SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
This presentation was delivered at the 2023 AICHE Annual Meeting. This presentation would focus on dynamic optimization of solid oxide cell systems with due consideration of chemical degradation over the cell lifetime. A multi-scale dynamic optimization algorithm is solved and found to yield superior results compared to the constant voltage or constant H2 production operation approaches.
The atomic lattice plays a critical role in the emergence of high-𝑇 𝑐 superconductivity in cuprates. While the dynamics associated with electron-lattice coupling typically unfold on picosecond-to-femtosecond timescales, we present an x-ray photon correlation spectroscopy investigation on an optimally doped La-based cuprate that reveals a strong response of kilosecond-scale lattice relaxation dynamics to the superconducting state. Notably, an anomaly emerges around 𝑇 𝑐 : upon cooling into the superconducting state, the average atomic relaxation lifetime decreases, i.e., dynamics accelerate. This indicates a significant change in the local disorder-induced strain field dynamics at the superconducting transition, highlighting a remarkable coupling between superconductivity and the lattice on quasistatic timescales.
Abstract Dynamic (temporal) graphs are a convenient mathematical abstraction for many practical complex systems including social contacts, business transactions, and computer communications. Community discovery is an extensively used graph analysis kernel with rich literature for static graphs. However, community discovery in a dynamic setting is challenging for two specific reasons. Firstly, the notion of temporal community lacks a widely accepted formalization, and only limited work exists on understanding how communities emerge over time. Secondly, the added temporal dimension along with the sheer size of modern graph data necessitates new scalable algorithms. In this paper, we investigate how communities evolve over time based on several graph metrics under a temporal formalization. We compare six different algorithmic approaches for dynamic community detection for their quality and runtime. We identify that a vertex-centric (local) optimization method works as efficiently as the classical modularity-based methods. To its advantage, such local computation allows for the efficient design of parallel algorithms without incurring a significant parallel overhead. Based on this insight, we design a shared-memory parallel algorithm DyComPar , which demonstrates between 4 and 18 fold speed-up on a multi-core machine with 20 threads, for several real-world and synthetic graphs from different domains.
In this paper, the empirical controllability covariance (ECC), which is calculated around the considered operating condition of a power system, is applied to quantify the degree of controllability of system voltages under specific dynamic var source locations. An optimal dynamic var source placement method addressing fault-induced delayed voltage recovery (FIDVR) issues is further formulated as an optimization problem that maximizes the determinant of ECC. The optimization problem is effectively solved by the NOMAD solver, which implements the mesh adaptive direct search algorithm. The proposed method is tested on an NPCC 140-bus system and the results show that the proposed method with fault specified ECC can solve the FIDVR issue caused by the most severe contingency with fewer dynamic var sources than the voltage sensitivity index (VSI)-based method. The proposed method with fault unspecified ECC does not depend on the settings of the contingency and can address more FIDVR issues than the VSI method when placing the same number of SVCs under different fault durations. It is also shown that the proposed method can help mitigate voltage collapse.
This paper considers two dynamic load models that are widely used in industry to account for induction motor behavior: CMLD and CLOD. These models must be parametrized for the specific utility system in a general way so that they can be used in planning studies and provide a conservative but realistic representation of load behavior. This study considers a measurement-based approach to tuning both models. The load modeling study compares the response of the tuned models to generic candidate models using historical events. This study considers one area-based subsystem to simplify the modeling approach and reduce the number of models required for simulations. Additionally, because dynamic load models often produce similar results for different sets of parameters, a sensitivity study was conducted to assess the parameter impacts on the voltage response. The sensitivity study covers the parameters that are tuned using event measurements. The process to estimate the parameters uses the particle-swarm optimization algorithm. Overall, the performance of the tuned model more accurately captures recovery voltage, delayed recovery, and settling voltage than its predecessor models while not being overly tuned so that it remains general for peak summer conditions.
Optimizing the performance of solid oxide electrolysis cells (SOECs) for long-term hydrogen (H 2 ) production at high temperatures is crucial, as prolonged operation leads to efficiency losses and shorter cell lifespans due to chemical degradation. Here, in this work, we adopt a quasi-steady state approach for dynamic optimization over extended operational periods to address the disparity in timescales between cell operation and degradation. Integrating a 2-D non-isothermal SOEC model with balance-of-plant (BOP) equipment, we explore three optimization objectives: minimizing terminal degradation, maximizing integral efficiency, and minimizing the levelized cost of H 2 (LCOH). Our dynamic optimization algorithm reduces LCOH by 9.5% and 16% compared to strategies focusing solely on terminal degradation and integral efficiency, respectively. For electricity prices of 0.03 $\$$/mWh and 0.3 $\$$ mWh optimal replacement schedules range from 5 to 2 years, depending on the operational mode. Furthermore, a flexible operational mode yields additional improvements in LCOH over traditional galvanostatic and potentiostatic modes.
