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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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At least 325 records · Page 18

Improving power grid transient stability and transfer capability using HVDC emergency power controls

In the future, with high levels of renewable energy sources penetration, the transmission corridors will have to be flexible to operate under faster transients and variable power flows. Fast power ramping and overloading capabilities of HVDC system can be utilized to ensure transient stability of the interconnected power grid. In this paper, HVDC auxiliary controllers have been studied that can provide the power grid with additional synchronizing torques through fast DC power changes in response to arising system conditions. The effectiveness of the auxiliary controllers in improving transient stability is demonstrated by performing dynamic simulations on the detailed model of the Western Interconnection system (WECC).

Sundaresh, Lakshmi↗

Dynamic Economic Dispatch Considering Transmission–Distribution Coordination and Automatic Regulation Effect

With the rapid increase of renewable energy sources, the variability and uncertainty in power systems are significantly increased, and the power system operation is facing more and more challenges. In the traditional dynamic economic dispatch (DED) of transmission systems, the frequency and voltage regulation effects are not actively utilized. In addition, the transmission systems and distribution systems are optimized separately; the transmission–distribution coordination effect is not considered. In this article, a new DED is proposed. The optimal scheduling results are obtained by making a balance between operating cost and reliability cost. Both the automatic regulation effect and transmission–distribution coordination effect are explored and exploited in terms of the equivalent reserve, which can reduce the reserve provided by generating units, and can further enhance the security and economics of power system operation. The validity and effectiveness of the proposed model are illustrated by case studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Enhancing ACPF Analysis: Integrating Newton-Raphson Method with Gradient Descent and Computational Graphs

This paper presents a new method for enhancing Alternating Current Power Flow (ACPF) analysis. The method integrates the Newton-Raphson (NR) method with Enhanced-Gradient Descent (GD) and computational graphs. The integration of renewable energy sources in power systems introduces variability and unpredictability, and this method addresses these challenges. It leverages the robustness of NR for accurate approximations and the flexibility of GD for handling variable conditions, all without requiring Jacobian matrix inversion. Furthermore, computational graphs provide a structured and visual framework that simplifies and systematizes the application of these methods. The goal of this fusion is to overcome the limitations of traditional ACPF methods and improve the resilience, adaptability, and efficiency of modern power grid analyses. We validate the effectiveness of our advanced algorithm through comprehensive testing on established IEEE benchmark systems. Furthermore, our findings demonstrate that our approach not only speeds up the convergence process but also ensures consistent performance across diverse system states, representing a significant advancement in power flow computation.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Uncertainty-Informed Operation Coordination in a Water-Energy Nexus

The widespread deployment of smart heterogeneous technologies and the growing complexity in our modern society calls for effective coordination of the interdependent lifeline networks. In particular, operation coordination of electric power and water infrastructures is urgently needed as the water system is one of the most energy-intensive networks, an interruption in which may quickly evolve into a dramatic societal concern. This paper develops a novel analytic for uncertainty-aware day-ahead operation optimization of the interconnected power and water systems (PaWS). Joint probabilistic constraint (JPC) programming is employed to capture the uncertainties in wind resources and water demand forecasts. The proposed integrated stochastic model is presented as a non-linear non-convex optimization problem, where the non-linear hydraulic constraints in the water network are linearized using piece-wise linearization technique, and the non-convexity is efficiently tackled with a solution methodology to convert the proposed model with JPCs to a tractable mixed-integer linear programming (MILP) formulation that can be quickly solved to optimality. Here, the suggested framework is applied to a 15-node commercial-scale water network jointly operated with a power transmission system using a modified IEEE 57-bus test system. The numerical results demonstrate the of the proposed stochastic framework, resulting in cost reduction (13% on average when compared to the traditional setting) and energy saving of the integrated model under different realizations of uncertain renewable energy sources (RESs) and water demand scenarios. Additionally, the scalability of the proposed model is tested on a modified IEEE 118-bus test system connected to five water networks.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Formulation of Three-Phase State Estimation Problem Using a Virtual Reference

