PowerSystems.jl — A power system data management package for large scale modeling
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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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Extreme weather events pose significant risks to power grid stability due to their severe consequences and potential for widespread failures. Energy storage systems hold great potential for enhancing grid resilience against such events by providing reliable power during peak demand periods. However, accurately quantifying the size, location, and investment costs of new energy storage assets is a complex task, as energy storage planning decisions depend on the investment choices of other generation technologies and the integration of new transmission projects. Here, this paper presents a novel capacity expansion planning framework that simultaneously optimizes investments in energy storage, generation, and transmission, determining their optimal size, location, and type, while incorporating extreme weather events into long-term planning. More specifically, our stress-event-informed planning framework integrates the impact of heatwaves and wildfires into the planning process, identifying least-cost investment solutions that comply with policy goals and enhance grid resilience. The proposed framework employs machine-learning-based modeling to project heatwave-induced loads and performance-based risk assessment to evaluate wildfire-driven transmission line derates. Using industry-standard datasets to accurately represent the transmission topology of the Western Interconnection (WI) system, the proposed framework is applied to the WI 40-zone system, with investment decisions reported for the years 2030, 2035, and 2040. Simulation results reveal that with just a 10% increase in investment costs, resilience against extreme events can be significantly improved, with investment decisions heavily favoring energy storage, particularly 4-hour energy storage systems.
Solving the alternating current power flow equations in real time is essential for secure grid operation, yet classical Newton–Raphson solvers can be slow under stressed conditions. Existing graph neural networks for power flow are typically trained on a single system and often degrade on different systems. We present PowerModelsGAT-AI, a physics-informed graph attention network that predicts bus voltages and generator injections. The model uses bus-type-aware masking to handle different bus types and balances multiple loss terms, including a power-mismatch penalty, using learned weights. We evaluate the model on 14 benchmark systems (4 to 6,470 buses) and train a unified model on 13 of these under contingency conditions with up to two branch outages, achieving an average normalized mean absolute error of 0.89% for voltage magnitudes and R 2 >0.99 for voltage angles. We also show continual learning: when adapting a base model to a new 1,354-bus system, standard fine-tuning causes severe forgetting with error increases exceeding 1000% on base systems, while our experience replay and elastic weight consolidation strategy keeps error increases below 2% and in some cases improves base-system performance. Interpretability analysis shows that learned attention weights correlate with physical branch parameters (susceptance: r=0.38 ; thermal limits: r=0.22 ), and feature importance analysis supports that the model captures established power flow relationships.
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District energy systems efficiently provide thermal energy to multiple buildings and facilities through a network of shared infrastructure. Frequently, district energy systems are centered around combined heat and power (CHP) plants that generate electricity as well as heating and cooling to the local buildings. By providing both electricity and thermal energy from a single fuel source and central location, these systems use less fuel, decrease energy and operational costs, and reduce the need for heating and cooling equipment in individual buildings.
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Work on the NYSERDA-MDAO project, presented at the OpenMDAO workshop.
In the Smart Grid paradigm, this critical infrastructure operation is increasingly exposed to cyber-threats due to the increased dependency on communication networks. An adversary can launch an attack on a power grid operation through False Data Injection into system measurements and/or through attacks on the communication network, such as flooding the communication channels with unnecessary data or intercepting messages. A cross-layered strategy that combines power grid data, communication grid monitoring and Machine Learning based processing is a promising solution for detecting cyberthreats. In this paper, an implementation of an integrated solution of a cross-layer framework is presented. The advantage of such a framework is the augmentation of valuable data that enhances the detection of anomalies in the operation of power grid. IEEE 118-bus system is built in Simulink to provide a power grid testing environment and communication network data is emulated using SimComponents. The performance of the framework is investigated under various FDI and communication attacks.
