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The Ko’olauloa community resilience hub has affordable microgrid options to meet its resilience requirements. Renewables and battery are insufficient without very high cost. Either diesel generation or a hydrogen battery are needed to assure availability and keep cost down.
This project demonstrated the effectiveness of RD2C-developed, resilience-driven control strategies on high-fidelity system models with high penetrations of inverter-based resources, under the impacts of unexpected natural events and cyber-physical attacks. Controller performance and overall system resilience were quantified using data from a comprehensive suite of scenarios, and provided a clear understanding on how to operate and deploy these mitigation strategies.
In December of 2025, the American Society of Civil Engineers updated Florida’s infrastructure grade to a ”C+” – reflecting overall investments in recent years. With the recommendation to further strengthen the electric grid and establish consistent building standards, extreme weather, aging infrastructure, and population in-migration are still compounding Florida’s grid vulnerabilities. Key challenges related to hurricane and other tropical & marine weather events are not unique to Florida. However, the state’s topography, location, demographics, and variation among rural and urban centers for tourism and industry, demonstrate the need for unique approaches to advancing critical infrastructure resilience, particularly for large concentrations of elderly residents or in areas of historic underinvestment. Florida’s grid asset advancements are credited with a reduction in average power outage durations. Yet, improvements since 2021 have set the stage for future resilience activities – as modernization efforts have greatly improved data collection – enabling better understanding of vulnerabilities and trends across counties and localities (particularly in Central Florida).
The goal of this project is to identify and address cybersecurity gaps by developing a multi-layer multi-channel cyber-physical defense and survival mechanism for operating distribution networks with high penetration of solar / inverter-based resource (IBR) / distributed energy resource (DER). The proposed security enhancements are built upon the distributed framework and solution architecture for both information technology (IT) and operational technology (OT) systems. The technical solutions consist of two composite functionalities and six layers: proactive defense (vulnerability assessment, communication protection, and attack detection, as layers 1-3), and adaptive self-healing (attack-resilient control, adaptive recovery, and resilient survival, as layers 4-6). These layers, built on and extended from DHS CISA Cyber Framework, establish an integrated and robust cybersecurity framework for operating large-scale distribution networks.
The Unalakleet Microgrid Optimization Project aimed to strengthen the reliability and efficiency of the isolated electric power system that serves the Tribal community of Unalakleet, Alaska. The community relies entirely on a local wind-diesel microgrid, consisting of four 475 kW diesel generators and six 100 kW wind turbines, to provide electricity to approximately 745 residents and Tribal facilities. Because Unalakleet is not on a road system and is located nearly 400 miles from the nearest major power grid, maintaining a resilient and efficient local energy system is critical. The scope of this project included upgrading a portion of the transmission line between the wind farm and the power plant to increase voltage and reduce line losses, along with modernizing the Supervisory Control and Data Acquisition (SCADA) system to improve monitoring, control, and data management of the power system. These upgrades were designed to increase wind energy utilization, reduce diesel fuel consumption by tens of thousands of gallons annually, and improve overall grid stability. By allowing more of the community’s electricity to be supplied by local renewable wind resources, the project was designed to lower operating costs, reduce dependence on imported fuel, and strengthen the long-term resilience of the power system. These improvements represent an important step toward the community’s long-term energy vision of expanding renewable generation, incorporating energy storage, and eventually achieving “diesels-off” operation, where essential Tribal loads are powered primarily by local renewable resources.
Mount Desert Island (MDI) (≈110 sq mi; ≈10,000 year‑round residents) is located on the eastern coast of Maine. Acadia National Park is located on the island, protecting 47,000+ acres of land which draws millions of visitors annually. The island is divided into four towns (Tremont, Southwest Harbor, Mount Desert, Bar Harbor). The island’s distribution system is supplied by a single 35‑kV mainland feeder and a mix of aging distribution components; outage exposure is not uniform across the island. Residents fear that outage events are starting to exceed resilience expectations. MDI community leaders asked Sandia National Laboratories to characterize energy system vulnerability and to develop conceptual microgrid solutions that improve community resilience while also producing co‑benefits for reliability, access, and sustainable development.
This document provides a concise overview of the analytical capabilities of the TASTI-GRID team. Utilizing additional data and resources, it synthesizes platform insights to present a distinct perspective on investing in grid resilience throughout Mississippi. This document is not intended to influence or direct state- level decisions related to investments in grid resilience and reliability. TASTI-GRID should not be used for real-time monitoring, supporting the ESF #12 emergency response functions, or predicting the restoration times for outages.
