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NASA NTRS1994

(abstract) Atmospheric Transmission Model Through Autonomous Visibility Measurement

Performance of deep space optical communications is impacted by atmospheric outages. By monitoring known stellar objects with calibrated telescopes, atmospheric attenuation statistics can be recorded. This paper describes a program to develop an atmospheric transmission model for optical communications by making automous visibility measurements of known stellar objects from three locations in the southwestern US. The data will be correlated and infused with weather data to establish the atmospheric transmission model. This model will be sent in link margin analysis for optical communications channels. The statistics will be updated on a quarterly basis and calibrated using data taken over a long period of time. This calibration is expected to allow extrapolation to other ground-based locations through use of global weather databases.

attenuation model↗

DOE OSTI2024

High-Burnup BWR LOCA Burst Analysis Framework Development and Demonstration

Nuclear power currently contributes approximately 20% of total electricity generation in the United States and more than 10% globally. Given the increasing reliance on nuclear energy to achieve our nation’s goal of reaching net-zero carbon emissions by 2050, there is significant pressure on the existing nuclear industry to extend plant operational licenses and improve efficiency. This is crucial as the existing nuclear fleet serves as a vital bridge until new light water and advanced reactors can be developed and deployed, bolstering the supply of carbon-free energy to meet domestic demands. Operational costs primarily consist of plant operation and maintenance and fuel costs, influenced by materials and reactor core designs. These factors, coupled with heavily subsidized renewable energy markets, create a challenging economic environment for the existing light water reactor fleet, as well as for new build projects. To address these economic challenges, the nuclear industry has developed a strategic blueprint aimed at enhancing nuclear power’s economic sustainability. Past initiatives, such as efforts to eliminate fuel failures by 2010 and reduce operating costs by 30% before 2020, have laid the groundwork. Optimizing core design parameters, including burnup limits and enrichment levels, can lengthen cycles, reduce outages, reduce batch reload batch fractions and spent fuel storage requirements, and lower maintenance and operating expenses, thereby enhancing economic viability. In the United States, boiling water reactors (BWRs) comprise approximately one-third of the fleet, although much of the research and development focus has traditionally been on pressurized water reactors (PWRs). Advances in modeling and simulation, particularly through the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, are crucial to the long-term viability of BWRs, just as they are for PWRs. A key research area of the high burnup/increased enriched fuel initiative is focused on addressing loss-of-coolant-accident (LOCA)-related issues. NEAMS has dedicated significant effort to enhancing tools to better support BWRs, with a current focus on showcasing the BWR framework for high-burnup LOCA analysis. This high-fidelity work will demonstrate a best estimate pin-by-pin high-burnup BWR LOCA analysis to assess full-core cladding rupture behavior. This modeling capability will help with better understanding and realistic evaluation of fuel fragmentation, relocation, and dispersal (FFRD) phenomena at BWRs, which then could be used to prevent FFRD at BWRs without penalizing operational parameters. In addition, the results of this work will help identify strategies to identify additional margins or to potentially limit cladding rupture through core design optimizations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Impact assessment of geomagnetic induced current neutral blocking devices with power electronics sources on distance relay for 230 kV transmission lines

Electrical grids increased the integration of power electronics sources onto transmission lines. In addition, recent reports show a significant rise in geomagnetic storms occurring in 2024. These geomagnetic storms can affect transmission lines by inducing electrical currents within them, potentially causing power outages due to overloaded power transformers. Geomagnetic induced current neutral blocking devices (GIC-NBDs) are capacitors on the ground of wye power transformers, to avoid damage caused by geomagnetic storms. The integration of distance relays and transmission lines with power electronics sources and GIC-NBDs needs to be studied to observe if GIC-NBDs and power electronics sources could adversely affect operation of the electrical grid. In this analysis, the effect of 2 MW power electronics sources and GIC-NBDs on distance relays is assessed for different 230 kV transmission line lengths in radial and nonradial power systems. The simulations measured the apparent impedances, with different electrical faults. Results for a typical 2,650 μF GIC-NBD application were presented on impedance plots, and the distance relay model operations were assessed. In conclusion, the tests for radial and nonradial power systems showed the behavior of the distance elements and source impedance ratios, assessing the effect of power electronics sources and GIC-NBDs on 230 kV transmission lines.