The use of solid particles as a heat transfer fluid in receivers for concentrating solar powerplants has garnered much attention in the past decade. Particles are not only stable at temperatures above 700 °C but are also able to absorb solar energy directly, which gives them a distinct advantage over conventional molten salts. The use of particles can, however, cause material degradation from the flow of hot or cold particles through discharge hoppers or along the inner receiver surfaces and other system components (e.g. tubes, valves etc.), depending on operating mode of the receiver. Stainless steel alloy 316, nickel-based alloy Inconel® 740H®, Haynes 230, and a 60% alumina, low-cement refractory castable from Allied Mineral Products were tested for wear from first-of-a-kind high-temperature low-velocity abrasion-erosion experiments. The tests are designed to simulate potential conditions in Generation 3 falling particle Concentrating Solar Power (CSP) systems. Simultaneous experiments were conducted at room temperature as well to draw comparison on the role of temperature on abrasion wear rates. It was noted that the abrasion wear rate in candidate materials was driven by the oxidation resistance of the material. The findings from these experiments will be used to determine life-time of critical components in particle receiver concentrated solar powerplants.
Electrical grids are geographical and topological structures whose voltage states are challenging to represent accurately and efficiently for visual analysis. The current common practice is to use colored contour maps, yet these can misrepresent the data. We examine the suitability of four alternative visualization methods for depicting voltage data in a geographically dense distribution system-Voronoi polygons, H3 tessellations, S2 tessellations, and a network-weighted contour map. We find that Voronoi tessellations and network-weighted contour maps more accurately represent the statistical distribution of the data than regular contour maps.
Electrical grids are geographical and topological structures whose voltage states are challenging to represent accurately and efficiently for visual analysis. The current common practice is to use colored contour maps, yet these can misrepresent the data. We examine the suitability of four alternative visualization methods for depicting voltage data in a geographically dense distribution system - Voronoi polygons, H3 tessellations, S2 tessellations, and a network-weighted contour map. We find that Voronoi tessellations and network-weighted contour maps more accurately represent the statistical distribution of the data than regular contour maps.
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Next generation of fuel cells, electrolyzers, and batteries requires higher power, faster kinetics, and larger energy density, which necessitate the use of compositionally complex oxides to achieve multifunctionalities and activity. These compositionally complex oxides may change their phases and structures during an electrochemical process – a so-called “electrochemically driven phase transformation”. The origin for such a phase change has remained obscure. More importantly, there is a need to develop high performance solid oxide fuel cells with an enhanced stability. In this work, the La 0.6 Sr 0.4 Co 0.2 Fe 0.8 O 3 (LSCF) cathode surface is modified by infiltration of Pr 6 O 11 and the power density at 0.8V and 750 °C is improved by 21%. In addition, by replacing the traditional barrier layer Gd 0.2 Ce 0.8 O 1.9 with mixed conducting Pr 0.1 Gd 0.1 Ce 0.8 O 1.9 , the power density increases by 38%. The different mechanism of promotions was investigated by electrochemical impedance spectroscopy. The ohmic resistance is dramatically reduced by applying the PGCO interlayer, and the distribution of relaxation time was used to analyze the mechanism for which the polarization resistance was decreased attributing to the mixed conduction nature in PrO x . An increase of power density at 0.8 V of 0.358 W/cm 2 (71%) is achieved with the implementation of both surface modification and buffer layer engineering. An experimental study and a theoretical analysis were then carried out on phase evolution in praseodymium nickelates. Nickelate-based electrodes show up to 60× greater phase transformation during operation when compared to thermally annealed ones. Theoretical analysis suggests that the presence of a reduced oxygen partial pressure at the interface between the oxygen electrode and the electrolyte is the origin for the phase change in an oxygen electrode. Guided by the theory, an addition of the electronic conduction in the interface layer leads to the significant suppression of phase change, while improving cell performance and performance stability. When an oxygen electrode is under polarization, the oxygen partial pressure at the interface between the oxygen electrode and the electrolyte is lower than that of incoming oxidant. Under a high polarization, the environment at the aforementioned interface may lead to phase transformation of the oxygen electrode. The local oxygen partial pressure is determined by the transport properties at the interfaces. An addition of the electronic conduction in the interface layer, for instance using (Pr,Gd)-doped ceria to replace Gd-doped ceria, results in improved cell performance and performance stability, while the phase transformation is significantly suppressed. This work provides a fundamental understanding of the origin for phase transformation in oxygen electrodes during operation and use this knowledge to develop a high-performance electrode that exhibits improved performance stability.