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DOE OSTI2025

Machine Learning for Scalable and Optimal Load Shedding Under Power System Contingency

Prompt and effective corrective actions in response to unexpected contingencies are crucial for improving power system resilience and preventing cascading blackouts. The optimal load shedding (OLS) accounting for network limits has the potential to address the diverse system-wide impacts of contingency scenarios as compared to traditional local schemes. However, due to the fast cascading propagation of initial contingencies, real-time OLS solutions are challenging to attain in large systems with high computation and communication needs. In this paper, we propose a decentralized design that leverages offline training of a neural network (NN) model for individual load centers to autonomously construct the OLS solutions from locally available measurements. Our learning-for-OLS approach can greatly reduce the computation and communication needs during online emergency responses, thus preventing the cascading propagation of contingencies for enhanced power grid resilience. Numerical studies on both the IEEE 118-bus system and a synthetic Texas 2000-bus system have demonstrated the efficiency and effectiveness of our scalable OLS learning design for timely power system emergency operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

The Statistical Spread of Transmission Outages on a Fast Protection Time Scale Based on Utility Data

When there is a fault, the protection system automatically removes one or more transmission lines on a fast time scale of less than one minute. The outaged lines form a pattern in the transmission network. We extract these patterns from utility outage data, determine some key statistics of these patterns, and then show how to generate new patterns consistent with these statistics. The generated patterns provide a new and easily feasible way to model the overall effect of the protection system at the scale of a large transmission system. This new data-driven generative modeling of protection is expected to contribute to simulations of disturbances in large grids so that they can better quantify the risk of blackouts. Analysis of the pattern sizes suggests an index that describes how much outages spread in the transmission network at the fast timescale.

Transmission↗

DOE OSTI2017

Resilient off-grid microgrids: Capacity planning and N-1 security

Over the past century the electric power industry has evolved to support the delivery of power over long distances with highly interconnected transmission systems. Despite this evolution, some remote communities are not connected to these systems. These communities rely on small, disconnected distribution systems, i.e., microgrids to deliver power. However, as microgrids often are not held to the same reliability standards as transmission grids, remote communities can be at risk for extended blackouts. To address this issue, we develop an optimization model and an algorithm for capacity planning and operations of microgrids that include N-1 security and other practical modeling features like AC power flow physics, component efficiencies and thermal limits. Lastly, we demonstrate the computational effectiveness of our approach on two test systems; a modified version of the IEEE 13 node test feeder and a model of a distribution system in a remote community in Alaska.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Enhancing Active Distribution Systems Resilience by Fully Distributed Self-Healing Strategy

Distributed restoration can exploit smart grid technologies to enhance the resilience of active distribution networks toward a self-healing smart grid. However, the large number of decision variables, especially the binary ones for reconfiguration, bring challenges to developing scalable distributed distribution service restoration (DDSR) strategies. This paper proposes a fully distributed solution procedure based on the alternating direction method of multipliers (ADMM) for mixed-integer programming problems and applies to develop the DDSR framework. The method consists of relax-drive-polish phases, 1) relaxing binary variables, and applying the convex ADMM as a warm start; 2) driving the solutions toward Boolean values through a proximal operator; 3) fixing the obtained binding binary variables and solving the rest of the problem to polish results and achieve a high-quality suboptimal solution. Then, an autonomous clustering strategy and consensus ADMM are integrated with the proposed method to realize the fully distributed cluster-based framework of DDSR. This framework can first determine DER scheduling and switch status for reconfiguration to energize the out-of-service areas from local faults, and then provide the load restoration solution in a distributed manner for total blackouts in large-scale distribution networks. Furthermore, the effectiveness and scalability of the proposed DDSR framework are demonstrated through testing on the IEEE 123-node, IEEE 8500-node, and synthetic 100k-node test feeders.