Distance relays↗

DOE OSTI2021

Physics-Based Feature Extraction from Bulk Time-Series PMU Datasets for Event Detection

In this work, two physics-based feature extraction techniques are developed for bulk time-series phasor measurement unit (PMU) datasets collected from the field to train the machine learning model for anomaly detection. Two approaches have been developed to extract useful features for different types of events. An admittance-based feature extraction technique is developed to detect events that involve line outages and system topology variations. The developed algorithm extracts the system equivalent admittance variation. Additionally, Fielder’s Theory is utilized to further reduce the potential computation burden by sectionalizing large-scale grids and datasets into smaller areas. Second, an oscillation-based feature extraction technique is developed to detect low-frequency oscillations in power grids. The dominant oscillation modes in the grids are extracted using energy-sorted Prony analysis. The extracted dominant oscillation modes by the developed work exhibit a high fitting resolution. Finally, the developed techniques have been validated using large-scale and real-world datasets.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Wildfire and power grid nexus in a changing climate

Global wildfire events have had increasingly severe impacts in recent years, particularly in the western USA, driven by extreme fire-weather conditions, fuel accumulation and multiple ignition sources. Wildfires sparked by power lines tend to be larger and more destructive, as they often occur during high winds, which accelerate the spread of fires. Moreover, efforts to contain wildfires frequently result in power outages, causing considerable economic disruption. Here, in this Review, we examine wildfire risks related to power-line-induced ignitions, infrastructure damage, climate-induced environmental impacts, grid operational risks, real-time grid management risks, vegetation management risks, and financial and funding risks in the context of a changing climate and their interdependence with power grid infrastructures. We then explore the resilience of power grids under wildfire threats, looking at risk analysis, prediction and mitigation strategies. The Review also shares practical insights and experiences in the USA to inform researchers, policymakers and industry professionals.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Dispatch analysis of flexible power operation with multi-unit small modular reactors

The relevance of nuclear power plant flexible power operation (FPO) is rising due to increased penetration of variable renewables. Small Modular Reactors (SMRs) such as NuScale are envisaged to have multiple units on one site. This provides opportunities for coordinating FPO between units, but also introduces challenges as common services (e.g., refueling equipment) must be shared between units and fuel loadings may be standardized. It is therefore important to quantify whether FPO with SMRs is economically beneficial. Here, this paper quantifies the economics of FPO on different timescales to analyze refueling outages of such multi-unit SMRs. A 24-h price-taker profit maximization of SMR operation is first solved considering revenues from wholesale power and ancillary service (AS) markets. The results are then used to find the most profitable operation strategy over several years for multiple units at the site accounting for the physics and materials limits on NPP operation, including refueling outages. Results show a small but appreciable participation of nuclear into AS, contributing 4% of revenues. Due to the longer fuel cycle, over a decade of operation the units’ refueling outages drifted by 3 months, ultimately leading to refueling during summer.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

NASA NTRS1988

An assessment of nominal and contingency altitude reboost scenarios during Space Station assembly

The results of an analysis of several reboost scenarios for nominal and contingency SS operations during the Phase I assembly sequence are presented. Space Station program requirements on assembly and operational altitudes are outlined, and essential features of the SS reaction control system are described. A nominal reboost strategy designed to meet the current program requirements is presented. In addition, reboost strategies are developed for several contingency scenarios, including one missed STS mission, and an extended STS outage. It is shown that the time-averaged propellant 'cost' of maintaining altitude is greatest for equivalent continuous-thrusting altitude maintenance; it decreases with increasing reboost/rendezvous time interval to an asymptotic minimum value.