Growing awareness of climate change has led to increased focus on new energy processes that mitigate generation of CO 2 , or provide for its efficient capture. To enable the development of these processes, advanced modeling and optimization platforms have been created. New capabilities among platforms include efficient solution strategies for online optimization problems. For real time, dynamic optimization of CO 2 capture processes, we demonstrate the application of the Python‐based Pyomo platform to facilitate realization of Moving Horizon Estimation and Nonlinear Model Predictive Control, through novel nonlinear optimization and sensitivity strategies. This capability allows large scale, first principle models to be applied for online optimization. Here, we introduce the Control and Adaptation with Predictive Sensitivity Enhancements (CAPRESE) framework to demonstrate this framework for advanced energy processes. Moreover, we present two case studies: profile tracking with detailed tray‐by‐tray distillation models and Bubbling Fluidized Bed reactors for CO 2 capture. © 2018 American Institute of Chemical Engineers AIChE J , 65: e16511 2019
Abstract Cerium(III/IV) is a redox couple with promising applications in redox flow batteries and redox‐mediated water electrolyzers, but due to its high oxidation potential, parasitic oxygen evolution can occur and decrease the Faradaic efficiency (FE) of the oxidation reaction. Dynamic potential dosing can help balance the transport limitations and the electrooxidation rates, allowing for control over the cerium(III) concentration near the electrode, enhancing FE. Due to the large number of possible pulsing operating conditions, Bayesian optimization (BO) was used to rapidly identify the optimal potential pulses. With an active pulse voltage of 2.5 V vs. Ag/AgCl, the maximum FE achieved was 0.91 (active pulse time = 5 ms, resting pulse time = 136 ms), underscoring the need for long inactive times for cerium(III) diffusion to the electrode. Furthermore, a multiobjective BO was performed to identify optimal trade‐offs between FE and the partial current density to achieve improvements over constant current operation.
A data-driven chaos indicator concept is introduced to characterize the degree of chaos for nonlinear dynamical systems. The indicator is represented by the prediction accuracy of surrogate models established purely from data. It provides a metric for the predictability of nonlinear motions in a given system. When using the indicator to implement a tune-scan for a quadratic Hénon map, the main resonances and their asymmetric stop-band widths can be identified. When applied to particle transportation in a storage ring, as particle motion becomes more chaotic, its surrogate model prediction accuracy decreases correspondingly. Therefore, the prediction accuracy, acting as a chaos indicator, can be used directly as the objective for nonlinear beam dynamics optimization. This method provides a different perspective on nonlinear beam dynamics and an efficient method for nonlinear lattice optimization. Applications in dynamic aperture optimization are demonstrated as real world examples.
Dynamic ride-sharing (DRS) has been projected to be a key solution to lowering system-wide congestion. Despite recent development progress, demand studies for DRS suggest low levels of willingness for travelers to use such services. The disconnect between DRS system designs and user preferences limits the application impacts of DRS in the real world. Therefore, this paper aims to design a new DRS system by considering the user preferences of choices under different levels of services. In this study, an agent-based approach is used to model a fleet of shared vehicles that allows DRS. An optimization model is developed to match riders to vehicles while accounting for traveler delay and delay acceptance. Travelers are also dynamically issued predictive discounts to incentivize them to accept longer trip delays. Results show that the proposed approach can improve system efficiency by increasing average vehicle occupancy by up to 1.0 persons/trip and DRS acceptance up to 38.9% depending on fleet size. Additionally, congestion is eased through the decrease of empty vehicle miles traveled by up to 7.1%.
A data-driven chaos indicator concept is introduced to characterize the degree of chaos for nonlinear dynamical systems. The indicator is represented by the prediction accuracy of surrogate models established purely from data. It provides a metric for the predictability of nonlinear motions in a given system. When using the indicator to implement a tune-scan for a quadratic Hénon map, the main resonances and their asymmetric stop-band widths can be identified. When applied to particle transportation in a storage ring, as particle motion becomes more chaotic, its surrogate model prediction accuracy decreases correspondingly. So, the prediction accuracy, acting as a chaos indicator, can be used directly as the objective for nonlinear beam dynamics optimization. This method provides a different perspective on nonlinear beam dynamics and an efficient method for nonlinear lattice optimization. Applications in dynamic aperture optimization are demonstrated as real world examples.
