The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Ram Rajagopal - One of the best experts on this subject based on the ideXlab platform.
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unbalanced multi phase distribution Grid Topology estimation and bus phase identification
IET Smart Grid, 2019Co-Authors: Yizheng Liao, Yang Weng, Guangyi Liu, Zhongyang Zhao, Chinwoo Tan, Ram RajagopalAbstract:There is an increasing need for monitoring and controlling uncertainties brought by distributed energy resources in distribution Grids. For such goal, accurate multi-phase Topology is the basis for correlating measurements in unbalanced distribution networks. Unfortunately, such Topology knowledge is often unavailable due to limited investment. Also, the bus phase labeling information is inaccurate due to human errors or outdated records. For this challenge, this paper utilizes smart meter data for an information-theoretic approach to learn the Topology of distribution Grids. Specifically, multi-phase unbalanced systems are converted into symmetrical components, namely positive, negative, and zero sequences. Then, this paper proves that the Chow-Liu algorithm finds the Topology by utilizing power flow equations and the conditional independence relationships implied by the radial multi-phase structure of distribution Grids with the presence of incorrect bus phase labels. At last, by utilizing Carson's equation, this paper proves that the bus phase connection can be correctly identified using voltage measurements. For validation, IEEE systems are simulated using three real data sets. The simulation results demonstrate that the algorithm is highly accurate for finding multi-phase Topology even with strong load unbalancing condition and DERs. This ensures close monitoring and controlling DERs in distribution Grids.
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urban mv and lv distribution Grid Topology estimation via group lasso
IEEE Transactions on Power Systems, 2019Co-Authors: Yizheng Liao, Yang Weng, Ram RajagopalAbstract:The increasing penetration of distributed energy resources poses numerous reliability issues to the urban distribution Grid. The Topology estimation is a critical step to ensure the robustness of distribution Grid operation. However, the bus connectivity and Grid Topology estimation are usually hard in distribution Grids. For example, it is technically challenging and costly to monitor the bus connectivity in urban Grids, e.g., underground lines. It is also inappropriate to use the radial Topology assumption exclusively because the Grids of metropolitan cities and regions with dense loads could be with many mesh structures. To resolve these drawbacks, we propose a data-driven Topology estimation method for medium voltage (MV) and low voltage (LV) distribution Grids by only utilizing the historical smart meter measurements. Particularly, a probabilistic graphical model is utilized to capture the statistical dependencies amongst bus voltages. We prove that the bus connectivity and Grid Topology estimation problems, in radial and mesh structures, can be formulated as a linear regression with a least absolute shrinkage regularization on grouped variables ( group lasso ). Simulations show highly accurate results in eight MV and LV distribution networks at different sizes and 22 Topology configurations using Pacific Gas and Electric Company residential smart meter data.
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unbalanced multi phase distribution Grid Topology estimation and bus phase identification
arXiv: Systems and Control, 2018Co-Authors: Yizheng Liao, Yang Weng, Guangyi Liu, Zhongyang Zhao, Chinwoo Tan, Ram RajagopalAbstract:There is an increasing need for monitoring and controlling uncertainties brought by distributed energy resources in distribution Grids. For such goal, accurate multi-phase Topology is the basis for correlating measurements in unbalanced distribution networks. Unfortunately, such Topology knowledge is often unavailable due to limited investment, especially for \revv{low-voltage} distribution Grids. Also, the bus phase labeling information is inaccurate due to human errors or outdated records. For this challenge, this paper utilizes smart meter data for an information-theoretic approach to learn the Topology of distribution Grids. Specifically, multi-phase unbalanced systems are converted into symmetrical components, namely positive, negative, and zero sequences. Then, this paper proves that the Chow-Liu algorithm finds the Topology by utilizing power flow equations and the conditional independence relationships implied by the radial multi-phase structure of distribution Grids with the presence of incorrect bus phase labels. At last, by utilizing Carson's equation, this paper proves that the bus phase connection can be correctly identified using voltage measurements. For validation, IEEE systems are simulated using three real data sets. The simulation results demonstrate that the algorithm is highly accurate for finding multi-phase Topology even with strong load unbalancing condition and DERs. This ensures close monitoring and controlling DERs in distribution Grids.