Bilardo, Vincent J., Jr.↗

NASA NTRS2011

An Analysis of Database Replication Technologies with Regard to Deep Space Network Application Requirements

The Deep Space Network (DSN) has three communication facilities which handle telemetry, commands, and other data relating to spacecraft missions. The network requires these three sites to share data with each other and with the Jet Propulsion Laboratory for processing and distribution. Many database management systems have replication capabilities built in, which means that data updates made at one location will be automatically propagated to other locations. This project examines multiple replication solutions, looking for stability, automation, flexibility, performance, and cost. After comparing these features, Oracle Streams is chosen for closer analysis. Two Streams environments are configured - one with a Master/Slave architecture, in which a single server is the source for all data updates, and the second with a Multi-Master architecture, in which updates originating from any of the servers will be propagated to all of the others. These environments are tested for data type support, conflict resolution, performance, changes to the data structure, and behavior during and after network or server outages. Through this experimentation, it is determined which requirements of the DSN can be met by Oracle Streams and which cannot.

Oracle Database File System↗

NASA NTRS2014

Use of Semi-Autonomous Tools for ISS Commanding and Monitoring

As the International Space Station (ISS) has moved into a utilization phase, operations have shifted to become more ground-based with fewer mission control personnel monitoring and commanding multiple ISS systems. This shift to fewer people monitoring more systems has prompted use of semi-autonomous console tools in the ISS Mission Control Center (MCC) to help flight controllers command and monitor the ISS. These console tools perform routine operational procedures while keeping the human operator "in the loop" to monitor and intervene when off-nominal events arise. Two such tools, the Pre-positioned Load (PPL) Loader and Automatic Operators Recorder Manager (AutoORM), are used by the ISS Communications RF Onboard Networks Utilization Specialist (CRONUS) flight control position. CRONUS is responsible for simultaneously commanding and monitoring the ISS Command & Data Handling (C&DH) and Communications and Tracking (C&T) systems. PPL Loader is used to uplink small pieces of frequently changed software data tables, called PPLs, to ISS computers to support different ISS operations. In order to uplink a PPL, a data load command must be built that contains multiple user-input fields. Next, a multiple step commanding and verification procedure must be performed to enable an onboard computer for software uplink, uplink the PPL, verify the PPL has incorporated correctly, and disable the computer for software uplink. PPL Loader provides different levels of automation in both building and uplinking these commands. In its manual mode, PPL Loader automatically builds the PPL data load commands but allows the flight controller to verify and save the commands for future uplink. In its auto mode, PPL Loader automatically builds the PPL data load commands for flight controller verification, but automatically performs the PPL uplink procedure by sending commands and performing verification checks while notifying CRONUS of procedure step completion. If an off-nominal condition occurs during procedure execution, PPL Loader notifies CRONUS through popup messages, allowing CRONUS to examine the situation and choose an option of how PPL loader should proceed with the procedure. The use of PPL Loader to perform frequent, routine PPL uplinks offloads CRONUS to better monitor two ISS systems. It also reduces procedure performance time and decreases risk of command errors. AutoORM identifies ISS communication outage periods and builds commands to lock, playback, and unlock ISS Operations Recorder files. Operation Recorder files are circular buffer files of continually recorded ISS telemetry data. Sections of these files can be locked from further writing, be played back to capture telemetry data that occurred during an ISS loss of signal (LOS) period, and then be unlocked for future recording use. Downlinked Operation Recorder files are used by mission support teams for data analysis, especially if failures occur during LOS. The commands to lock, playback, and unlock Operations Recorder files are encompassed in three different operational procedures and contain multiple user-input fields. AutoORM provides different levels of automation for building and uplinking the commands to lock, playback, and unlock Operations Recorder files. In its automatic mode, AutoORM automatically detects ISS LOS periods, then generates and uplinks the commands to lock, playback, and unlock Operations Recorder files when MCC regains signal with ISS. AutoORM also features semi-autonomous and manual modes which integrate CRONUS more into the command verification and uplink process. AutoORMs ability to automatically detect ISS LOS periods and build the necessary commands to preserve, playback, and release recorded telemetry data greatly offloads CRONUS to perform more high-level cognitive tasks, such as mission planning and anomaly troubleshooting. Additionally, since Operations Recorder commands contain numerical time input fields which are tedious for a human to manually build, AutoORM's ability to automatically build commands reduces operational command errors. PPL Loader and AutoORM demonstrate principles of semi-autonomous operational tools that will benefit future space mission operations. Both tools employ different levels of automation to perform simple and routine procedures, thereby offloading human operators to perform higher-level cognitive tasks. Because both tools provide procedure execution status and highlight off-nominal indications, the flight controller is able to intervene during procedure execution if needed. Semi-autonomous tools and systems that can perform routine procedures, yet keep human operators informed of execution, will be essential in future long-duration missions where the onboard crew will be solely responsible for spacecraft monitoring and control.