To address the rising electricity demand and greenhouse gas concentration in the environment, considerable effort is being carried out across the globe on installing and operating renewable energy sources. However, the renewable energy production is affected by diurnal and seasonal variability. To ensure that the electric grid remains reliable and resilient even for the high penetration of renewables into the grid, various types of energy storage systems are being investigated. In this paper, a compressed-air energy storage (CAES) system integrated with a natural gas combined-cycle (NGCC) power plant is investigated where air is extracted from the gas turbine compressor or injected back into the gas turbine combustor when it is optimal to do so. First-principles dynamic models of the NGCC plant and CAES are developed along with the development of an economic model. The dynamic optimization of the integrated system is undertaken in the Python/Pyomo platform for maximizing the net present value (NPV). NPV optimization is undertaken for 14 regions/cases considering year-long locational marginal price (LMP) data with a 1 h interval. Design variables such as the storage capacity and storage pressure, as well as the operating variables such as the power plant load, air injection rate, and air extraction rate, are optimized. Results show that the integrated CAES system has a higher NPV than the NGCC-only system for all 14 regions, thus indicating the potential deployment of the integrated system under the assumption of the availability of caverns in close proximity to the NGCC plant. The levelized cost of storage is found to be in the range of 136–145 $/MWh. Roundtrip efficiency is found to be between 74.6–82.5%. A sensitivity study with respect to LMP shows that the LMP profile has a significant impact on the extent of air injection/extraction while capital expenditure reduction has a negligible effect.
This study investigates the technical and economic feasibility of using high levels of solar energy penetration up to 400 MW into a smart grid system of 60,000 smart houses. A novel non-cooperative Stackelberg game is introduced that incorporates the profitability of the supply-side and helps in solving problems related to overgeneration and photovoltaic curtailment. The non-cooperative game is intended to find the optimal dynamic prices that would leverage distributed storage through the demand-side to stabilize the power grid operation. Ten cases are studied with five photovoltaic plant sizes and two battery designs. Here, a novel quantitative analysis of high levels of solar penetration as a percentage of the total electricity demand is introduced to evaluate the technical feasibility of the studied cases. To evaluate the economic viability of the proposed smart grid system, four metrics were used: the levelized cost of energy, the levelized cost of storage, the payback period, and the net present value. Two out of ten studied cases were concluded to be the most promising cases, one with a solar photovoltaic plant size of 200 MW and the other with 300 MW. The case with 300 MW solar plant is preferred as it paves the way for more solar energy deployment with a solar penetration percentage up to 67.78%. This case had a payback period of 10.72 years and a net present value of $\$51.44$ M for the solar plant and a payback period of 12.06 years and a net present value of $\$40.75$ M for the demand-side.
It is essential to improve the resiliency of power distribution systems (PDS) with the increase in extreme weather events, number of malicious threats and consumers’ need for higher reliability. Here, this paper provides a formal approach to evaluate the operational resiliency of PDS, and quantify the resiliency of a system using a code-based metric. A combination of steady state and dynamic simulation tools is used to determine the resiliency metric. Dynamic simulation tools help with analyzing impact of short-term events, which might affect operational resiliency in long term. A dynamic optimization algorithm for changing operating criteria to increase the sustainability of the most critical loads has been proposed. The proposed theoretical approach is validated using a simple power distribution system model and simulation results demonstrate the ability to quantify the resiliency using the proposed code-based metric. The time-dependent quantification of resiliency has been demonstrated on a test system of two connected CERTS microgrids.
Abstract not provided.
We describe pyomo.dae, an open source Python-based modeling framework that enables high-level abstract specification of optimization problems with differential and algebraic equations. The pyomo.dae framework is integrated with the Pyomo open source algebraic modeling language, and is available at http://www.pyomo.org. One key feature of pyomo.dae is that it does not restrict users to standard, predefined forms of differential equations, providing a high degree of modeling flexibility and the ability to express constraints that cannot be easily specified in other modeling frameworks. Other key features of pyomo.dae are the ability to specify optimization problems with high-order differential equations and partial differential equations, defined on restricted domain types, and the ability to automatically transform high-level abstract models into finite-dimensional algebraic problems that can be solved with off-the-shelf solvers. Moreover, pyomo.dae users can leverage existing capabilities of Pyomo to embed differential equation models within stochastic and integer programming models and mathematical programs with equilibrium constraint formulations. Collectively, these features enable the exploration of new modeling concepts, discretization schemes, and the benchmarking of state-of-the-art optimization solvers.
The (viscous) anisotropic hydrodynamic approach, especially after perturbative inclusion of all residual viscous terms, has been shown to dramatically outperform viscous hydrodynamics in several simplified situations for which exact solutions exist but which share with realistic expansion scenarios the problem of large dissipative currents. Here we will report on the present status of applying viscous anisotropic hydrodynamics in a highly efficient simulation of the full three-dimensional quark-gluon plasma. Results from accelerated 3+1-dimensional viscous hydrodynamic simulations using graphics processing units will be compared to the anisotropic frameworks.
Not Available