State Estimation (SE) is the backbone of modern Energy Management System (EMS) due its capability of processing real time measurements and provide reliable information to system operators. Since its introduction to power systems in the 70’s, SE has been widely used in transmission networks. Distribution grids on the other hand lack sufficient number of real time measurements, and for that reason SE has not been widely implemented on these systems. The recent increase in the number of renewable energy sources connected to the grid at lower voltage levels, the advent of Distribution Automation (DA) and Smart Grids necessitate closer monitoring of distribution networks and thus forcing utilities to upgrade their operations and deploy Advanced Distribution Management Sytems (ADMS). Therefore, Distribution System State Estimation (DSE) is paramount to provide real time monitoring of active distribution grids. This papers investigates the formulation of the three-phase distribution state estimation where in the most general case, none of the system buses may have balanced three phase voltages. In such a case, the choice and definition of the slack bus need to be revisited. Most of the existing work arbitrarily assign the root bus of the distribution feeder as the balanced three-phase reference which may not be realistic for today’s active distribution systems. This paper addresses this shortcoming of the existing SE formulations by introducing a virtual reference bus. Here, the proposed approach is effectively validated by simulations carried out on distribution test systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Distributional Deep Reinforcement Learning-Based Emergency Frequency Control

Emergency frequency control is one of the most critical approaches to maintain power system stability after major disturbances. With the increasing number of grid-connected renewable energy sources, existing model-based methods of frequency control are facing up with challenges of computational speed and scalability for large-scale systems. In this paper, the emergency frequency control problem is formulated as a Markov Decision Process (MDP) and solved through a novel Distributional Deep Reinforcement Learning (DDRL) method, namely the distributional soft actor critic (DSAC) method. Compared with other RL methods that only estimate the mean value, the proposed DSAC model estimates the distribution of value function over returns. This advancement can lead to more insights and knowledge for the agent, with the benefit of a much faster and more stable learning process, and the improved frequency control performance. Here, the simulation results on IEEE 39-bus and IEEE 118-bus systems demonstrate the effectiveness and robustness of proposed models, as well as the advantage compared to other state-of-the-art DRL algorithms.

24 POWER TRANSMISSION AND DISTRIBUTION↗

An Information Theoretic Approach to Identify Dominant Voltage Influencers for Unbalanced Distribution Systems

Smart distribution grid with multiple renewable energy sources can experience random voltage fluctuations due to variable generation, which may result in voltage violations. Traditional voltage control algorithms are inadequate to handle fast voltage variations. Therefore, new dynamic control methods are being developed that can significantly benefit from the knowledge of dominant voltage influencer (DVI) nodes. DVI nodes for a particular node of interest refer to nodes that have a relatively high impact on the voltage fluctuations at that node. Conventional power flow-based algorithms to identify DVI nodes are computationally complex, which limits their use in real-time applications. This paper proposes a novel information theoretic voltage influencing score (VIS) that quantifies the voltage influencing capacity of nodes with DERs/active loads in a three phase unbalanced distribution system. VIS is then employed to rank the nodes and identify the DVI set. VIS is derived analytically in a computationally efficient manner and its efficacy to identify DVI nodes is validated using the IEEE 37-node test system. It is shown through experiments that KL divergence and Bhattacharyya distance are effective indicators of DVI nodes with an identifying accuracy of more than 90%. Additionally, the computation burden is also reduced by an order of 5, thus providing the foundation for efficient voltage control.