Brzezinski, Amy S.↗

DOE OSTI2022

MPC4CLR (Model-Predictive-Control-for-Critical-Load-Restoration-in-Power-Distribution-Systems) [SWR-22-24]

Model predictive control (MPC) is a system or process control technique for making decisions under uncertainty via rolling look-ahead optimizations at each control step where only the current step decisions are applied, and the rest are discarded. In this work, we developed an MPC for a critical load restoration (CLR) in power distribution systems to recover system service (electricity delivery) following an extreme event-triggered substation outage. The method considers the problem of controlling distributed energy resources (DERs) of the distribution system with the objective of achieving maximum load pick up while satisfying distribution network flow and voltage constraints. A linearized optimal power flow (OPF) model is employed to represent the physics of the network. The problem formulation is augmented with a ramping (up) reserve product for the DERs to ensure improved and upward monotonic load restoration as time evolves. Simulation analysis and performance tests are performed using a modified IEEE 13-bus test feeder integrated with wind, solar, microturbine, and energy storage battery. The software is developed using various software packages in Julia and Python. The MPC model is implemented using the JuMP optimization language in Julia while the data analytics including renewable generation and load demand forecasts, running the MPC simulation and visualizations is performed in Python.

Eseye, Abinet Tesfaye↗

DOE OSTI2026

PowerModelsGAT-AI: Physics-Informed Graph Attention for Multi-System Power Flow With Continual Learning

Solving the alternating current power flow equations in real time is essential for secure grid operation, yet classical Newton–Raphson solvers can be slow under stressed conditions. Existing graph neural networks for power flow are typically trained on a single system and often degrade on different systems. We present PowerModelsGAT-AI, a physics-informed graph attention network that predicts bus voltages and generator injections. The model uses bus-type-aware masking to handle different bus types and balances multiple loss terms, including a power-mismatch penalty, using learned weights. We evaluate the model on 14 benchmark systems (4 to 6,470 buses) and train a unified model on 13 of these under contingency conditions with up to two branch outages, achieving an average normalized mean absolute error of 0.89% for voltage magnitudes and R 2 >0.99 for voltage angles. We also show continual learning: when adapting a base model to a new 1,354-bus system, standard fine-tuning causes severe forgetting with error increases exceeding 1000% on base systems, while our experience replay and elastic weight consolidation strategy keeps error increases below 2% and in some cases improves base-system performance. Interpretability analysis shows that learned attention weights correlate with physical branch parameters (susceptance: r=0.38 ; thermal limits: r=0.22 ), and feature importance analysis supports that the model captures established power flow relationships.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Large Scale Bilevel Optimization for N-K SCOPF Using Adversarial Robustness

Ensuring a secure dispatch against multiple simultaneous outages has long been desired to maintain grid security in the presence of severe events, such as extreme weather phenomena. Traditionally denoted as N-k security constrained optimal power flow (N-k SCOPF), this problem is intractable to solve due to its size being combinatorial in the number of simultaneous outages and due to the non-convex nature of the AC network constraints. This hinders the use of N-k SCOPF for operating realistic-scale systems. In this paper, we introduce a methodology to scalably solve an AC-feasible dispatch that improves security over k simultaneous outages. Our methodology poses N-k SCOPF as a bilevel optimization problem and solves it using an adversarial robustness approach. We develop new efficient methods to solve each level of the bilevel optimization by employing knowledge of the physics of the underlying system. This yields significant improvements in speed and convergence that enable us to address the N-k SCOPF problem at scale. We demonstrate the effectiveness of our method by conducting a comprehensive analysis of an N-3 SCOPF for a 500-bus network. Furthermore, we emphasize the ability of our physics-driven techniques to handle larger systems by successfully scaling up to 12,000 buses.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