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DOE OSTI2022

Blackstart and Fault Ride-Through Capability of DFIG-Based Wind Turbines

Grid-forming wind turbines must be able to re-energize transmission branches, motor loads, unbalanced loads, and power electronics loads following a blackout. Further, they must ride through faults that can arise during restoration. To tackle these challenges, this paper sets forth a grid-forming control strategy for wind turbines using doubly-fed induction generators (DFIGs). In particular, a special set of feedforward control loops are engineered to mitigate the impacts of restoration transients as well as symmetrical and asymmetrical faults on the regulation of DFIG rotor currents and stator voltages. This paper also leverages two-axis anti-windup regulators to bound two-axis voltage and current commands within ratings. As a result, the controller performance is demonstrated via a high-fidelity simulation model of a notional wind-dominant grid.

17 WIND ENERGY↗

DOE OSTI2023

Evaluation of Power Transmission Lines Hardening Scenarios Using a Machine Learning Approach

The power transmission infrastructure is vulnerable to extreme weather events, particularly hurricanes and tropical storms. A recent example is the damage caused by Hurricane Maria (H-Maria) in the archipelago of Puerto Rico in September 2017, where major failures in the transmission infrastructure led to a total blackout. Numerous studies have been conducted to examine strategies to strengthen the transmission system, including burying the power lines underground or increasing the frequency of tree trimming. However, few studies focus on the direct hardening of the transmission towers to accomplish an increase in resiliency. This machine learning-based study fills this need by analyzing three direct hardening scenarios and determining the effectiveness of these changes in the context of H-Maria. A methodology for estimating transmission tower damage is presented here in this study as well as an analysis of impact of replacing structures with a high failure rate with more resilient ones. We found the steel self-support-pole to be the best replacement option for the towers with high failure rate. Furthermore, the third hardening scenario, where all wooden poles were replaced, exhibited a maximum reduction in damaged towers in a single line of 66% while lowering the mean number of damaged towers per line by 10%.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

SolarPlus-Optimizer v0.1

With the falling costs of solar arrays and battery storage and reduced reliability of the grid due to natural disasters, small-scale local generation and storage resources are beginning to proliferate. However, very few software options exist for integrated control of building loads, batteries and other distributed energy resources. The available software solutions on the market can force customers to adopt one particular ecosystem of products, thus limiting consumer choice, and are often incapable of operating independently of the grid during blackouts. In this software package, we present the "Solar+ Optimizer" (SPO), a control platform that provides demand flexibility, resiliency and reduced utility bills, built using open-source software. SPO employs Model Predictive Control (MPC) to produce real time optimal control strategies for the building loads and the distributed energy resources on site. SPO is designed to be vendor-agnostic, protocol-independent and resilient to loss of wide-area network connectivity. The software was evaluated in a real convenience store in northern California with on-site solar generation, battery storage and control of HVAC and commercial refrigeration loads. Preliminary tests showed price responsiveness of the building and cost savings of more than 10% in energy costs alone.

Prakash, AnandKrishnan↗

DOE OSTI2023

Machine Learning–Adjusted WRF Forecasts to Support Wind Energy Needs in Black Start Operations

Abstract The push for increased capacity of renewable sources of electricity has led to the growth of wind-power generation, with a need for accurate forecasts of winds at hub height. Forecasts for these levels were uncommon until recently, and that, combined with the nocturnal collapse of the well-mixed boundary layer and daytime growth of the boundary layer through the levels important for energy generation, has contributed to errors in numerical modeling of wind generation resources. The present study explores several machine learning algorithms to both forecast and correct standard WRF Model forecasts of winds and temperature at hub height within wind turbine plants over several different time periods that are critical for the anticipation of potential blackouts and aiding in black start operations on the power grid. It was found that mean square error for day-2 wind forecasts from the WRF Model can be improved by over 90% with the use of a multioutput neural network, and that 60-min forecasts of WRF error, which can then be used to adjust forecasts, can be made with an LSTM with great accuracy. Nowcasting of temperature and wind speed over a 10-min period using an LSTM produced very low error and especially skillful forecasts of maximum and minimum values over the turbine plant area.