42 ENGINEERING↗

Bayesian GAN-Based False Data Injection Attack Detection in Active Distribution Grids With DERs

Advancements in information and communication technologies have revolutionized monitoring and control capabilities within smart grids. However, it also brings new vulnerabilities to data acquisition systems and state estimation functions, which attackers can subtly tamper with the measurement data through compromising the communication network. Moreover, the high penetration of renewable energy sources with the inherited characteristics of uncertainty and variability further complicates the design of effective intrusion detection systems. In this paper, a Bayesian deep learning-based approach is developed to detect cyber attacks and maintain the security of smart grids. Our method specifically addresses the prevalent issue of imbalanced data in real power systems, which arises from the predominance of normal system operations over compromised or attacked states. Employing a novel Bayesian GAN-based technique, our approach successfully discriminates between secure and compromised measurement data, even in scenarios with significant data imbalance. Furthermore, the proposed method accommodates various practical application factors, ensuring accurate intrusion detection despite the presence of measurement noise. The feasibility and effectiveness of the proposed detection mechanism are validated by testing on IEEE 13-node and 123-node test systems. Simulation results and comparisons with literature methods demonstrate the superiority of proposed cybersecurity solutions.

Bayesian GAN↗

Quantitative Metrics for Grid Resilience Evaluation and Optimization

Power system resilience has become a critical topic in recent years because of the increasing trend of extreme events and the growing integration of intermittent renewable energy sources. To enhance grid resilience against high-impact, low-frequency events, two questions should be answered: how to quantify the resilience of a given grid and how to incorporate the quantification into power system planning, operation, and restoration. Here this paper develops a new set of quantitative metrics with clear physical interpretation to comprehensively evaluate power system resilience. Using microgrids as an example, an event-based corrective scheduling (ECS) model and an online model predictive control (OMPC) model are developed to integrate the proposed quantitative resilience metrics into power system optimization models for resilience enhancement. The ECS model employs extreme event data to investigate the optimal restoration solution and to help microgrid operators prepare to respond to similar events. The OMPC model provides online decision-making support for operators to handle ongoing outages in the most resilient fashion. The effectiveness and superiority of the proposed quantitative resilience metrics and the resilience enhancement models are demonstrated through simulations and comparative studies on an IEEE test feeder and a real distribution feeder in Southern California.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Visualizing Comparisons of Bill of Materials

Protecting critical infrastructure from cyber attacks, natural disasters, and other disruptions is a priority of the U.S. Government. Critical infrastructure includes providing electricity to homes and businesses, supplying natural gas for heating, and producing renewable energy sources. A loss of these services, as seen in the Solarwinds supply chain attack in 2020 , Texas snowstorm of 2021, the Colonial Pipeline cyber incident of 2021, and the Washington power substation attacks in 2022 result in high costs to consumers, disruption of everyday life, and even death. To protect the infrastructure, we first have to know what equipment we are protecting. The complexity of distributed manufacturing and development coupled with the increasing prevalence of cyber and supply chain attacks necessitates a greater understanding of the hardware and software components that comprise equipment in critical infrastructure. When a vulnerability in a single software library can have disastrous consequences, it is vital to understand critical equipment and systems at a granular level. This need has led to increased energy around the development and incorporation of bill-of-materials (BOM) into existing asset management practices to aid in mitigating, and responding to future attacks \cite{noauthor_software_nodate}. While much of the current research is devoted to creating BOMs, it is equally important to develop methodologies for leveraging BOMs to answer questions, such as: How has my software changed? Are two pieces of equipment equivalent? Does this piece of equipment that just arrived match my historical information? In this work, we demonstrate how BOMs can be represented by graph structures. We then describe how these structures can be fed into a graph comparison algorithm to produce a novel interactive visualization that allows us to not only identify differences in BOMs, but show exactly where they are in the product.

