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DOE OSTI2021

Resilient NdFeB magnet recycling under the impacts of COVID-19 pandemic: Stochastic programming and Benders decomposition

Neodymium-iron-boron (NdFeB) magnets are the most powerful magnets per unit volume sold in the commercial market. Despite the increasing demand for clean energy applications such as electric vehicles and wind turbines, disruptive events including the COVID-19 pandemic have caused significant uncertainties in the supply and demand for NdFeB magnets. Therefore, this study aims to alleviate the risk of supply shortage for NdFeB magnets and the containing critical materials, rare-earth elements (REEs), through the development of a resilient reverse supply chain and logistics network design. We develop scenarios to model the unique impact of the COVID-19 pandemic on the proposed business, incorporating both disruption intensity and recovery rate. We formulate a chance-constrained two-stage stochastic programming model to maximize the profit while guaranteeing the network resiliency against disruption risks. To solve the problem in large-scale instances, we develop an efficient Benders decomposition algorithm that reduces the computational time by 98.5% on average compared to the default CPLEX algorithm. When applied to the United States, the model suggests the optimal facility locations, processing capacities, inventory levels, and material flows for NdFeB magnet recyclers that could meet 99.7% of the demand. To the best of our knowledge, this study is the first to incorporate the impacts of the COVID-19 pandemic to design a resilient NdFeB magnet recycling supply chain and logistics network, leveraging risk-averse stochastic programming.

42 ENGINEERING↗

DOE OSTI2015

Comparing mesophilic and thermophilic anaerobic digestion of chicken manure: Microbial community dynamics and process resilience

Highlights: • Microbial community dynamics and process functional resilience were investigated. • The threshold of TAN in mesophilic reactor was higher than the thermophilic reactor. • The recoverable archaeal community dynamic sustained the process resilience. • Methanosarcina was more sensitive than Methanoculleus on ammonia inhibition. • TAN and FA effects the dynamic of hydrolytic and acidogenic bacteria obviously. - Abstract: While methane fermentation is considered as the most successful bioenergy treatment for chicken manure, the relationship between operational performance and the dynamic transition of archaeal and bacterial communities remains poorly understood. Two continuous stirred-tank reactors were investigated under thermophilic and mesophilic conditions feeding with 10%TS. The tolerance of thermophilic reactor on total ammonia nitrogen (TAN) was found to be 8000 mg/L with free ammonia (FA) 2000 mg/L compared to 16,000 mg/L (FA1500 mg/L) of mesophilic reactor. Biomethane production was 0.29 L/gV S{sub in} in the steady stage and decreased following TAN increase. After serious inhibition, the mesophilic reactor was recovered successfully by dilution and washing stratagem compared to the unrecoverable of thermophilic reactor. The relationship between the microbial community structure, the bioreactor performance and inhibitors such as TAN, FA, and volatile fatty acid was evaluated by canonical correspondence analysis. The performance of methanogenic activity and substrate removal efficiency were changed significantly correlating with the community evenness and phylogenetic structure. The resilient archaeal community was found even after serious inhibition in both reactors. Obvious dynamics of bacterial communities were observed in acidogenic and hydrolytic functional bacteria following TAN variation in the different stages.

09 BIOMASS FUELS↗

DOE OSTI2025

Field-resilient supercurrent diode in a multiferroic Josephson junction

The research on supercurrent diodes has surged rapidly due to their potential applications in electronic circuits at cryogenic temperatures. To unlock this functionality, it is essential to find supercurrent diodes that can work consistently at zero magnetic field and under ubiquitous stray fields generated in electronic circuits. However, a supercurrent diode with robust field tolerance is currently lacking. Here, we demonstrate a field-resilient supercurrent diode by incorporating a 2D multiferroic material into a Josephson junction, and observed a pronounced supercurrent diode effect at zero magnetic field. More importantly, the supercurrent rectification persists over a wide and bipolar magnetic field range beyond industrial standards for field tolerance. By theoretically modeling a multiferroic Josephson junction, we unveil that the interplay between spin-orbit coupling and multiferroicity underlies the unusual field resilience of the observed diode effect. This work introduces multiferroic Josephson junctions as a new field-resilient superconducting device for cryogenic electronics.