WISP: Watching grid Infrastructure Stealthily through Proxies (Final Technical Report)

The complex interdependencies of cyber systems (sensors and communications), physical grids and associated electricity market operations make protecting electric power grids a significant challenge. The energy sector is constantly under new, targeted, advanced and dangerous cyber-attacks that have the potential to result in the loss of human life. These threats are further exacerbated by our need to modernize the grid. One focus of cyber security research in smart grids is the securing of the SCADA system through advanced intrusion detection systems (IDS) and bad data detection algorithms in state estimation. These methods either require full knowledge of the system topology and parameters or fail to understand the physical behaviors under attack. WISP (Watching grid Infrastructure Stealthily through Proxies) is designed to provide additional protection to the power grid using only publicly available data. In particular, WISP exploits the spatio-temporal nature of the real time locational marginal prices (LMPs), in conjunction with other information such as bids, weather, outages and load data to analyze anomalous power pricing behaviors and then correlate those observations to localize regions of interest and identify potential cyber events. WISP is non-intrusive as the tool is deployed as a service in the Cloud or on premise and provides reliable information to system operators for enhanced situational awareness, without impeding energy delivery functions. The WISP technology comprises three modules: the data-driven anomaly detection core, the vulnerability and risk analysis and the root cause analysis. The data-driven anomaly detection core performs the tasks of feature selection, anomaly detection and attack region localization. The vulnerability and risk analysis module provides system level information of the vulnerable variables and times, assisting the operators in selecting monitoring and protection nodes. The root cause analysis module takes the detection results and identifies potential operational conditions that contribute to the detected anomalies. In Phase I, we have demonstrated the feasibility and effectiveness of WISP. We developed a realistic electricity market simulator capable of generating normal and attack market data under various operational conditions. We developed a series of cyber-attack detection and analysis algorithms and evaluated them under multiple data sources. Finally, we integrated all modules into an end-to-end software, providing functions for data management, data analytics and visualization. Specifically, we have achieved: (i) real-time data acceptance from external utility interfaces with >99% acceptance rate; (ii) high performance anomaly detection algorithms with >98% detection accuracy and <0.1% false alarm rate; and (iii) ultra-low computing delay <50 milliseconds. Additionally, our team developed algorithms to identify the vulnerable variables in electricity market operations and root cause analysis functions to identify major contributors to the price spikes. These ancillary modules are necessary when deploying WISP in real world industry environment. In Phase II, we have demonstrated the effectiveness of WISP software on realistic largescale power systems. We performed red team testing for the Phase I WISP software and identified software vulnerabilities and implemented corresponding mitigation solutions. We adapted the electricity market simulator for the Texas synthetic 2000-bus system and generated datasets for the false data injection attacks. We created database and visualization interfaces for the Texas system and the ISO New England system. We performed software optimization in terms of operation efficiency, computing speed and detection accuracy. Finally, we tested the software on the Texas system and the ISO New England system and evaluated the detection performance. Overall, we achieved above 89% detection rate, below 3% false alarm rate and below 37 seconds of end-to-end detection delay.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2016

C/NOFS Remote Sensing of Ionospheric Reflectance

Alfvn waves play critical roles in the electrodynamic coupling of plasmas at magnetically conjugate regions in near-Earth space. Associated electric (E*) and magnetic (dec B*) field perturbations sampled by sensors on satellites in low-Earth orbits are generally super positions of incident and reflected waves. However, lack of knowledge about ionospheric reflection coefficients (alpha) hinders understanding of generator outputs and load absorption of Alfvn wave energies. Here we demonstrate a new method for estimating using satellite measurements of ambient E* and dec B* then apply it to a case in which the Communication Navigation Outage Forecasting System (CNOFS) satellite flew conjugate to the field of view of a 630.0 nm all-sky imager at El Leoncito, Argentina, while medium-scale traveling ionosphere disturbances were detected in its field of view. In regions of relatively large amplitudes of E* and B*,calculated values ranged between 0.67 and 0.88. This implies that due to impedance mismatches, the generator ionosphere puts out significantly more electromagnetic energy than the load can absorb. Our analysis also uncovered caveats concerning the methods range of applicability in regions of low E* and B*. The method can be validated in future satellite-based auroral studies where energetic particle precipitation fluxes can be used to make independent estimates of alpha.