Jones, Rebecca D.↗

The photosynthetic response of C 3 and C 4 bioenergy grass species to fluctuating light

Abstract Bioenergy grass species are a renewable energy source, but their productivity has not been fully realized. Improving photosynthetic efficiency has been proposed as a mechanism to increase the productivity of bioenergy grass species. Fluctuating light, experienced by all field grown crops, is known to reduce photosynthetic efficiency. This experiment aimed to evaluate the photosynthetic performance of both C 3 and C 4 bioenergy grass species under steady state and fluctuating light conditions by examining leaf gas exchange. The fluctuating light regime used here decreased carbon assimilation across all species when compared to expected steady state values. Overall, C 4 species assimilated more carbon than C 3 species during the fluctuating light regime, with both photosynthetic types assimilating about 16% less carbon than expected based on steady state measurements. Little diversity was observed in response to fluctuating light among C 3 species, and photorespiration partially contributed to the rapid decreases in net photosynthetic rates during high to low light transitions. In C 4 species, differences among the four NADP‐ME species were apparent. Diversity observed among C 4 species in this experiment provides evidence that photosynthetic efficiency in response to fluctuating light may be targeted to increase C 4 bioenergy grass productivity.

09 BIOMASS FUELS↗

Characterizing Flow Instabilities During Transient Events in the Turbine Rim Seal Cavity

Abstract Gas turbine engine design requires considerations not only for long-term steady operation but also for critical transient events. Aircraft engines undergo significant stress during takeoff and landing, while power generation turbines must be flexible for hot restarts as renewable energy sources come online and offline. During these transient cycles, engines sustain wear and degradation that can lead to a reduction in the lifespan of their components and more frequent, costly maintenance. Cooling flows are often used to mitigate these effects, but can lead to complex and problematic flow interactions. This study uses high-frequency response pressure probes and heat flux gauges in the rim seal cavity of a one-stage research turbine to characterize the properties of large-scale flow structures during transient operation. A continuous-duration turbine testing facility provides the ability to assess the importance of these transients by first reaching steady-state operation before imposing transient behaviors. Although previous studies have conducted similar measurements for steady purge flows and wheel speeds, varying these parameters to simulate transient effects revealed several unique phenomena not identifiable with discrete steady measurements. The measurement approach connects the varied transient parameter to the behavior of the flow structures to enable a better understanding of the type of instability observed and the root cause of its formation. In particular, a relationship between instability cell formation and rim sealing effectiveness was identified using experimental data and was supported through computational simulations.

Engineering↗

A Comprehensive Review of Working Fluids for High-Temperature Heat Pumps: History, Selection, and Evaluation

High-temperature heat pumps (HTHPs) are essential for enhancing energy efficiency across various industrial applications, especially in terms of integrating with renewable energy sources and recovering waste heat. This article thoroughly investigates suitable working fluids for HTHPs, highlighting the evolution from traditional refrigerants to contemporary alternatives with low global warming potential (GWP). It proposes comprehensive selection criteria for these working fluids, pre-selects low-GWP working fluids, and outlines a screening methodology. The pre-selected low-GWP working fluids are evaluated for applications in three typical industrial scenarios involving HTHPs. Furthermore, this study demonstrates that regulatory compliance and environmental impacts significantly influence the development of next-generation refrigerants. The choice of working fluids is closely linked to the types of vapor compression cycles, tailored to the specific industrial applications for HTHPs. This study emphasizes areas for future research, including the development of innovative working fluids; integrated strategies that account for performance, safety, and regulatory standards; alignment of HTHP components; exploration of natural working fluids; and broadening the applications of existing working fluids.

Compression↗

Numerical Study of Solar Receiver Tube With Modified Surface Roughness for Enhanced and Selective Absorptivity in Concentrated Solar Power Tower

Abstract Concentrated solar power (CSP) is a reliable renewable energy source that is progressively lowering its cost of energy. However, the heat loss due to reflected and emitted radiation hinders the maximum achievable thermal efficiency for solar receiver tubes on the solar tower. Current solar selective coatings cannot withstand the high temperatures that come with state-of-the-art CSP towers often needing to be recoated soon after initial operation. We intend to use Inconel 718 with different additive manufacturing (AM) practices to construct surfaces that allow for more light-trapping to occur. By adjusting printing parameters, we can tailor a surface to allow for more absorption while diminishing emitted radiation heat loss. By using COMSOL Multiphysics, we can generate these theoretical surfaces to emulate a printed surface, and using the coupled Multiphysics we can simulate how the surface dictates radiation properties. Our results show that by having a rougher surface we can enhance the absorptivity of Inconel 718 (IN718) by 38.8%. We expect this work to transform how solar absorber tubes are manufactured without using selective coatings and supplement the US Department of Energy (DOE) 2030 SunShot Initiative.

