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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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This presentation considers issues that arise in developing a roadmap toward sustainable and secure energy for US Army and other agency installations in Europe and elsewhere. Issues include scope, baseline, conservation and efficiency, energy balance, grid integration, resilience and security, procurement and financing, performance-period issues and end-of-performance-period issues.
Puerto Rico has committed to meeting its electricity needs with 100% renewable energy by 2050, along with realizing interim goals of 40% by 2025, 60% by 2040, the phaseout of coal-fired generation by 2028, and a 30% improvement in energy efficiency by 2040 as established in Puerto Rico Energy Public Policy Act (Act 17). To meet these goals and support widespread end-use electrification, Puerto Rico is exploring renewable energy and innovative technologies for energy storage, distributed generation, distribution control, electric vehicles, and energy efficient and responsive loads that can be deployed in each of Puerto Rico's cities and communities. Launched in February 2022, a two-year study entitled Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy (PR100) will perform a comprehensive analysis of stakeholder-driven pathways to Puerto Rico's clean energy future. This fact sheet describes the five activities this robust and objective energy analysis entails.
Solar photovoltaics (PV) will play a crucial role in decarbonizing the electrical grid and limit the effects of detrimental climate change. Long lifetimes of PV installations are a win-win situation, as it not only reduces carbon emissions directly through avoidance of fossil fuel emissions but also indirectly, as it reduces the demand of needed materials and potentially recycling. However, during their decades-long lifetime expectations installations are also more commonly exposed to extreme weather events. Building durable solar system to withstand extreme weather events is essential in the electrification of the economy and will save lives, particularly when they power critical infrastructure such as hospitals. As more PV is installed in regions prone to extreme weather events, high-quality materials, installation and monitoring practices can mitigate risk. Evaluating resilience requires combined computational, analytical, and experimental capabilities that are best leveraged by teams working across multiple disciplines.
Rapidly increasing usage of nuclear-integrated energy units has created new challenges in terms of cybersecurity. This paper discusses the potential cyberthreat challenges and cyber risks associated with the widespread adoption of these units, and the role of artificial intelligence (AI) and machine learning (ML) techniques in enhancing the security and resilience of these systems.
NREL's resilience assessment methodology uses a data-driven and stakeholder approach to combine quantitative and qualitative data/information. The reason for this approach is to understand where there is the potential to create unintended consequences and expose additional vulnerabilities. This presentation will provide an overview of the methodology used across the nation and globe and highlight some key lessons.
For decades, transformer flexibility of operation has been limited to adjustable voltage ratio and, in some cases, several ratings with staged cooling. Transformers reconnectable to multiple voltage ratings have been primarily applied as emergency replacements. Other versions of “flexible transformers” were developed for continuous, long-term operation at specific sites, with application limited to large utilities. A new technology that supports today’s power systems adaptability requirements has been developed to provide not only voltage and power rating flexibility, but also an adjustable short-circuit impedance. This last, unique feature allows for a closer match to actual system operational requirements and could be used in the future to enhance resiliency and improve dynamic control of electrical grids. While the solution’s fundamentals have already been introduced in several technical forums, the intention of this article is to focus on the potential benefits of this new type of transformer and share the deployment experience of a full-scale prototype that has been in operation in a major U.S. utility since 2021
This paper proposes a learning-based building flexibility estimation and control framework to improve system economics and resilience. A data-driven building load flexibility model consisting of weather forecasting and estimating load consumption is proposed to quantify building heating, ventilation, and air conditioning (HVAC) load flexibility. A reinforcement learning-based microgrid controller is proposed to dispatch distributed generators, distributed energy resources, and build HVAC loads while taking flexibility information as one of the inputs. Simulation analysis is conducted on the model of a real microgrid in California. The effectiveness of the proposed learning-based building flexibility estimation and control in reducing microgrid energy costs and improving the sustainability of critical loads is demonstrated.
Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy Study (PR100) provided a comprehensive analysis of pathways for Puerto Rico to achieve its goal of 100% renewable energy by 2050, based on extensive stakeholder input. The study illuminates immediate and longer-term investments needed to achieve reliability while pursuing Puerto Rico's energy goals and addressing critical energy needs. Expected benefits of these investments include improvements in safety, security, health, and economic opportunity.