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urban mv and lv distribution Grid Topology estimation via group lasso
arXiv: Machine Learning, 2018Co-Authors: Yizheng Liao, Yang Weng, Ram RajagopalAbstract:The increasing penetration of distributed energy resources poses numerous reliability issues to the urban distribution Grid. The Topology estimation is a critical step to ensure the robustness of distribution Grid operation. However, the bus connectivity and Grid Topology estimation are usually hard in distribution Grids. For example, it is technically challenging and costly to monitor the bus connectivity in urban Grids, e.g., underground lines. It is also inappropriate to use the radial Topology assumption exclusively because the Grids of metropolitan cities and regions with dense loads could be with many mesh structures. To resolve these drawbacks, we propose a data-driven Topology estimation method for MV and LV distribution Grids by only utilizing the historical smart meter measurements. Particularly, a probabilistic graphical model is utilized to capture the statistical dependencies amongst bus voltages. We prove that the bus connectivity and Grid Topology estimation problems, in radial and mesh structures, can be formulated as a linear regression with a least absolute shrinkage regularization on grouped variables (\textit{group lasso}). Simulations show highly accurate results in eight MV and LV distribution networks at different sizes and 22 Topology configurations using PG\&E residential smart meter data.
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distributed energy resources Topology identification via graphical modeling
IEEE Transactions on Power Systems, 2017Co-Authors: Yang Weng, Yizheng Liao, Ram RajagopalAbstract:Distributed energy resources (DERs), such as photovoltaic, wind, and gas generators, are connected to the Grid more than ever before, which introduces tremendous changes in the distribution Grid. Due to these changes, it is important to understand where these DERs are connected in order to sustainably operate the distribution Grid. But the exact distribution system Topology is difficult to obtain due to frequent distribution Grid reconfigurations and insufficient knowledge about new components. In this paper, we propose a methodology that utilizes new data from sensor-equipped DER devices to obtain the distribution Grid Topology. Specifically, a graphical model is presented to describe the probabilistic relationship among different voltage measurements. With power flow analysis, a mutual information-based identification algorithm is proposed to deal with tree and partially meshed networks. Simulation results show highly accurate connectivity identification in the IEEE standard distribution test systems and Electric Power Research Institute test systems.
Ross Baldick - One of the best experts on this subject based on the ideXlab platform.
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voltage regulation algorithms for multiphase power distribution Grids
IEEE Transactions on Power Systems, 2016Co-Authors: Vassilis Kekatos, Georgios B Giannakis, Liang Zhang, Ross BaldickAbstract:Time-varying renewable energy generation can result in serious under-/over-voltage conditions in future distribution Grids. Augmenting conventional utility-owned voltage regulating equipment with the reactive power capabilities of distributed generation units is a viable solution. Local control options attaining global voltage regulation optimality at fast convergence rates is the goal here. In this context, novel reactive power control rules are analyzed under a unifying linearized Grid model. For single-phase Grids, our proximal gradient scheme has computational complexity comparable to that of the rule suggested by the IEEE 1547.8 standard, but it enjoys well-characterized convergence guarantees. Furthermore, adding memory to the scheme results in accelerated convergence. For three-phase Grids, it is shown that reactive power injections have a counter-intuitive effect on bus voltage magnitudes across phases. Nevertheless, when our control scheme is applied to unbalanced conditions, it is shown to reach an equilibrium point. Numerical tests using the IEEE 13-bus, the IEEE 123-bus, and a Southern California Edison 47-bus feeder with increased renewable penetration verify the properties of the schemes and their resiliency to Grid Topology reconfigurations.
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online energy price matrix factorization for power Grid Topology tracking
IEEE Transactions on Smart Grid, 2016Co-Authors: Vassilis Kekatos, Georgios B Giannakis, Ross BaldickAbstract:Grid security and open markets are two major smart Grid goals. Transparency of market data facilitates a competitive and efficient energy environment. But it may also reveal critical physical system information. Recovering the Grid Topology based solely on publicly available market data is explored here. Real-time energy prices are typically calculated as the Lagrange multipliers of network-constrained economic dispatch; that is, via a linear program (LP) typically solved every 5 min. Since the Grid Laplacian matrix is a parameter of this LP, someone apart from the system operator could try inferring this Topology-related matrix upon observing successive LP dual outcomes. It is first shown that the matrix of spatio-temporal prices can be factored as the product of the inverse Laplacian times a sparse matrix. Leveraging results from sparse matrix decompositions, Topology recovery schemes with complementary strengths are subsequently formulated. Solvers scalable to high-dimensional and streaming market data are devised. Numerical validation using synthetic and real-load data on the IEEE 30-bus Grid provide useful input for current and future market designs.
