advance and allow to take pre-event proactive control actions. The Distribution Phasor Measurement Units (D-PMUs) provide new opportunities and supporting such proactive actions. Here, a synchrophasor based resiliency driven pre-event reconfiguration can ensure minimizing impact of the expected event on the power distribution system and associated performance. However, the D-PMUs will also face challenges in terms of data quality similar to the transmission PMUs. The focus of this paper is to provide data mining approaches for anomaly detection in D-PMUs and proposing resiliency-driven pre-event reconfiguration with islanding as a proactive mechanisms to minimize the impact of adverse events on system using processed synchrophasors data. Results are validated for real industrial feeders and test cases with satisfactory response

controlled islanding↗

DOE OSTI2020

Dynamic Microgrids in Resilient Distribution Systems With Reconfigurable Cyber-Physical Networks

Modern distribution systems energized by inverter-interfaced distributed generators (DGs) operate as coupled cyber-physical networks (C/P-networks), where the controllable components in the physical network (P-network) are coordinated through the cyber network (C-network). The concept of dynamic microgrids (MGs) operation has been adopted to enable distribution system autonomous operation with varying electric boundaries. To further enhance system operation resiliency and flexibility, dynamic MGs operation with reconfigurable C/P-networks is discussed in this work. An evaluation framework is proposed to assess the operational feasibility of distribution feeders with multiple inverter-based dynamic MGs and come out with possible restoration solutions in the context of cross-layer C/P-network reconfiguration. Furthermore, distributed controllers are developed for components with different operating characteristics to realize seamless system topology variations and provide coordinated secondary regulation in various operation modes. Furthermore, the proposed evaluation framework along with the developed distributed controller has been validated using a Hardware-in-the-Loop (HIL) real-time CPS testbed.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Dynamic Restoration Strategy for Distribution System Resilience Enhancement

In electric power distribution systems, distributed energy resources (DERs) can act as controllable power sources and support utility operators to minimize power outages after extreme weather events (e.g., hurricane, earthquake, wildfire) and thus help enhance the grid's resilience. Meanwhile, the influences of extreme events and the capabilities of DERs are dynamic and difficult to predict. Hence, the desired distribution system restoration strategy should be able to evolve according to real-time fault/dis-turbance information and the availability of DERs. In this paper, we propose a new dynamic restoration strategy for distribution systems to enhance system resilience against potential hazards. An efficient reconfiguration algorithm is developed to eliminate the use of integer variables to relieve the computational burden. Model predictive control is implemented to adjust the system topology and DER operation set points based on the updated fault information and DER forecasts. The effectiveness of the proposed restoration model in enhancing distribution system resilience is validated through an IEEE 123-bus test system. Simulation results also validate that the proposed restoration model can mitigate the occurrence of unexpected events and the fluctuations of DERs.

distributed energy resources (DERs)↗

DOE OSTI2020

Dynamic Distribution System Restoration Strategy for Resilience Enhancement: Preprint

In electric power distribution systems, distributed energy re-sources (DERs) can act as controllable power sources and support utility operators to minimize power outages after ex-treme weather events (e.g., hurricane, earthquake, wildfire) and thus help enhance the grid's resilience. Meanwhile, the influ-ences of extreme events and the capabilities of DERs are dy-namic and difficult to predict. Hence, the desired distribution system restoration strategy should be able to evolve according to real-time fault/disturbance information and the availabil-ity of DERs. In this paper, we propose a new dynamic distribu-tion system restoration strategy to enhance system resilience against potential hazards. An efficient reconfiguration algo-rithm is developed to eliminate the use of integer variables to relieve the computational burden. Model predictive control is implemented to adjust the system topology and DER opera-tion setpoints based on the updated fault information and DER forecasts. The effectiveness of the proposed restoration model in enhancing distribution system resilience is validated through an IEEE 123-bus test system. Simulation results also validate that the proposed restoration model can mitigate the occurrence of unexpected events and the fluctuations of DERs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Physics-Informed Graph Neural Networks for Collaborative Dynamic Reconfiguration and Voltage Regulation in Unbalanced Distribution Systems