17 WIND ENERGY↗

DOE OSTI2018

Topological vulnerability of power grids to disasters: Bounds, adversarial attacks and reinforcement

Natural disasters like hurricanes, floods or earthquakes can damage power grid devices and create cascading blackouts and islands. The nature of failure propagation and extent of damage, among other factors, is dependent on the structural features of the grid, that are distinct from that of random networks. This paper analyzes the structural vulnerability of real power grids to impending disasters and presents intuitive graphical metrics to quantify the extent of topological damage. We develop two improved graph eigen-value based bounds on the grid vulnerability. Further we study adversarial attacks aimed at weakening the grid’s structural robustness and present three combinatorial algorithms to determine the optimal topological attack. Simulations on power grid networks and comparison with existing work show the improvements of the proposed measures and attack schemes.

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DOE OSTI2015

Battery Electric Vehicles can reduce greenhouse has emissions and make renewable energy cheaper in India

India's National Mission on Electric Mobility (NMEM) sets a countrywide goal of deploying 6 to 7 million hybrid and electric vehicles (EVs) by 2020. There are widespread concerns, both within and outside the government, that the Indian grid is not equipped to accommodate additional power demand from battery electric vehicles (BEVs). Such concerns are justified on the grounds of India's notorious power sector problems pertaining to grid instability and chronic blackouts. Studies have claimed that deploying BEVs in India will only

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2015

Scoping Study Investigating PWR Instrumentation during a Severe Accident Scenario

The accidents at the Three Mile Island Unit 2 (TMI-2) and Fukushima Daiichi Units 1, 2, and 3 nuclear power plants demonstrate the critical importance of accurate, relevant, and timely information on the status of reactor systems during a severe accident. These events also highlight the critical importance of understanding and focusing on the key elements of system status information in an environment where operators may be overwhelmed with superfluous and sometimes conflicting data. While progress in these areas has been made since TMI-2, the events at Fukushima suggests that there may still be a potential need to ensure that critical plant information is available to plant operators. Recognizing the significant technical and economic challenges associated with plant modifications, it is important to focus on instrumentation that can address these information critical needs. As part of a program initiated by the Department of Energy, Office of Nuclear Energy (DOE-NE), a scoping effort was initiated to assess critical information needs identified for severe accident management and mitigation in commercial Light Water Reactors (LWRs), to quantify the environment instruments monitoring this data would have to survive, and to identify gaps where predicted environments exceed instrumentation qualification envelop (QE) limits. Results from the Pressurized Water Reactor (PWR) scoping evaluations are documented in this report. The PWR evaluations were limited in this scoping evaluation to quantifying the environmental conditions for an unmitigated Short-Term Station BlackOut (STSBO) sequence in one unit at the Surry nuclear power station. Results were obtained using the MELCOR models developed for the US Nuclear Regulatory Commission (NRC)-sponsored State of the Art Consequence Assessment (SOARCA) program project. Results from this scoping evaluation indicate that some instrumentation identified to provide critical information would be exposed to conditions that significantly exceeded QE limits for extended time periods for the low frequency STSBO sequence evaluated in this study. It is recognized that the core damage frequency (CDF) of the sequence evaluated in this scoping effort would be considerably lower if evaluations considered new FLEX equipment being installed by industry. Nevertheless, because of uncertainties in instrumentation response when exposed to conditions beyond QE limits and alternate challenges associated with different sequences that may impact sensor performance, it is recommended that additional evaluations of instrumentation performance be completed to provide confidence that operators have access to accurate, relevant, and timely information on the status of reactor systems for a broad range of challenges associated with risk important severe accident sequences.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2016