This paper presents a novel model-free multi-agent Reinforcement Learning (RL) control method to enhance the resilience of community energy systems in island mode, which coordinates multiple objectives without the necessity of identifying system models that require expert knowledge. Specifically, a community-level coordinator agent is designed to allocate renewable energy resources among different buildings, and multiple building-level agents are developed to optimize load schedules based on limited energy resources and requirements of building loads and occupants’ comfort. In a two-day evaluation, our RL approach demonstrated a similar performance against MPC without requiring system models and formulation of optimization problems as required in MPC.
Traditionally, buildings are dependent on utility infrastructure, and when a grid failure happens, end users rely on the closest source of energy storage to sustain operation until power is restored. For buildings, that typically means using an electric generator. This electric generator either uses on-site energy storage such as fossil fuels in a tank or a gas connection which is, in turn, tied to gas wells—also a form of energy storage. Generators are popular for their ease of implementation and low capital costs; however, they have limited value outside of disruptions, and they are a source of scope 1 emissions, or direct greenhouse gas emissions from sources controlled by the building owner. In contrast, some power generation and storage systems, such as photovoltaic (PV) panels and battery energy storage systems (BESS), can serve the same purpose during grid disruptions while presenting advantages outside of power failure. This paper explores methods for storing and converting energy on-site to increase building resiliency, focusing on solutions that minimize scope 1 emissions. We analyze the cost and carbon impacts of energy efficiency measures, PV arrays, and BESS, with and without generators, in a simulation test case. We find significant benefits can be achieved both during and outside of power failure events when designing systems that integrate the on-demand capability of generators, the low carbon energy supplied by PV, and the storage capabilities of BESS. Specifically, adding even minimal BESS and PV can result in downsizing the generator, increasing generator efficiency and requiring less fuel.
Final Technical Report on Seminole Tribe of Florida Rural Reservation Resiliency Initiative project on the Big Cypress reservation.
Microgrids can be operated in a utility-connected mode with the neighboring distribution system or in an islanded mode in separation with the distribution system. As the major disasters occur more and more often, islanded microgrid is proposed as a possible solution to serve essential loads until the utility service is restored. This paper evaluates the feasibility of using microgrids as a resiliency resource by a field test. A control scheme, integrating droop control and feedback control, is applied on a modified IEEE 13-node test feeders. The simulation results validate the proposed control scheme and the transient stability
The worldwide energy demand in electric vehicles and the increasing global temperature have called for development of high-energy and long-life lithium-ion batteries (LIBs) with improved high temperature operational resiliency. However, current attention has been mostly focused on cycling aging at elevated temperature, leaving considerable gaps of knowledge in the failure mechanism and practical control of abusive calendar aging and thermal runaway that are highly related to the eventual operational lifetime and safety performance of LIBs. Herein, using a combination of various in situ synchrotron X-ray and electron microscopy techniques, we report a multiscale understanding of surface structure effects involved in regulating the high temperature operational tolerance of polycrystalline Ni-rich layered cathodes. Our results collectively show that an ultraconformal poly (3,4-ethylenedioxythiophene) coating can effectively prevent LiNi 0.8 Co 0.1 Mn 0.1 O 2 cathode from undesired phase transformation and transition metal dissolution on the surface, atomic displacement and dislocations within primary particles, intergranular cracking along the grain boundaries within secondary particles, and intensive bulk oxygen release during high state-of-charge and high temperature aging. As a result, the present work highlights the essential role of surface structure controls in overcoming the multiscale degradation pathways of high-energy battery materials at extreme temperature.
Abstract With the onset and rapid growth of smart manufacturing, there is a constant increase in the demand for automation technologies to enhance productivity while providing uninterrupted, cost‐efficient, and resilient machining. Traditional manufacturing systems, however, suffer from several losses due to machine faults and degradation. Specifically, tool wear directly impacts the precision and quality of the milled parts, which causes an increase in the scrap production. Hence, more attempts are required to meet the desired quota of successful parts, which in turn results in wasted material, longer delays, further tool degradation, and higher energy, machining, and labor costs. As such, this paper develops a multi‐objective optimization framework to generate the optimal control set points (e.g., feed rate and width of cut) that minimize the total cost of machining operations resulting from multiple contradictory cost functions (e.g., material, energy, tardiness, machining, labor, and tool) in the presence of tool wear. Notably, we estimate the total expected cost in dollars, which provides automatic and intuitive weighting in this multi‐objective formulation. The optimization framework is tested on a high‐fidelity face milling model that has been validated on real data from industry. Results show significant dollar savings of up to as compared to the default control scheme.