This document provides a concise overview of the analytical capabilities of the TASTI-GRID team. Utilizing additional data and resources, it synthesizes platform insights to present a distinct perspective on investing in grid resilience throughout Mississippi. This document is not intended to influence or direct state- level decisions related to investments in grid resilience and reliability. TASTI-GRID should not be used for real-time monitoring, supporting the ESF #12 emergency response functions, or predicting the restoration times for outages.
The Kahikinui Homestead Community in Maui faces energy resilience challenges due to its remote, off-grid location. This report summarizes preliminary technical assistance to design a microgrid for six modular homes using solar PV, wind, and battery storage. Results indicate that a hybrid PV and battery system, supplemented by small wind turbines, offers a cost-effective, resilient solution supporting energy independence and disaster preparedness.
The Kahikinui Homestead Community in Maui faces energy resilience challenges due to its remote, off-grid location. This report summarizes preliminary technical assistance to design a microgrid for six modular homes using solar PV, wind, and battery storage. Findings show hybrid PV and battery systems, supplemented by small wind turbines, offer cost-effective, resilient solutions. This work supports enhanced energy independence and disaster preparedness for the community.
Critical energy infrastructures are increasingly relying on advanced sensing and control technologies for efficient and optimal utilization of flexible energy resources. Algorithmic procedures are needed to ensure that such systems are designed to be resilient to a wide range of cyber-physical adversarial events. This paper provides a robust optimization framework to quantify the largest adversarial perturbation that a system can accommodate without violating pre-specified resiliency metrics. We formulate the maximal adversarial set characterization as a bi-level optimization problem which is solved via Lagrangian relaxations.We illustrate the proposed algorithm on an islanded microgrid example: a modified IEEE 123-node feeder with distributed energy resources. Simulations are carried out to characterize the tolerable adversarial perturbations for varying levels of available flexibility (energy reserves).
The electric grid serves a vital role in the supply chain of nearly all industrial and commercial organizations. A Microgrid infrastructure can provide this service and beneficial non-emergency services including a variety of generation/energy sources. To demonstrate the applicability of microgrids for energy resiliency, we present a microgrid resiliency case study for United Parcel Service’s (UPS) three separate shipping facilities. The goal, to enhance energy security, minimize cost and prevent cascading losses within other related business units. The impacts and consequences of which are quantified in this study using a Mean Failure Cost (MFC) risk assessment measure. MFC accounts for the potential loses to identified stakeholders that may result from a set of identified failures due to a set of identified threats. In this case, our study uses a method we call All Hazards Econometric System (AHES). AHES incorporates the cost of COOP using a strategy that considers the payback period of microgrid installation as compared to other energy delivery strategies.
Neuromorphic event-based networks use asynchronous time-dependent information to extract features from input data that can allow for edge-based distributed applications such as object recognition. The noise resilience properties of such networks, especially in the context of space applications, are yet to be explored. In this paper, we use the hierarchy of time surfaces (HOTS) algorithm, which is one of the neuromorphic algorithms, to understand the least and most resilient modules in a neuromorphic network. The HOTS algorithm relies on the computing of time surfaces that maps the temporal delays between neighboring pixels into normalized features that involve many computations that are also found in other neuromorphic networks such as exponential decays, distance computations, etcetera. We implemented HOTS on a Digilent PYNQ board with a Xilinx Zynq 7020 system on a chip, and we subjected the boards running the HOTS network inference to neutron radiation at the Los Alamos Neutron Science Center. Furthermore, we used simulation models from our previous similar experiments on the event-based sensor to create a neutron induced noise model to quantify the effect of this noise on the overall performance of the network. This experiment provides the preliminary measurements of the reliability of the HOTS algorithm and proposes methods to create a more reliable HOTS architecture in future spacecraft missions.
Climate change affects agricultural productivity worldwide. Increased prices of food commodities are the initial indication of drastic edible yield loss, which is expected to increase further due to global warming. This situation has compelled plant scientists to develop climate change-resilient crops, which can withstand broad-spectrum stresses such as drought, heat, cold, salinity, flood, submergence and pests, thus helping to deliver increased productivity. Genomics appears to be a promising tool for deciphering the stress responsiveness of crop species with adaptation traits or in wild relatives toward identifying underlying genes, alleles or quantitative trait loci. Molecular breeding approaches have proven helpful in enhancing the stress adaptation of crop plants, and recent advances in high-throughput sequencing and phenotyping platforms have transformed molecular breeding to genomics-assisted breeding (GAB). In view of this, the present review elaborates the progress and prospects of GAB for improving climate change resilience in crops, which is likely to play an ever increasing role in the effort to ensure global food security.