Burke, W. J.↗

DOE OSTI2021

AGGREGATE: dAta-driven modelinG preservinG contRollable dEr for outaGe mAnagemenT and rEsiliency (Final Report)

The AGGREGATE project team successfully developed and validated various modules for outage management. Brief summaries of each module are provided to showcase their strength for outage management and restoration for a distribution system with a high penetration of connected distribution energy resources (DERs). In recent years, inverter-based DERs have been widely deployed in distribution system. A most of behind-the-meter (BTM) solar power generation is not visible to the utility. The data-driven DER and load estimation modules are using machine learning (ML) and artificial intelligence (AI) to manage this issue, which provides an opportunity for distribution system operators (DSOs) to operate systems and make decisions in real-time for a distribution system with a high penetration of DERs deployed. Also, the estimated DER and true load can be further leveraged in network aggregation and cold-load pick up estimation for reducing the computing complexity and providing for fast restoration. After load demand and DER power generations have been estimated, the information will support topology and state estimation (SE). The topology estimation module demonstrated the viability of mixed integer linear programming (MILP) formulation to estimate the most likely operational radial topology and outage sections using power flow measurements, historical/estimated load and DERs data and smart meter ping measurements. Formulation includes continuous (power flow, load and DERs data) and binary measurements (smart meter ping measurements) in a single formulation. Errors in continuous data and binary data are modeled as normal distribution and Bernoulli distribution, respectively. In the future distribution grid, the power injection from controllable DERs will be essential for efficient and resilient grid operation. However, determining the optimal DER injections and restoration actions is dependent on knowledge of the system states. State estimation (SE), already the cornerstone of transmission energy management systems, will become commonplace in distribution management systems as more measurements become available from deployment of automated metering infrastructure (AMI). Observability analysis is the first step in SE, as it determines the sufficiency of the available measurements for accurately estimating the current system states. A new type of pseudo-measurement called a Correlational Measurement (CM) is introduced in this module, to enhance the observability of the system to enable more accurate SE. CMs encapsulate knowledge of correlation between demand patterns for similar classes of loads as well as injection patterns for same-technology renewable DERs. During grid contingency scenarios, DERs have been traditionally disconnected, without any fault ride-through capabilities. However, with new regulations and better technology, it is feasible for these resources to contribute to the grid’s restoration after an adverse event and hence enhance resilience. The controllability module proposes a two-step restoration scheme for the power system restoration process by leveraging additional degrees of freedom in power electronics interfaced DERs for mitigating voltage problems. In a resilience mode without the utility system, the distribution grid relies on DERs to serve critical load. In such a severe event with multiple faults on the distribution feeders, actuation of various protective devices (PDs) divides the distribution system into electrical islands. The undetected actuated PDs due to fault current contributions from DERs can delay the restoration process, thereby reducing the system resilience. The Advanced Outage Management (AOM) and the Advanced Feeder Restoration (AFR) modules developed in this project provide improved system resilience with multiple DERs. AOM identifies the faulted sections and actuated PDs in a distribution system with DERs by incorporating smart meter data. The most credible outage scenario including fault locations, PD actuations, and fault indicator (FI) failures is identified by a set of binary integer linear programming incorporating hypotheses. The AFR module serves to restore a distribution system with available energy resources taking into consideration the availability of utility sources and DERs. By partitioning the system into islands, critical load will be served with the available generation resources within islands based on the solution of a MILP. When the utility systems become available, the optimal path will be determined by a spanning tree search algorithm that reconnects these islands back to substations and restores the remaining load. The transmission and distribution (T&D) co-simulation module was used to validate the effect of a control action performed on the distribution side assets as it propagates to the transmission side. This ensures that the control action performed results in a feasible operating point on both the transmission and the distribution system. In addition to validation, the team used the T&D co-simulation module to demonstrate how distribution system assets can be used to mitigate issues on the transmission system. Specifically, the team demonstrated that appropriate switching operations on the distribution side can alleviate the line overload condition on the transmission side without causing new operational constraint violations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Smart-DS: Synthetic Models for Advanced, Realistic Testing: Distribution Systems and Scenarios