Yang, Hung-Yu [Univ. of California, Los Angeles, C↗

DOE OSTI2022

Machine learning prediction of connectivity, biodiversity and resilience in the Coral Triangle

Abstract Even optimistic climate scenarios predict catastrophic consequences for coral reef ecosystems by 2100. Understanding how reef connectivity, biodiversity and resilience are shaped by climate variability would improve chances to establish sustainable management practices. In this regard, ecoregionalization and connectivity are pivotal to designating effective marine protected areas. Here, machine learning algorithms and physical intuition are applied to sea surface temperature anomaly data over a twenty-four-year period to extract ecoregions and assess connectivity and bleaching recovery potential in the Coral Triangle and surrounding oceans. Furthermore, the impacts of the El Niño Southern Oscillation (ENSO) on biodiversity and resilience are quantified. We find that resilience is higher for reefs north of the Equator and that the extraordinary biodiversity of the Coral Triangle is dynamic in time and space, and benefits from ENSO. The large-scale exchange of genetic material is enhanced between the Indian Ocean and the Coral Triangle during La Niña years, and between the Coral Triangle and the central Pacific in neutral conditions. Through machine learning the outstanding biodiversity of the Coral Triangle, its evolution and the increase of species richness are contextualized through geological times, while offering new hope for monitoring its future.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Cyber‐Resilient Distributed Energy Resource Control Algorithms for Smart Distribution Grids

ABSTRACT This paper focuses on the development of cyber‐resilient gradient‐based optimisation algorithms and theoretical proof for grid‐interactive distributed energy resource (DER) control to enable two grid services of virtual power plants (VPPs) dispatch and grid voltage regulation, considering the communication and security impacts. Firstly, the combined DER dispatch and voltage regulation as a real‐time gradient‐based optimisation problem is recapped. Thereafter, we consider a probabilistic traffic model to characterise packet delays and loss in a communication network, and study how the delays enter the process of information exchange among the grid measurement units, local DER controllers and the grid control centre that execute this control algorithm in a coordinated manner. Then, a strategy combining delay thresholds and message update rules is proposed to immunity the asynchrony resulting from the communications traffic and it avoids possible numerical instabilities and sensitivities of the power tracking and voltage regulation capabilities, resulting as cyber‐resilient DER control algorithms. Additionally, their convergence is theoretically proved. Effectiveness of proposed cyber‐resilient algorithms has been validated on the IEEE 37‐bus system in terms of convergence, VPP tracking and voltage regulation performance for smart distribution systems with high penetration of DERs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2026

Stochastic Microgrid Scheduling With Chance‐Constrained Resilience Consideration

Traditionally, it is assumed that microgrids transition seamlessly from grid‐connected operation to islanded mode in the event of sudden main grid outages. In reality, the islanding process, especially unintentional islanding, is rarely seamless. Instead, it is subject to voltage and frequency fluctuations caused by the instantaneous disconnection of the point of common coupling (PCC) switch, variations in loads and renewable generation output and even the protection tripping of distributed energy resources (DERs). To mitigate these fluctuations and facilitate a smooth islanding process, we propose a stochastic microgrid scheduling model that incorporates chance‐constrained resilience measures. Specifically, the resilience measure is defined as the probability of successful islanding (PSI), that is, the probability that a microgrid can mitigate the generation‐demand imbalance caused by the disconnection of the PCC switch, variations in load and renewable generation and DER tripping. This measure is modelled using chance constraints. Unlike existing reliability and resilience indices, which typically neglect the possibility of microgrid/DER failure under extreme events and assume their survival while primarily focussing on reducing impact duration or magnitude, the proposed PSI‐based framework explicitly addresses microgrid and DER survival during the islanding transition. The formulated nonlinear chance constraints are approximated using a multiinterval approach and equivalently represented as a mixed‐integer linear programming (MILP) formulation. Case study results validate the proposed method, showing that the PSI estimation error is reduced to less than 8%, compared to approximately 28% with existing methods. Various sensitivity analyses on the DER tripping rate and PSI settings were performed to validate the robustness of the proposed method. In particular, the necessity of accounting for DER tripping in the PSI calculation was demonstrated.

chance constrained optimization↗

DOE OSTI2023

Operational resilience metrics for power systems with penetration of renewable resources

Abstract Modern power grid is evolving towards carbon neutrality by deploying increasing amount of renewable energy resources. However, the impact of renewable generation on power system planning and operation is not sufficiently investigated, especially the capability of renewable penetrated power systems to resist and recover from major disturbances, which is a critical concern for system operators. Novel metrics and evaluation methodologies are needed to depict systems’ ability in response to events caused by natural disasters, and quantitatively evaluate system performance in various time scales. In this paper, operational resilience metrics are proposed for power systems with penetration of renewable energy resources based on transient stability principles. A systematic methodology is proposed to quantitatively assess the evolution of system performance during various stages of the disaster process. Based on the proposed metrics, a resilience‐oriented disaster management strategy is designed and validated using the modified IEEE 39‐bus test system. The simulation results demonstrate the validity of the proposed metrics and strategy, and show that the system resilience is enhanced during the mitigation of fault conditions.