Radiation Hardened Electronics Destined For Severe Nuclear Reactor Environments

Post nuclear accident conditions represent a harsh environment for electronics. The full station blackout experience at Fukushima shows the necessity for emergency sensing capabilities in a radiation-enhanced environment. This NEET (Nuclear Energy Enabling Technologies) research project developed radiation hardened by design (RHBD) electronics using commercially available technology that employs commercial off-the-shelf (COTS) devices and present generation circuit fabrication techniques to improve the total ionizing dose (TID) hardness of electronics. Such technology not only has applicability to severe accident conditions but also to facilities throughout the nuclear fuel cycle in which radiation tolerance is required. For example, with TID tolerance to megarads of dose, electronics could be deployed for long-term monitoring, inspection and decontamination missions. The present work has taken a two-pronged approach, specifically, development of both board and application-specific integrated circuit (ASIC) level RHBD techniques. The former path has focused on TID testing of representative microcontroller ICs with embedded flash (eFlash) memory, as well as standalone flash devices that utilize the same fabrication technologies. The standalone flash devices are less complicated, allowing better understanding of the TID response of the crucial circuits. Our TID experiments utilize biased components that are in-situ tested, and in full operation during irradiation. A potential pitfall in the qualification of memory circuits is the lack of rigorous testing of the possible memory states. For this reason, we employ test patterns that include all ones, all zeros, a checkerboard of zeros and ones, an inverse checkerboard, and random data. With experimental evidence of improved radiation response for unbiased versus biased conditions, a demonstration-level board using the COTS devices was constructed. Through a combination of redundancy and power gating, the demonstration board exhibits radiation resilience to over 200 krad. Furthermore, our ASIC microprocessor using RHBD techniques was shown to be fully functional after an exposure of 2.5 Mrad whereas the COTS microcontroller units failed catastrophically at <100 krad. The methods developed in this work can facilitate the long-term viability of radiation-hard robotic systems, thereby avoiding obsolescence issues. As a case in point, the nuclear industry with its low purchasing power does not drive the semiconductor industry strategic plans, and the rapid advancements in electronics technology can leave legacy systems stranded.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2016

Thermoelectric-Driven Sustainable Sensing and Actuation Systems for Fault-Tolerant Nuclear Incidents

The Fukushima Daiichi nuclear incident in March 2011 represented an unprecedented stress test on the safety and backup systems of a nuclear power plant. The lack of reliable information from key components due to station blackout was a serious setback, leaving sensing, actuation, and reporting systems unable to communicate, and safety was compromised. Although there were several independent backup power sources for required safety function on site, ultimately the batteries were drained and the systems stopped working. If, however, key system components were instrumented with self-powered sensing and actuation packages that could report indefinitely on the status of the system, then critical system information could be obtained while providing core actuation and control during off-normal status for as long as needed. This research project focused on the development of such a self-powered sensing and actuation system. The electrical power is derived from intrinsic heat in the reactor components, which is both reliable and plentiful. The key concept was based around using thermoelectric generators that can be integrated directly onto key nuclear components, including pipes, pump housings, heat exchangers, reactor vessels, and shielding structures, as well as secondary-side components. Thermoelectric generators are solid-state devices capable of converting heat directly into electricity. They are commercially available technology. They are compact, have no moving parts, are silent, and have excellent reliability. The key components to the sensor package include a thermoelectric generator (TEG), microcontroller, signal processing, and a wireless radio package, environmental hardening to survive radiation, flooding, vibration, mechanical shock (explosions), corrosion, and excessive temperature. The energy harvested from the intrinsic heat of reactor components can be then made available to power sensors, provide bi-directional communication, recharge batteries for other safety systems, etc. Such an approach is intrinsically fault tolerant: in the event that system temperatures increase, the amount of available energy will increase, which will make more power available for applications. The system can also be used during normal conditions to provide enhanced monitoring of key system components.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2015

Improving Limit Surface Search Algorithms in RAVEN Using Acceleration Schemes: Level II Milestone