The explosion of new ideas for distributed energy resources and advanced distribution grid algorithms faces a critical challenge: how can one effectively develop and evaluate these breakthrough technologies without an adequate set of power systems network, load, and resource data to test against? Today's open test systems, particularly for distribution systems, are tiny and incomplete; and though with enough effort, obtaining utility data under NDA can overcome scale challenges, the results can't be openly shared, preventing fair comparison. The Smart-DS project-led by NREL in partnership with MIT, Universidad Pontificia Comillas, CYME, and EDD-is working to overcome this gap by generating multiple large-scale, open, synthetic, distribution systems that are realistic but not real. These datasets go beyond a single medium voltage feeder (~1000 customers) to cover entire metropolitan areas and their surrounds with up to hundreds of feeders and millions of customers, complete with low voltage customer connections and high voltage sub-transmission. This scale allows adequate testing and analysis of not just local controls and advanced DERs, but also multiple substation interactions, optimized switching, full-scale distribution-OPF, and more. To accompany these datasets, the project is also building a rich set of scenario generation tools that can be used for distribution and transmission systems alike. This includes automated, spatially aware access to world-class, high-resolution solar, wind, and weather data: forward looking generation mixes: and highly configurable DER, load, climate, outage, control-scheme, and other scenarios. We will also highlight DiTTo, an exciting open-source, multi-way distribution dataset transformation tool for many-to-many format translation with scenario, merge/split, and many other manipulation capabilities.

DERs↗

DOE OSTI2021

Application of Mobile Energy Storage for Enhancing Power Grid Resilience: A Review

Natural disasters can lead to large-scale power outages, affecting critical infrastructure and causing social and economic damages. These events are exacerbated by climate change, which increases their frequency and magnitude. Improving power grid resilience can help mitigate the damages caused by these events. Mobile energy storage systems, classified as truck-mounted or towable battery storage systems, have recently been considered to enhance distribution grid resilience by providing localized support to critical loads during an outage. Compared to stationary batteries and other energy storage systems, their mobility provides operational flexibility to support geographically dispersed loads across an outage area. This paper provides a comprehensive and critical review of academic literature on mobile energy storage for power system resilience enhancement. As mobile energy storage is often coupled with mobile emergency generators or electric buses, those technologies are also considered in the review. Allocation of these resources for power grid resilience enhancement requires modeling of both the transportation system constraints and the power grid operational constraints. These aspects are discussed, along with a discussion on the cost–benefit analysis of mobile energy resources. The paper concludes by presenting research gaps, associated challenges, and potential future directions to address these challenges.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Material Selection and Sizing of a Thermoelectric Generator (TEG) for Power Generation in a Self-Powered Heating System

By employing the high temperature heat source to directly generate the electricity needed to power auxiliary systems in a natural gas furnace, boiler or hot water heater, a “self-powered” heating system can provide several benefits. Compared with a traditional furnace, boiler or hot water heater, when overall fuel utilization is kept constant, the self-powered system will have a higher primary energy efficiency, lower operating costs, and dramatically improved building safety and resilience during electric grid outages. Furthermore, a self-powered heating system only has a single utility connection – natural gas, without an electric connection – thus simplifying installation. A thermoelectric generator can be used for direct energy conversion of thermal energy to electricity with no moving parts, which offers a very simple means to provide power for the self-powered heating system, and the operation is without noise or vibration and can thus provide a very long system life. This paper provides an analysis focused on materials selection and the thermal power requirements for a thermoelectric generator (TEG) for use in a self-powered heating system. The dimensionless figure of merit for thermoelectric materials, zT, is used to estimate the optimal efficiency that can be achieved with a TEG to produce the electric power required in such an application. Comparisons of the predicted efficiency, the required heat transfer rate to the TEG and the heat transfer area needed for sustained operation under thermal conditions relevant to the self-powered heating application are made for several potential thermoelectric materials. This analysis was used to develop system requirements for a self-powered hot water heater using a TEG for electric power generation.

Laclair, Tim↗