14 SOLAR ENERGY↗

Marine energy converters: Potential acoustic effects on fishes and aquatic invertebrates

The potential effects of underwater anthropogenic sound and substrate vibration from offshore renewable energy development on the behavior, fitness, and health of aquatic animals is a continuing concern with increased deployments and installation of these devices. Initial focus of related studies concerned offshore wind. However, over the past decade, marine energy devices, such as a tidal turbines and wave energy converters, have begun to emerge as additional, scalable renewable energy sources. Because marine energy converters (MECs) are not as well-known as other anthropogenic sources of potential disturbance, their general function and what is known about the sounds and substrate vibrations that they produce are introduced. Furthermore, while most previous studies focused on MECs and marine mammals, this paper considers the potential of MECs to cause acoustic disturbances affecting nearshore and tidal fishes and invertebrates. In particular, the focus is on particle motion and substrate vibration from MECs because these effects are the most likely to be detected by these animals. Finally, an analysis of major data gaps in understanding the acoustics of MECs and their potential impacts on fishes and aquatic invertebrates and recommendations for research needed over the next several years to improve understanding of these potential impacts are provided.

16 TIDAL AND WAVE POWER↗

Power System Frequency Dynamics Modeling, State Estimation, and Control using Neural Ordinary Differential Equations (NODEs) and Soft Actor-Critic (SAC) Machine Learning Approaches

With the global energy transition of the electric power system, grid control, supervision, and protection is becoming more challenging. With the increasing integration of renewable energy sources (RES), the system dynamics are changing, causing traditional power system dynamic modeling with swing equation-based modeling approaches to fail. Additionally, the converter-dominated power grid is decreasing the system inertia, making the power system more fragile to the frequency swings. This paper first investigates and compares the application of a model-based Kalman filter state estimation approach with (i) a model-free machine learning approach --- neural ordinary differential equations (NODEs) --- and (ii) a data-driven system identification (SysId) approach to model and infer critical state values of the power system frequency dynamics. Then a model predictive control (MPC) framework is compared to a model-free Soft Actor-Critic (SAC) reinforcement learning (RL) control algorithm in providing efficient fast frequency response (FFR) to the power system frequency dynamics. The approaches are compared in terms of their performance goals as well as their per-timestep computational efficiency. Furthermore, the comparative study for state estimation shows that for the model-free requirement, both NODEs and SysId can provide accurate state estimates; however, with increasing model complexity, NODEs can be a better choice for model identification. Similarly, the results from the FFR comparative study show that the SAC RL-based FFR, once trained, outperforms MPC with better control signals and faster computation time, making the SAC RL-based FFR better option for providing FFR to the power system.

97 MATHEMATICS AND COMPUTING↗

Comparison of Planar and Tubular Flow Field Plates for Proton Exchange Membrane Fuel Cells (PEMFCs) through Simulation

Proton exchange membrane fuel cells are excellent clean energy alternatives to current non-renewable energy sources. Bipolar plates are a key component of these fuel cells and directly responsible for their performance. Traditional bipolar plates are created in a planar form, but recent research has revealed a novel tubular design that performs similarly to conventional plates. Here, this study compares traditional planar designs with tubular ones. The finite element ANSYS software was used to determine and visualize velocity and pressure distributions of fluid flow through the bipolar plate channels.

25 ENERGY STORAGE↗