Network reconfiguration has long been employed as a strategic approach to minimize power distribution system losses and effectively regulate voltage levels. Tap-changing voltage regulators are also critical for controlling bus voltages, especially in accommodating the increasing integration of distributed energy resources (DERs) with intermittent outputs. This paper introduces novel methodologies to address the challenges of dynamic reconfiguration and optimal tap setting in unbalanced three-phase distribution systems. We propose an approximated mixed-integer quadratically constrained program (MIQCP) to model dynamic reconfiguration, along with a pioneering formulation for voltage regulator (VR) tap-setting based on Special Ordered Set type 1 (SOS1). To mitigate computational complexity, we propose a physics-informed spatial-temporal graph convolutional network (STGCN) with an integrated link classifier. The proposed approach enables efficient solution generation by fixing specific variables in the MIQCP instance and solving the simplified sub-MIP using an MIP solver. Numerical studies demonstrate the superior prediction accuracy of our STGCN model compared to baseline neural network models, resulting in reduced DER curtailment and voltage deviation with shorter computation time.

dynamic reconfiguration↗

DOE OSTI2025

Machine Learning-Assisted Distribution System Network Reconfiguration Problem

High penetration from volatile renewable energy resources in the grid and the varying nature of loads raise the need for frequent line switching to ensure the efficient operation of electrical distribution networks. Operators must ensure maximum load delivery, reduced losses, and the operation between voltage limits. However, computations to decide the optimal feeder configuration are often computationally expensive and intractable, making it unfavorable for real-time operations. This is mainly due to the existence of binary variables in the network reconfiguration optimization problem. To tackle this issue, we have devised an approach that leverages machine learning techniques to reshape distribution networks featuring multiple substations. This involves predicting the substation responsible for serving each part of the network. Hence, it leaves simple and more tractable Optimal Power Flow problems to be solved. This method can produce accurate results in a significantly faster time, as demonstrated using the IEEE 37-bus distribution feeder. Compared to the traditional optimization-based approaches, a feasible solution is achieved approximately ten times faster for all the tested scenarios.

deep neural networks↗

DOE OSTI2022

Co-optimization of repairs and dynamic network reconfiguration for improved distribution system resilience

In this work, a post-disaster distribution system repair and restoration (DSRR) strategy is proposed to improve distribution system resilience. The DSRR strategy is formulated as a two-stage optimization. The first stage is a comprehensive co-optimization of repair crew scheduling, dynamic network reconfiguration, and distributed energy resource (DER) dispatch based on the forecast load profile. The goal is to minimize the accumulative operating cost caused by the load reduction payment as well as DER operating cost. In particular, since the number of available repair crews is usually smaller than the number of faulted lines after a disaster event, the DSRR strategy determines the optimal scheduling for repairing faulted lines. The second stage is a re-dispatch of the DER power output and load shedding based on the real-time load demand of each bus. The proposed algorithm is validated by case studies of the IEEE 33-bus and 123-bus test systems. We consider those scenarios in which faults occur in multiple heavy-loaded feeders. The simulation results demonstrate that the DSRR strategy effectively coordinate the repair scheduling, network reconfiguration and load shedding to minimize the operating cost.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Seasonal Reconfiguration of Electrical Distribution Systems to Mitigate the Impact of Electric Vehicle Charging

Power grids face challenges in their infrastructure related to the integration of electric vehicles (EV). In particular, EV charging stations may induce instability in key system parameters such as substantial voltage drops, active power losses, and transformer overload due to high demand during charging periods. This article presents a seasonal reconfiguration strategy based on the differential evolution (DE) algorithm, aimed at enhancing system performance under highly variable and stochastic load profiles, particularly those driven by EV charging. The DEA algorithm is hybridized with the find-union (FU) algorithm to efficiently ensure network radiality throughout the optimization process. The proposed methodology is validated on a hybrid distribution system composed of the IEEE 33-bus network, a modified IEEE 13-bus system, and a specific 13-bus microgrid. Results have demonstrated that seasonal reconfiguration significantly reduces active power losses and mitigates transformer loading during critical demand hours, thereby quantifiably increasing the system’s performance. As an integral component of the proposed approach, an analysis of CO2 emissions associated with energy losses is included, allowing a contextualized assessment of the environmental benefits of seasonal reconfiguration in various geographical areas.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Network reconfiguration and distributed energy resource scheduling for improved distribution system resilience

Electric utility companies work to restore as much load as possible after power outages caused by extreme weather events. In this paper, an outage management strategy is proposed to enhance distribution system resilience through network reconfiguration and distributed energy resources (DERs) scheduling. After a line fault, the proposed algorithm can identify radial network topology based on the rank of the incidence matrix. The reconfiguration is implemented by switching tie lines and sectionalizing lines. With the new network topology, an optimal DER scheduling problem is solved to minimize the accumulative cost for dispatchable DER operation and load reduction. Finally, the optimal topology that minimizes the accumulative cost is selected from all radial topologies. The computational workload is relatively low because only linear programming needs to be solved. Using the case studies of the IEEE 69-bus and IEEE 123-bus systems, we consider the worst-case scenarios in which faults occur in the upstream feeder. The simulation results demonstrate that the proposed strategy allows for a relatively high percentage of the load to remain in service after line faults. Furthermore, compared with microgrid-formation approaches, the proposed strategy has advantages when applied to the distribution systems with several normally-open tie lines and low DER penetration.