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voltage regulation algorithms for multiphase power distribution Grids
arXiv: Systems and Control, 2015Co-Authors: Vassilis Kekatos, Georgios B Giannakis, Liang Zhang, Ross BaldickAbstract:Time-varying renewable energy generation can result in serious under-/over-voltage conditions in future distribution Grids. Augmenting conventional utility-owned voltage regulating equipment with the reactive power capabilities of distributed generation units is a viable solution. Local control options attaining global voltage regulation optimality at fast convergence rates is the goal here. In this context, novel reactive power control rules are analyzed under a unifying linearized Grid model. For single-phase Grids, our proximal gradient scheme has computational complexity comparable to that of the rule suggested by the IEEE 1547.8 standard, but it enjoys well-characterized convergence guarantees. Adding memory to the scheme results in accelerated convergence. For three-phase Grids, it is shown that reactive injections have a counter-intuitive effect on bus voltage magnitudes across phases. Nevertheless, when our control scheme is applied to unbalanced conditions, it is shown to reach an equilibrium point. Yet this point may not correspond to the minimizer of a voltage regulation problem. Numerical tests using the IEEE 13-bus, the IEEE 123-bus, and a Southern California Edison 47-bus feeder with increased renewable penetration verify the convergence properties of the schemes and their resiliency to Grid Topology reconfigurations.
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online energy price matrix factorization for power Grid Topology tracking
arXiv: Machine Learning, 2014Co-Authors: Vassilis Kekatos, Georgios B Giannakis, Ross BaldickAbstract:Grid security and open markets are two major smart Grid goals. Transparency of market data facilitates a competitive and efficient energy environment, yet it may also reveal critical physical system information. Recovering the Grid Topology based solely on publicly available market data is explored here. Real-time energy prices are calculated as the Lagrange multipliers of network-constrained economic dispatch; that is, via a linear program (LP) typically solved every 5 minutes. Granted the Grid Laplacian is a parameter of this LP, one could infer such a Topology-revealing matrix upon observing successive LP dual outcomes. The matrix of spatio-temporal prices is first shown to factor as the product of the inverse Laplacian times a sparse matrix. Leveraging results from sparse matrix decompositions, Topology recovery schemes with complementary strengths are subsequently formulated. Solvers scalable to high-dimensional and streaming market data are devised. Numerical validation using real load data on the IEEE 30-bus Grid provide useful input for current and future market designs.
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Grid Topology identification using electricity prices
Power and Energy Society General Meeting, 2014Co-Authors: Vassilis Kekatos, Georgios B Giannakis, Ross BaldickAbstract:The potential of recovering the Topology of a Grid using solely publicly available market data is explored here. In contemporary whole-sale electricity markets, real-time prices are typically determined by solving the network-constrained economic dispatch problem. Under a linear DC model, locational marginal prices (LMPs) correspond to the Lagrange multipliers of the linear program involved. The interesting observation here is that the matrix of spatiotemporally varying LMPs exhibits the following property: Once premultiplied by the weighted Grid Laplacian, it yields a low-rank and sparse matrix. Leveraging this rich structure, a regularized maximum likelihood estimator (MLE) is developed to recover the Grid Laplacian from the LMPs. The convex optimization problem formulated includes low rank-and sparsity-promoting regularizers, and it is solved using a scalable algorithm. Numerical tests on prices generated for the IEEE 14-bus benchmark provide encouraging Topology recovery results.
Yizheng Liao - One of the best experts on this subject based on the ideXlab platform.