Gui, Jianzhong↗

DOE OSTI2021

The LSBmax algorithm for boosting resilience of electric grids post (N‐2) contingencies

Abstract A computationally improved algorithm is presented to find the best transmission switching (TS) candidate for boosting resilience of electricity grids subject to ( N ‐2) contingencies. Here, resilience is computed as the reduction in load shed after the above‐mentioned ( N‐ ) contingencies. TS is a planned line outage, and past research shows that changing the transmission system's topology changes the power flow and removes post contingency violations. Finding the best TS candidate in a computationally suitable time for effectively boosting resilience is a challenge. The best TS candidate is found using a novel heuristic method by decreasing the search space based on proximity to the bus with the maximum load shedding (LSB). The LSB algorithm is faster than existing algorithms in the literature; and, it is compatible with both the AC and DC optimal power flow formulations. To validate the authors' claims of speedup and accuracy, two metrics are used to analyze the results from the IEEE 39‐bus and 118‐bus systems. Finally, the inherent parallelism of the LSB algorithm is leveraged on a high‐performance computing platform and applied to the large‐scale Polish 2383‐bus test system to validate scalability in both size and speedup in computation time.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Reliability and resilience of environmental flows under uncertainty: reconsidering water year types and inconsistent flow requirements in California

Abstract Environmental water allocation in California is a complex legal process involving various government agencies and stakeholders. E-flow requirements can be based on annual runoff typologies called water year types (WYTs), which dictate water volume, timing, and duration. In this study, we examined hydropower licensing documents of the major water and power projects in the Central Sierra Nevada to catalog e-flow requirements by WYT. In this study case, we identify how WYT classification systems and categories vary across and within different basins. Additionally, we assessed the impacts of climate change on hydrology, the frequency of WYTs identified, and the reliability and resilience of e-flows using future projections (2031–2060) of 10 Global Circulation Models (GCMs). We then propose a potential adaptation strategy using a 30 year moving percentiles approach to recalculate WYTs. We identified eight WYT classifications systems were identified, and their WYT distributions statistically significantly changes across all GCMs, even though most GCMs indicate no statistically significant change in hydrology. Disparities in future impacts are observed among and within hydropower projects, with some river reaches showing negative impacts on reliability and resilience. The adaptation strategy can generally boost resilience and improve reliability, but simply updating existing WYT thresholds without flexible regulatory frameworks reconsidering WYTs and e-flows thresholds, may not yield substantial improvements. Challenges in managing e-flows in California within regulatory and hydroclimatic contexts are intricate due to the lack of standardized approaches, leading to inconsistencies and potential conflicts among stakeholders, that will likely be exacerbated by climate change. Thus, we emphasize that targeted, site-specific, and adaptive management strategies are crucial, besides the need for a harmonized and consistent approach to defining and applying WYT categories and methods and/or e-flow assessments.

Environmental Sciences & Ecology↗

DOE OSTI2024

Staircase resiliency in a fluctuating cellular array

Inhomogeneous mixing by stationary convective cells set in a fixed array is a particularly simple route to layering. Layered profile structures, or staircases, have been observed in many systems, including drift-wave turbulence in magnetic confinement devices. The simplest type of staircase occurs in passive-scalar advection, due to the existence and interplay of two disparate timescales, the cell turn-over (τ H ), and the cell diffusion (τ D ) time. In this simple system, we study the resiliency of the staircase structure in the presence of global transverse shear and weak vortex scattering. The fixed cellular array is then generalized to a fluctuating vortex array in a series of numerical experiments. The focus is on regimes of low-modest effective Reynolds numbers, as found in magnetic fusion devices. By systematically perturbing the elements of the vortex array, we learn that staircases form and are resilient (although steps become less regular, due to cell mergers) over a broad range of Reynolds numbers. The criteria for resiliency are (a) τ D >>τ H and (b) a sufficiently high profile curvature (κ ≥ 1.5). We learn that scalar concentration travels along regions of shear, thus staircase barriers form first, and scalar concentration "homogenizes" in vortices later. The scattering of vortices induces a lower effective speed of scalar concentration front propagation. The paths are those of the least time. We observe that if background diffusion is kept fixed, the cell geometric properties can be used to derive an approximation for the effective diffusivity of the scalar. Furthermore, the effective diffusivity of the fluctuating vortex array does not deviate significantly from that of the fixed cellular array.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2016