42 ENGINEERING↗

DOE OSTI2023

Cyber-Physical Reconfiguration for Disaster Resilience of Power Distribution Systems

Cyber-physical distribution systems (CPDS) have emerged from the integration of information technology into distribution systems. While offering substantial benefits, this integration also introduces vulnerabilities. The interaction between cyber networks and distribution systems renders CPDS susceptible to disasters. To ensure critical load supply and system resilience, rapid post-disaster load restoration is required. The paper proposes a critical load restoration (CLR) framework in CPDS using a network reconfiguration approach that exploits the existing post-disaster resources to restore critical loads within the shortest possible time. Using graph theory, the cyber network and distribution system are integrated into a single digraph, minimizing the CLR complexity in CPDS. A cost metric is also defined to satisfy network-specific objectives and constraints. A heuristic is proposed to guide the load restoration process using the cost metric within the integrated digraph. Simulation results confirm the framework's superiority over existing literature, which either overlooks cyber components or prolongs restoration with additional resource deployment.

cyber-physical system↗

DOE OSTI2020

Impacts of Integrating Topology Reconfiguration and Vehicle-to-Grid Technologies on Distribution System Operation

Autonomous electric vehicles (AEVs) provide unique opportunities to cope with the uncertainties of distributed energy generation in distribution networks. But the effects are limited by both inherent radial topology and the behaviors of decentralized AEVs. As such, we investigate the potential benefits of dynamic distribution network reconfiguration (DDNR), taking into account AEVs' spatial-temporal availability and their charging demand. We propose a mixed integer programming model to optimally coordinate the charging/discharging of AEVs with DDNR, while satisfying AEVs' original travel plan. Numerical studies based on a test system overlaying the IEEE 33-node test feeder and Sioux Falls transportation network show that DDNR and AEV complement each other, which improves the operation of the distribution system. We also conduct sensitivity analyses on inputs including renewable fluctuation and AEVs penetration level.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS1995

A decentralized software bus based on IP multicas ting

We describe decentralized reconfigurable implementation of a conference management system based on the low-level Internet Protocol (IP) multicasting protocol. IP multicasting allows low-cost, world-wide, two-way transmission of data between large numbers of conferencing participants through the Multicasting Backbone (MBone). Each conference is structured as a software bus -- a messaging system that provides a run-time interconnection model that acts as a separate agent (i.e., the bus) for routing, queuing, and delivering messages between distributed programs. Unlike the client-server interconnection model, the software bus model provides a level of indirection that enhances the flexibility and reconfigurability of a distributed system. Current software bus implementations like POLYLITH, however, rely on a centralized bus process and point-to-point protocols (i.e., TCP/IP) to route, queue, and deliver messages. We implement a software bus called the MULTIBUS that relies on a separate process only for routing and uses a reliable IP multicasting protocol for delivery of messages. The use of multicasting means that interconnections are independent of IP machine addresses. This approach allows reconfiguration of bus participants during system execution without notifying other participants of new IP addresses. The use of IP multicasting also permits an economy of scale in the number of participants. We describe the MULITIBUS protocol elements and show how our implementation performs better than centralized bus implementations.

Callahan, John R.↗

DOE OSTI2021

Applying Utility's Advanced Grid Technologies to Improve Resiliency of a Critical Load

The US DOE Office of Electricity's Energy Storage Program's joint R&D work with the Cordova Electric Cooperative (CEC) has deployed several advanced grid technologies that are providing benefits today to Cordova Alaska's electricity users. Advanced grid technologies deployed through DoE co-funded R&D include a 1MW Battery Energy Storage System (BESS), and enhanced monitoring including Phasor Measurement Units (PMU's) to help better understand the operational impacts of the added BESS. This paper will highlight key accomplishments to-date in deploying and using advanced grid technologies, and then outline the next phase of work that will use these technologies to implement an operating scheme to reconfigure the utility's distribution system and utility resources including BESS to provide emergency back-up power to a critical load: the Cordova Community Medical Center (CCMC). This paper will include additional insights on the use of utility resources to support critical loads via case study examples by the National Rural Electric Cooperative Assoc. (NRECA).

