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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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The need for a small step semantics and more generally for a thorough documentation and understanding of Coq's tacticals and PVS's strategies arise with their growing use and the progressive uncovering of their subtleties. The purpose of the following study is to provide a simple and clear formal framework to describe their detailed semantics, and highlight their differences and similarities.
We describe an approach to strategy-based proving for improved interactive deduction in specialized domains. An experimental package of strategies (tactics) and support functions called Manip has been developed for PVS to reduce the tedium of arithmetic manipulation. Included are strategies aimed at algebraic simplification of real-valued expressions. A general deduction architecture is described in which domain-specific strategies, such as those for algebraic manipulation, are supported by more generic features, such as term-access techniques applicable in arbitrary settings. An extended expression language provides access to subterms within a sequent.
We present a library of Prototype Verification System (PVS) meta-theories that verifies a class of distributed systems in which agent commu nication is through message-passing. The theoretic work, outlined in, consists of iterative schemes for solving systems of linear equations , such as message-passing extensions of the Gauss and Gauss-Seidel me thods. We briefly review that work and discuss the challenges in formally verifying it.
Computational reflection is a well-known and powerful way to speed up the proof process in an interactive theorem prover. This presentation introduces the concept using PVS, and showcases 2 of the strategies using the method.
This is a review of the REORG: Resilience and Stability Oriented Cellular Grid Formation and Optimizations for Communities with Solar PVs and Mobile Energy Storage for the NREL ADMS Test Bed Industry Advisory Board (IAB) meeting held on January 20, 2022.
Accurate knowledge of the distribution system topology and parameters is required to achieve good voltage control performance, but this is difficult to obtain in practice. This paper proposes a physical-model-free voltage control method based on a surrogate-model-enabled deep reinforcement learning approach. Specifically, a surrogate model is trained in a supervised manner using the recorded limited number of historical data to learn the relationship between the power injections and voltage fluctuations of each node. Then, the deep reinforcement learning algorithm is applied to learn an optimal control strategy from the experiences obtained by continuous interactions with the surrogate model. The proposed method can achieve physical-model-free control of unbalanced distribution network and inform real-time decisions to deal with fast voltage fluctuations caused by the rapid variation of PV generation. Simulation results on an unbalance IEEE 123-bus system show that the proposed method can achieve similar performance as that of perfect physical-model-based approaches while being advantageous over other traditional methods.
Global sensitivity analysis (GSA) of distribution systems with respect to stochastic PV and load variations plays an important role in designing optimal voltage control schemes. This paper proposes a data-driven framework for GSA of distribution systems. In particular, two representative surrogate modeling-based approaches are developed, including the traditional Gaussian process-based and the analysis of variance (ANOVA) kernel ones. The key idea is to develop a surrogate model that captures the hidden global relationship between voltage and real and reactive power injections from the historical data. With the surrogate model, the Sobol indices can be conveniently calculated through either the sampling-based method or the analytical method to assess the global sensitivity of voltage to variations of PV and load power injections. The sampling-based method approximates the Sobol indices using Monte Carlo simulations while the analytical method calculates them by resorting to the ANOVA expansion framework. Comparison results with other model-based GSA methods on the unbalanced three-phase IEEE 37-bus and 123-bus distribution systems show that the proposed framework can achieve much higher computational efficiency with negligible loss of accuracy. The results on a real 240-node distribution system using actual smart meter data further validate the feasibility and scalability of the proposed framework.
Global sensitivity analysis (GSA) of the voltage to uncertain power injection variations plays an important role for appropriate Volt-VAR optimization. This paper proposes a data-driven GSA method for large-scale distribution systems with a large number of uncertain sources. Specifically, the deep Gaussian process is used to identify the mapping relationship between uncertain power injections and voltages. This allows us to resort to the analysis of variance framework to calculate the Sobol indices for GSA. Unlike the existing polynomial chaos expansion and Gaussian process-based approaches, our proposed method has much better scalability. Test results on the EPRI 1747-node K1 circuit with a different number and different probability distributions of uncertain sources demonstrate that the proposed method can achieve accurate GSA under various conditions.
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Runtime assurance is a control framework where a complex controller operates under the observation of a monitor. If the monitor detects the controller exhibiting undesirable behavior, control is passed off to a trusted controller until a desirable state is regained. The runtime assurance architecture provides a layer of assurance to the system being controlled, but special care must be taken that the resulting overall system, consisting of the monitors and controllers, is behaving as intended. This talk aims to formally model and reason about runtime assurance-equipped systems as hybrid programs- which are models that consist of both discrete and continuous components. Using the verification tool Plaidypvs, safety properties of some examples involving RTA architectures is shown.