SENSITIVE TO FREEZING2 Aids in Resilience to Salt and Drought in Freezing-Sensitive Tomato

SENSITIVE TO FREEZING2 (SFR2) is crucial for protecting chloroplast membranes following freezing in Arabidopsis (Arabidopsis thaliana). It has been shown that SFR2 homologs are present in all land plants, including freezing-sensitive species, raising the question of SFR2 function beyond freezing tolerance. Similar to freezing, salt and drought can cause dehydration. Thus, it is hypothesized that in freezing-sensitive plants SFR2 may play roles in their resilience to salt or drought. To test this hypothesis, SlSFR2 RNAi lines were generated in the cold/freezing-sensitive species tomato (Solanum lycopersicum [M82 cv]). Hypersensitivity to salt and drought of SlSFR2-RNAi lines was observed. Higher tolerance of wild-type tomatoes was correlated with the production of trigalactosyldiacylglycerol, a product of SFR2 activity. Tomato SFR2 in vitro activity is Mg 2+ -dependent and its optimal pH is 7.5, similar to that of Arabidopsis SFR2, but the specific activity of tomato SFR2 in vitro is almost double that of Arabidopsis SFR2. When salt and drought stress were applied to Arabidopsis, no conditions could be identified at which SFR2 was induced prior to irreversibly impacting plant growth, suggesting that SFR2 protects Arabidopsis primarily against freezing. Discovery of tomato SFR2 function in drought and salt resilience provides further insights into general membrane lipid remodeling-based stress tolerance mechanisms and together with protection against freezing in freezing-resistant plants such as Arabidopsis, it adds lipid remodeling as a possible target for the engineering of abiotic stress-resilient crops.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Optimal Network Reconfiguration and Scheduling With Hardware-in-the-Loop Validation for Improved Microgrid Resilience

With the increased occurrence of various major extreme weather events, power outages and prompt power system restorations have recently drawn more attention to the resilience and recovery of power systems. From the perspective of a more resilient power delivery at the distribution grid, system restoration using network topology reconfiguration together with optimal scheduling of distributed energy resources are adopted in this paper. The proposed optimization model aims at minimizing the total load shedding cost and other operational costs, in which linearized topological constraints borrowed from graph theory and linearized DistFlow models are respectively used to maintain the radial network topology and power flow balance after system contingencies. To demonstrate the applicability of the proposed strategy, a real-world case study of a networked three-microgrid system in Adjuntas, Puerto Rico, is used with the consideration of different independent/interconnected microgrid scenarios, contingencies, and fairness settings. Furthermore, hardware-in-the-loop testing is conducted for the same three-microgrid network, where the closely matched results with the simulated ones have validated the effectiveness of the proposed restoration strategy, which is now ready to move one step forward towards field deployment. Finally, to test the proposed restoration strategy in a larger networked system, the modified IEEE-33 bus test distribution system is considered, and the results show a more resilient power delivery for critical loads under three and four line outages.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2017

Blockchain: A Path to Grid Modernization and Cyber Resiliency

Blockchain may help solve several complex problems related to integrity and trustworthiness of rapid, distributed, complex energy transactions and data exchanges. In a move towards resilience, blockchain commoditizes trust and enables automated smart contracts to support auditable multiparty transactions based on predefined rules between distributed energy providers and customers. Blockchain based smart contracts also help remove the need to interact with third-parties, facilitating the adoption and monetization of distributed energy transactions and exchanges, both energy flows as well as financial transactions. This may help reduce transactive energy costs and increase the security and sustainability of distributed energy resource (DER) integration, helping to remove barriers to a more decentralized and resilient power grid. This paper explores the application of blockchain and smart contracts to improve smart grid cyber resiliency and secure transactive energy applications.