Here, a systems governance approach emphasizes a holistic perspective that identifies and navigates the interdependencies and conflicts between security and operational needs. Governance is defined as a collection of metasystems that provide the necessary constraints and processes to support, steer, adapt, transform, and sustain a system (Keating et al. 2022). Utilizing the Cynefin framework, which distinguishes between simple, complicated, complex, and chaotic environments (Snowden and Boone 2007), the article highlights the challenges faced by nuclear power plants in predatory contexts and the importance of integrating security objectives into governance frameworks. By incorporating security as a fundamental aspect of governance, the article underscores its significance for persistence, adaptation, and transformation in the face of uncertainty. Additionally, it introduces key heuristics of systems security, such as the importance of context, knowledge‐based decision‐making, and organization‐specific sociological factors (Williams and Caskey 2024). Ultimately, this work provides valuable insights into enhancing resilient operations in complex environments by reinforcing the connection between effective governance and security in systems engineering.
Optimal mitigation planning for highly disruptive contingencies to a transmission-level power system requires optimization with dynamic power system constraints, due to the key role of dynamics in system stability to major perturbations. We formulate a generalized disjunctive program to determine optimal grid component hardening choices for protecting against major failures, with differential algebraic constraints representing system dynamics (specifically, differential equations representing generator and load behavior and algebraic equations representing instantaneous power balance over the transmission system). We optionally allow stochastic optimal pre-positioning across all considered failure scenarios, and optimal emergency control within each scenario. This novel formulation allows, for the first time, analyzing the resilience interdependencies of mitigation planning, preventive control, and emergency control. Using all three strategies in concert is particularly effective at maintaining robust power system operation under severe contingencies, as we demonstrate on the western system coordinating council 9-bus test system using synthetic multi-device outage scenarios.
Electric utility companies work to restore as much load as possible after power outages caused by extreme weather events. In this paper, an outage management strategy is proposed to enhance distribution system resilience through network reconfiguration and distributed energy resources (DERs) scheduling. After a line fault, the proposed algorithm can identify radial network topology based on the rank of the incidence matrix. The reconfiguration is implemented by switching tie lines and sectionalizing lines. With the new network topology, an optimal DER scheduling problem is solved to minimize the accumulative cost for dispatchable DER operation and load reduction. Finally, the optimal topology that minimizes the accumulative cost is selected from all radial topologies. The computational workload is relatively low because only linear programming needs to be solved. Using the case studies of the IEEE 69-bus and IEEE 123-bus systems, we consider the worst-case scenarios in which faults occur in the upstream feeder. The simulation results demonstrate that the proposed strategy allows for a relatively high percentage of the load to remain in service after line faults. Furthermore, compared with microgrid-formation approaches, the proposed strategy has advantages when applied to the distribution systems with several normally-open tie lines and low DER penetration.
This paper presents a multidisciplinary analysis of the Fukushima Dai-ichi Nuclear Power Plant accident. Along with the latest observations and simulation studies, we synthesize the time-series and event progressions during the accident across multiple disciplines, including in-plant physics and engineering systems, operators’ actions, emergency responses, meteorology, radionuclide release and transport, land contamination, and health impacts. We identify three key factors that exacerbated the consequences of the accident: (1) the failure of Unit 2 containment venting, (2) the insufficient integration of radiation measurements and meteorology data in the evacuation strategy, and (3) the limited risk assessment and emergency preparedness. We conclude with new research and development directions to improve the resilience of nuclear energy systems and communities, including (1) meteorology-informed proactive venting, (2) machine learning-enabled adaptive evacuation zones, and (3) comprehensive risk-informed emergency planning while leveraging the experience from responses to other disasters.