National and international security communities (e.g., U.S. Department of Defense) have shown increasing attention for innovating critical infrastructure and installations due to recurring high-profile flooding events in recent years. The standard infrastructure design approach relies on local precipitation-based intensity-duration-frequency (PREC-IDF) curves that do not account for snow process and assume stationary climate, leading to high failure risk and increased maintenance costs. This paper reviews the recently developed next-generation IDF (NG-IDF) curves that explicitly account for the mechanisms of extreme water available for runoff including rainfall, snowmelt, and rain-on-snow under nonstationary climate. The NG-IDF curve is an enhancement to the PREC-IDF curve and provides a consistent design approach across rain- to snow-dominated regions, which can benefit engineers and planners responsible for designing climate-resilient facilities, federal emergency agencies responsible for the flood insurance program, and local jurisdictions responsible for developing design manuals and approving subsequent infrastructure designs. Further, we discuss the recent advances in climate and hydrologic science communities that have not been translated into actional information in the engineering community. To bridge the gap, we advocate that building climate-resilient infrastructure goes beyond the traditional local design scale where engineers rely on recipe-based methods only; the future hydrologic design is a multi-scale problem and requires closer collaboration between climate scientists, hydrologists, and civil engineers.
Wildfires are projected to increase in severity and frequency due to climate change, and the electric grid is both a cause of wildfires and is vulnerable to wildfires. Equipment from the electric grid accounts for 10% of fires burned in California and 3% of fires nationally. Recent catastrophic wildfires, such as the Lahaina Fire, Camp Fire, Marshall Fire, and Smokehouse Creek fires, were all started by electrical equipment and show how devastating these events can be because they threaten lives and structures. Vegetation structure, weather and winds, climate and vegetation response, land use, and human activities all impact the likelihood of severe wildfires. We explore the relationship between the built environment, electric grid infrastructure specifically, and its role in causing catastrophic wildfires to find lessons learned for increasing resilience. Electric grid utility companies currently employ multiple methods to mitigate fire, including (1) early detection, (2) grid hardening, (3) vegetation management, and (4) pre-emptive shutoffs. Utility companies need to consider the conditions for wildfire and the impact that each mitigation strategy has on drivers of wildfire behavior, as a single solution will not be adequate. Utility companies need to work with stakeholders to develop a holistic strategy to reduce ignition likelihood and spread likelihood to reduce catastrophic wildfires and improve resiliency.
In the future, automated demand response mechanisms will be used as spinning reserve. Demand response in the smart grid must be resilient to cyber-physical threats. In this paper, we evaluate the resilience of demand response when used as spinning reserve in the presence of cyber-physical threats. We quantify this evaluation by correlating the stability of the system in the presence of attacks measured by system frequency (Hz) and attack level measured by the amount of load (MW) that responds to the demand response event. The results demonstrate the importance of anticipating the dependability of demand response before it can be relied upon as spinning reserve.
Abstract. Climate resilience is an emerging issue at contaminated sites and hazardous waste sites, since projected climate shifts (e.g., increased/decreased precipitation) and extreme events (e.g., flooding, drought) could affect ongoing remediation or closure strategies. In this study, we develop a reactive transport model (Amanzi) for radionuclides (uranium, tritium, and others) and evaluate how different scenarios under climate change will influence the contaminant plume conditions and groundwater well concentrations. We demonstrate our approach using a two-dimensional (2D) reactive transport model for the Savannah River Site F-Area, including mineral reaction and sorption processes. Different recharge scenarios are considered by perturbing the infiltration rate from the base case as well as considering cap-failure and climate projection scenarios. We also evaluate the uranium and nitrate concentration ratios between scenarios and the base case to isolate the sorption effects with changing recharge rates. The modeling results indicate that the competing effects of dilution and remobilization significantly influence pH, thus changing the sorption of uranium. At the maximum concentration on the breakthrough curve, higher aqueous uranium concentration implies that sorption is reduced with lower pH due to remobilization. To better evaluate the climate change impacts in the future, we develop the workflow to include the downscaled CMIP5 (Coupled Model Intercomparison Project) climate projection data in the reactive transport model and evaluate how residual contamination evolves through 2100 under four climate Representative Concentration Pathway (RCP) scenarios. The integration of climate modeling data and hydrogeochemistry models enables us to quantify the climate change impacts, assess which impacts need to be planned for, and therefore assist climate resiliency efforts and help guide site management.