The RAVEN code is becoming a comprehensive tool to perform Probabilistic Risk Assessment (PRA); Uncertainty Quantification (UQ) and Propagation; and Verification and Validation (V&V). The RAVEN code is being developed to support the Risk-Informed Safety Margin Characterization (RISMC) pathway by developing an advanced set of methodologies and algorithms for use in advanced risk analysis. The RISMC approach uses system simulator codes applied to stochastic analysis tools. The fundamental idea behind this coupling approach to perturb (by employing sampling strategies) timing and sequencing of events, internal parameters of the system codes (i.e., uncertain parameters of the physics model) and initial conditions to estimate values ranges and associated probabilities of figures of merit of interest for engineering and safety (e.g. core damage probability, etc.). This approach applied to complex systems such as nuclear power plants requires performing a series of computationally expensive simulation runs. The large computational burden is caused by the large set of (uncertain) parameters characterizing those systems. Consequently, exploring the uncertain/parametric domain, with a good level of confidence, is generally not affordable, considering the limited computational resources that are currently available. In addition, the recent tendency to develop newer tools, characterized by higher accuracy and larger computational resources (if compared with the presently used legacy codes, that have been developed decades ago), has made this issue even more compelling. In order to overcome to these limitations, the strategy for the exploration of the uncertain/parametric space needs to use at best the computational resources focusing the computational effort in those regions of the uncertain/parametric space that are “interesting” (e.g., risk-significant regions of the input space) with respect the targeted Figures Of Merit (FOM): for example, the failure of the system, subject of the analysis. These methodologies are named, in the RAVEN environment, adaptive sampling strategies. These methodologies infer system responses from surrogate models constructed from already existing samples (produced using high fidelity simulations) and suggest the most relevant location (coordinate in the input space) of the next sampling point to be explored in the uncertain/parametric domain. When using those methodologies, it is possible to understand features of the system response with a small number of carefully selected samples. This report focuses on the development and improvement of the limit surface search. The limit surface is an important concept in system reliability analysis. Without going into the details, which will be covered later in the report, the limit surface could be briefly described as an hyper-surface in the system uncertainty/parametric space separating the regions leading to a prescribed system outcome. For example, if the uncertainty/parametric space is the one generated by the reactor power level and the duration of the batteries, the system is a nuclear power plant and the system outcome discriminating variable is the clad failure in a station blackout scenario, then the limit surface separates the combinations of reactor power level and battery duration that lead to clad failure from the ones that do not.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2017

High Fidelity, “Faster than Real-Time” Simulator for Predicting Power System Dynamic Behavior - Final Technical Report

The “High Fidelity, Faster than RealTime Simulator for Predicting Power System Dynamic Behavior” was designed and developed by Illinois Institute of Technology with critical contributions from Electrocon International, Argonne National Laboratory, Alstom Grid and McCoy Energy. Also essential to the project were our two utility partners: Commonwealth Edison and AltaLink. The project was a success due to several major breakthroughs in the area of largescale power system dynamics simulation, including (1) a validated faster than real time simulation of both stable and unstable transient dynamics in a largescale positive sequence transmission grid model, (2) a threephase unbalanced simulation platform for modeling new grid devices, such as independently controlled singlephase static var compensators (SVCs), (3) the world’s first high fidelity threephase unbalanced dynamics and protection simulator based on Electrocon’s CAPE program, and (4) a firstofits kind implementation of a singlephase induction motor model with stall capability. The simulator results will aid power grid operators in their true time of need, when there is a significant risk of cascading outages. The simulator will accelerate performance and enhance accuracy of dynamics simulations, enabling operators to maintain reliability and steer clear of blackouts. In the longterm, the simulator will form the backbone of the newly conceived hybrid realtime protection and control architecture that will coordinate local controls, widearea measurements, widearea controls and advanced realtime prediction capabilities. The nation’s citizens will benefit in several ways, including (1) less down time from power outages due to the fasterthanrealtime simulator’s predictive capability, (2) higher levels of reliability due to the detailed dynamics plus protection simulation capability, and (3) more resiliency due to the three phase unbalanced simulator’s ability to model threephase and single phase networks and devices.

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DOE OSTI2017

Evaluation of the First Generation Dual-purpose Coatings for SiC Cladding

The loss of coolant and station blackout at the Fukushima Daichi nuclear power station highlighted the need to improve the safety margins of current and future Light Water Reactors (LWRs). Accident Tolerant Fuel (ATF) development is designed to find solutions to mitigate or prevent such events through improvement of fuels and fuel cladding. One promising ATF concept is the replacement of zirconium-based alloy cladding with silicon carbide (SiC) fiber-reinforced SiC matrix (SiCf-SiC) composites, due to the inherent accident resistant features of SiC. However, during normal operations, SiC corrodes in reactor coolant potentially at an unacceptable rate. In addition, it is likely for the SiC-based cladding to undergo microcracking under an applied stress, which potentially degrades the hermeticity.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2019

Frontiers in the Economics of Widespread, Long-Duration Power Interruptions: Proceedings from an Expert Workshop