BESS↗

NASA NTRS1984

Solid-State Crossbar Switch

Combines analog and digital circuits for multiline/multiport switching. Bidirectional solid-state crossbar switch provides interfacing and switching for 16 X24 coordinate ports for 16 parallel signal lines. Intended for rapid manual-controlled or computer controlled reconfiguration of distributed computing systems.

Anderson, T. O.↗

NASA NTRS1985

The embedded operating system project

The design and construction of embedded operating systems for real-time advanced aerospace applications was investigated. The applications require reliable operating system support that must accommodate computer networks. Problems that arise in the construction of such operating systems, reconfiguration, consistency and recovery in a distributed system, and the issues of real-time processing are reported. A thesis that provides theoretical foundations for the use of atomic actions to support fault tolerance and data consistency in real-time object-based system is included. The following items are addressed: (1) atomic actions and fault-tolerance issues; (2) operating system structure; (3) program development; (4) a reliable compiler for path Pascal; and (5) mediators, a mechanism for scheduling distributed system processes.

Campbell, R. H.↗

DOE OSTI2023

A Novel Resilience-Oriented Cellular Grid Formation Approach for Distribution Systems with Behind-the-Meter Distributed Energy Resources

This paper presents a resilience-oriented cellular grid formation approach to achieve scalable and reconfigurable community microgrid operations for distribution systems with behind-the-meter distributed energy resources. A set of interconnected solar photovoltaics, energy storage systems, and load is termed as a cell, implying a subset of the grid that can operate independently using its own resources. Cells are identified such that each cell inherently has sufficient energy resources to black start and can provide a certain level of backup power for its load under the loss of utility power supply. The proposed cell formation approach builds on a unique self-organizing map-based method (SomRes) to quantify a system's resilience. Using SomRes and a non-dominated sorting-based genetic algorithm (NSGA-II), a fast and efficient cell formation algorithm is developed to identify cells in a distribution system that are resilient against extreme events. The efficacy of the proposed approach is demonstrated on a numerical model of a real distribution feeder in Colorado, United States.

distributed energy resource↗

DOE OSTI2021

Switching Device-Cognizant Sequential Distribution System Restoration

This paper presents an optimization framework for sequential reconfiguration using an assortment of switching devices and repair process in distribution system restoration. Compared to existing studies, this paper considers types, capabilities and operational limits of different switching devices, making it applicable in practice. We develop a novel multi-phase method to find the optimal sequential operation of various switching devices and repair faulted areas. We consider circuit breakers, reclosers, sectionalizers, load breaker switches, and fuses. The switching operation problem is decomposed into two mixed-integer linear programming (MILP) subproblems. The first subproblem determines the optimal network topology and estimates the number of steps to reach that topology, while the second subproblem generates a sequence of switching operations to coordinate the switches. For repairing the faults, we design an MILP model that dispatches repair crews to clear faults and replace melted fuses. After clearing a fault, we update the topology of the network by generating a new sequence of switching operations, and the process continues until all faults are cleared. To improve the computational efficiency, a network reduction algorithm is developed to group line sections, such that only switchable sections are present in the reduced network. The proposed method is validated on the IEEE 123-bus and 8500-bus systems.

distribution system↗

DOE OSTI2022

Post-Disaster Microgrid Formation for Enhanced Distribution System Resilience

This paper proposes a deep reinforcement learning (DRL) based approach for post-disaster critical load restoration in active distribution systems to form microgrids through network reconfiguration to minimize critical load curtailments. Distribution networks are represented as graph networks, and optimal network configurations with microgrids are obtained by searching for the optimal spanning forest. The constraints to the research question being explored are the radial topology and power balance. Unlike existing analytical and population-based approaches, which necessitate the repetition of entire analyses and computation for each outage scenario to find the optimal spanning forest, the proposed approach, once properly trained, can quickly determine the optimal, or near-optimal, spanning forest even when outage scenarios change. When multiple lines fail in the system, the proposed approach forms microgrids with distributed energy resources in active distribution systems to reduce critical load curtailment. The proposed DRL-based model learns the action-value function using the REINFORCE algorithm, which is a model-free reinforcement learning technique based on stochastic policy gradients. A case study was conducted on a 33-node distribution test system, demonstrating the effectiveness of the proposed approach for post-disaster critical load restoration.

active distribution systems↗