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For new technologies, such as perovskite solar cells (PSC), life cycle analysis (LCA) offers a fundamental framework for examining potential environmental, energy and health impacts and mitigation options before large-scale commercialization and for guiding improvements in development and production that further reduce their environmental footprint. However, credible LCA studies require actual process-based material, energy and emissions data, which may not exist before the technologies are commercially produced. Thus, the perovskite LCA literature is based on linear extrapolations of laboratory data. In this paper we critically reviewed the PSC LCA literature, explain the reasoning for a wide divergence of results, and determined which data apply to scalable industrial production, materials and processes. Our investigation probed into the formulation of each layer of a PSC device, and its potential for industrial scale fabrication. We found that electricity use is the main contributor to reported LCA results, explaining the large difference, ranging from 7.78 kWh to 1,460 kWh/m –2 , among various studies. Subsequently, we identified and discuss methodological errors in some of these estimates. In terms of life-cycle toxicity most of the reviewed LCA studies do not attribute any major overall toxicity impact to the presence of lead in the PSC devices. We also reviewed and critiqued studies describing “worst-case” scenarios of accidental release of lead into the environment, and, in spite of statements in those studies, we found them to be inconclusive. Lastly, we discussed end-of-life (EoL) management options for resource recovery and for minimizing environmental impacts.
Advanced Distribution Management Systems (ADMS) are being widely adopted by electric utilities for managing and optimizing the operations of their distribution systems. Distributed photovoltaic (DPV) systems with smart inverters can be controlled to adjust active power and reactive power outputs, and they are envisioned to become a part of (centrally or distributed) controllable assets managed by the ADMS for optimizing grid operations. This paper proposes an optimal energy dispatch strategy controlling DPV systems for regulating distribution voltages and achieving conservation voltage reduction. A convex optimization model is proposed with the use of linearized power flow, and the gradient projection algorithm is used to solve the optimal active power and reactive power outputs of smart inverters. The proposed optimal energy dispatch is implemented using an open-source ADMS platform, and simulation results have demonstrated the effectiveness of the proposed approach on improving distribution grid operations.
This paper proposes a model-free decentralized control framework for the voltage regulation of large-scale distribution systems through the coordinated control of PV inverters. This is achieved by developing a novel interaction mechanism between the surrogate model and the centralized training and decentralized execution multiagent deep reinforcement learning framework. Specifically, the sparse Gaussian processes regression method is first utilized to develop the surrogate model of the original distribution system for reward calculation during the training stage, where each agent represents a sub-region in the centralized fashion for coordination strategy learning. After that, the learned control rules are used to inform controllers within each sub-region for real-time decisions with only local measurements. Comparative tests among various methods on the EPRI Ckt5 test system demonstrate the effectiveness of the proposed method.
Global sensitivity analysis (GSA) of distribution system with respect to stochastic PV variations plays an important role in designing optimal voltage control schemes. This paper proposes a Kriging, i.e., Gaussian process modeling enabled data-driven GSA method. The key idea is to develop a surrogate model that captures the hidden global relationship between voltage and real and reactive power injections from the historical data. With the surrogate model, the Sobol index can be conveniently calculated to assess the global sensitivity of voltage to various power injection variations. Comparison results with other model-based GSA methods on the IEEE 37-bus feeder, such as the polynomial chaos expansion and the Monte Carlo approaches demonstrate that the proposed method can achieve accurate GSA outcomes while maintaining high computational efficiency.
Here, it is found from actual data that due to generation dispatch and uncertain renewable generations and loads with complicated correlations, inferring the probabilistic distributions for uncertain inputs is challenging. Many probabilistic power flow approaches have been developed in the literature but their validations using realistic systems and data are lacking. This paper proposes a data-driven probabilistic analysis approach for system risk assessment of the miniWECC system using actual data. The sparse Gaussian process (SGP) is advocated to quantify the impacts of uncertain inputs on voltage security. SGP does not need the probability distribution function of uncertain inputs, can handle correlations and is highly computationally efficient. Results on the miniWECC system using realistic data show that SGP outperforms existing approaches and is able to quantify the voltage violation risks.