Mylrea, Michael E.↗

DOE OSTI2019

Robust Scheduling of Microgrids With Resiliency Constraints

This paper proposes a novel microgrid scheduling model considering the resiliency requirements. The resiliency of the microgrid is guaranteed by quickly adjusting the output of committed local resources to supply the local demands continuously when the supply of power from the main grid is interrupted. To capture the prevailing uncertainties in renewable generation and demand, a two-stage robust optimization model is formulated to minimize the total operating cost under the worst realization of the modeled uncertainties. The column and constraint generation (CCG) method is used to solve the problem in an iterative manner. The solution of the proposed robust scheduling model guarantees the resiliency of the microgrid by supporting a quick islanding without consumer interruption and ensures the robustness against uncertainties in renewable generation and demand.

Liu, Guodong↗

DOE OSTI2024

Resilience Assessment for Distribution Systems during Hurricanes: A Learning-Based Framework

This paper presents a proactive strategy for hurricane-resilient distribution systems. It proposes a Bayesian Neural Network-based outage prediction model considering various parameters, including electrical components, and weather and environmental factors. Addressing challenges in imbalanced outage datasets, a Bias-Variance Tradeoff method is proposed. A resilience assessment model quantifies resilience indices, providing insights into system weaknesses. The approach identifies weak points and serves as a planning benchmark. Numerical results on the modified IEEE 123-node test system demonstrate effectiveness in realistic hurricane scenarios.

Vahedi, Soroush↗

DOE OSTI2024

Comfort and Safety Metrics to Evaluate Customer Centric Resilience Enhancement Strategies

As the coincident events of extreme weather and major power outages continue to rise there is an increased need to implement resilience solutions. Many of these solutions are customer-centric, requiring the flexibility of demand, and many of the standard economic evaluations of these solutions do not include the benefits of resilience that are not easily quantified and monetized. Indoor air temperature has a large impact on the comfort and safety of building occupants during an extreme weather event. This work proposes metrics that can be used to compare the performance of resilience solutions in the context of comfort and safety.

Cost benefit analysis, Demand Side Management, Pow↗

DOE OSTI2024

Improved microgrid resiliency through distributionally robust optimization under a policy-mode framework

Critical energy infrastructure are constantly under stress due to the ever increasing disruptions caused by wildfires, hurricanes, other weather related extreme events and cyber-attacks. Hence it becomes important to make critical infrastructure resilient to threats from such cyber-physical events. However, such events are hard to predict and numerous in nature and type and it becomes infeasible to make a system resilient to every possible such cyber-physical event. Such an approach can make the system operation overly conservative and impractical to operate. Furthermore, distributions of such events are hard to predict and historical data available on such events can be very sparse, making the problem even harder to solve. To deal with these issues, in this paper we present a policy-mode framework that enumerates and predicts the probability of various cyber-physical events and then a distributionally robust optimization (DRO) formulation that is robust to the sparsity of the available historical data. The proposed algorithm is illustrated on an islanded microgrid example: a modified IEEE 123-node feeder with distributed energy resources (DERs) and energy storage. Simulations are carried to validate the resiliency metrics under the sampled disruption events.

Nazir, Mohammad Nawaf↗

DOE OSTI2023

Enhancing Cyber Resilience of Networked Microgrids using Vertical Federated Reinforcement Learning

This paper presents a novel federated reinforcement learning (Fed-RL) methodology to inject sufficient resiliency into the operations of the network of microgrids. We consider adversarial actions to the voltage and power control loop reference signals at the grid forming (GFM) inverters in the microgrids which are essential to integrate renewable resources. Therefore, we formulate a resilient reinforcement learning training setup that uses these adversarial injections to generate episodic trajectories and train the RL agents to alleviate their impact on performance. To circumvent the concerns about data-sharing and privacy for different owners of the microgrids in the networked setting, we bring in the aspects of the federated operation to propose novel Fed-RL algorithms. As the dynamics of each microgrid are coupled due to electrical interlinks, the conventional federated RL approaches using decoupled independent environments are not applicable, which leads us to propose a multi-agent vertically federated variation of actor-critic algorithms, namely federated soft actor-critic (FedSAC). We have performed numerical simulations on an IEEE 123-bus benchmark test feeder with three microgrids by creating a customized simulation setup by encapsulating the microgrid dynamic simulations in GridLAB-D/HELICS co-simulation platform with the OpenAI Gym environment and validated the proposed resilient and secured learning methodology.

Artificial Intelligence (AI), reinforcement learni↗