The costs to electric utility customers from short-term, limited geographic-scale power disruptions have been studied for many years. However, there is increasing interest among regulators, policy-makers, and utilities in developing and implementing methods for analyzing interruptions that are of longer duration (days, weeks, or longer) and of a larger geographic scope (entire metropolitan areas or regions which may extend across multiple service territories). A 2017 report from the U.S. National Academies of Sciences, Engineering, and Medicine indicated the importance of “develop[ing] comprehensive studies to assess the value to customers of improved reliability and resilience…during large-area, long-duration blackouts.” As a contribution toward fulfilling this recommendation, the U.S. Department of Energy’s (DOE) Office of Electricity’s Transmission Permitting and Technical Assistance Division, in partnership with Lawrence Berkeley National Laboratory (LBNL), convened a workshop to identify research needs and discuss potential avenues for methodological advances in the economics of widespread, long-duration power interruptions. The workshop was organized around six key themes in the economics of widespread, long-duration interruptions. LBNL conducted a competitive solicitation for white papers on these themes, and the authors selected were leading experts in academia, industry, and the non-profit sector. They were tasked with assessing the state of knowledge on particular topics, and identifying research needs and promising directions to expand it. These topics included definitions of resilience and reliability; regional economic modeling approaches; uncertainty quantification; data challenges and opportunities; contingent valuation survey techniques; and reduced-form analytical tools for assessing the impacts of power interruptions of this scale. The authors of the six white papers presented their findings at the workshop, titled “Frontiers in the Economics of Widespread, Long-Duration Power Interruptions,” and held in Washington, D.C., on March 6, 2018. Each paper was the subject of its own session, with the presentations followed by comments by assigned discussants, and in turn a general discussion of the papers, their findings, and the issues they raised. In addition to the authors, discussants, and LBNL and DOE staff, the workshop participants comprised government, industry, and academic thought leaders from across the country.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2019

Stochastic Optimization for Grid Resilience. FY18 Final Technical Report

The optimal restoration problem lies at the foundation of the evaluation and improvement of resilience in power systems. In this report we present a scalable decomposition algorithm for solving this problem for realistic power systems. The algorithm has been developed in three major research threads, corresponding to the chapters in the present report. Chapter 1 studies mixed-integer programming (MIP) formulations for the requirement that each electrical island that appears after a recon guration must have at least one energized generator. We provide three alternative MIP formulations for this restriction, show their equivalence, and prove that two of them are stronger in terms of their linear programming relaxation than the formulation most commonly used in the power systems literature. Since the time to solve MIPs can vary signi cantly between equivalent formulations, we also present computational experiments on IEEE test systems for the Intentional Controlled Islanding (ICI) and the Black Start Allocation (BSA) problems. We observe that a polynomially separable, exponential in size, strong formulation yields the best performance for the BSA problem and exhibits a comparable performance to a linear in size, weak formulation for the ICI problem. Chapter 2 presents a methodology to generate mixed-integer linear approximations of the AC power ow equations governing lines, transformers (including tap regulators and phase shifters), shunt and series compensators, and consumption responsive to voltage. Our approximations are capable of capturing limitations for power grids under abnormal or uncommon operating conditions where common assumptions used in power ow relaxations may not hold. Particularly, the approximation captures overvoltages under almost no load, which is a major concern when energizing lines after a blackout. The method constructs piecewise linear approximations of the non-linear power ow equations by minimizing the squared approximation error over a subset of the feasible domain. We test the method using a realistic database for the Chilean system (1548 buses, 1114 lines, 564 transformers). The resulting approximations yield root mean square errors in the order of 0.05pu and are successfully applied to solve a transmission switching instance for the Chilean system. Chapter 3 presents our algorithm for optimal restoration. The algorithm works by partitioning the problem into a master problem and a slave problem. The master problem optimizes over energization sequences of generators, buses and branches, for the entire restoration horizon, while respecting the energization requiments studied in Chapter 1. The slave problem veri es that there exist power ow solutions supporting the energization sequences proposed by the master problem, adding cuts to the master if an infeasible sequence is detected.

24 POWER TRANSMISSION AND DISTRIBUTION↗