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unbalanced multi phase distribution Grid Topology estimation and bus phase identification
IET Smart Grid, 2019Co-Authors: Yizheng Liao, Yang Weng, Guangyi Liu, Zhongyang Zhao, Chinwoo Tan, Ram RajagopalAbstract:There is an increasing need for monitoring and controlling uncertainties brought by distributed energy resources in distribution Grids. For such goal, accurate multi-phase Topology is the basis for correlating measurements in unbalanced distribution networks. Unfortunately, such Topology knowledge is often unavailable due to limited investment. Also, the bus phase labeling information is inaccurate due to human errors or outdated records. For this challenge, this paper utilizes smart meter data for an information-theoretic approach to learn the Topology of distribution Grids. Specifically, multi-phase unbalanced systems are converted into symmetrical components, namely positive, negative, and zero sequences. Then, this paper proves that the Chow-Liu algorithm finds the Topology by utilizing power flow equations and the conditional independence relationships implied by the radial multi-phase structure of distribution Grids with the presence of incorrect bus phase labels. At last, by utilizing Carson's equation, this paper proves that the bus phase connection can be correctly identified using voltage measurements. For validation, IEEE systems are simulated using three real data sets. The simulation results demonstrate that the algorithm is highly accurate for finding multi-phase Topology even with strong load unbalancing condition and DERs. This ensures close monitoring and controlling DERs in distribution Grids.
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urban mv and lv distribution Grid Topology estimation via group lasso
IEEE Transactions on Power Systems, 2019Co-Authors: Yizheng Liao, Yang Weng, Ram RajagopalAbstract:The increasing penetration of distributed energy resources poses numerous reliability issues to the urban distribution Grid. The Topology estimation is a critical step to ensure the robustness of distribution Grid operation. However, the bus connectivity and Grid Topology estimation are usually hard in distribution Grids. For example, it is technically challenging and costly to monitor the bus connectivity in urban Grids, e.g., underground lines. It is also inappropriate to use the radial Topology assumption exclusively because the Grids of metropolitan cities and regions with dense loads could be with many mesh structures. To resolve these drawbacks, we propose a data-driven Topology estimation method for medium voltage (MV) and low voltage (LV) distribution Grids by only utilizing the historical smart meter measurements. Particularly, a probabilistic graphical model is utilized to capture the statistical dependencies amongst bus voltages. We prove that the bus connectivity and Grid Topology estimation problems, in radial and mesh structures, can be formulated as a linear regression with a least absolute shrinkage regularization on grouped variables ( group lasso ). Simulations show highly accurate results in eight MV and LV distribution networks at different sizes and 22 Topology configurations using Pacific Gas and Electric Company residential smart meter data.
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unbalanced multi phase distribution Grid Topology estimation and bus phase identification
arXiv: Systems and Control, 2018Co-Authors: Yizheng Liao, Yang Weng, Guangyi Liu, Zhongyang Zhao, Chinwoo Tan, Ram RajagopalAbstract:There is an increasing need for monitoring and controlling uncertainties brought by distributed energy resources in distribution Grids. For such goal, accurate multi-phase Topology is the basis for correlating measurements in unbalanced distribution networks. Unfortunately, such Topology knowledge is often unavailable due to limited investment, especially for \revv{low-voltage} distribution Grids. Also, the bus phase labeling information is inaccurate due to human errors or outdated records. For this challenge, this paper utilizes smart meter data for an information-theoretic approach to learn the Topology of distribution Grids. Specifically, multi-phase unbalanced systems are converted into symmetrical components, namely positive, negative, and zero sequences. Then, this paper proves that the Chow-Liu algorithm finds the Topology by utilizing power flow equations and the conditional independence relationships implied by the radial multi-phase structure of distribution Grids with the presence of incorrect bus phase labels. At last, by utilizing Carson's equation, this paper proves that the bus phase connection can be correctly identified using voltage measurements. For validation, IEEE systems are simulated using three real data sets. The simulation results demonstrate that the algorithm is highly accurate for finding multi-phase Topology even with strong load unbalancing condition and DERs. This ensures close monitoring and controlling DERs in distribution Grids.
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urban mv and lv distribution Grid Topology estimation via group lasso
arXiv: Machine Learning, 2018Co-Authors: Yizheng Liao, Yang Weng, Ram RajagopalAbstract:The increasing penetration of distributed energy resources poses numerous reliability issues to the urban distribution Grid. The Topology estimation is a critical step to ensure the robustness of distribution Grid operation. However, the bus connectivity and Grid Topology estimation are usually hard in distribution Grids. For example, it is technically challenging and costly to monitor the bus connectivity in urban Grids, e.g., underground lines. It is also inappropriate to use the radial Topology assumption exclusively because the Grids of metropolitan cities and regions with dense loads could be with many mesh structures. To resolve these drawbacks, we propose a data-driven Topology estimation method for MV and LV distribution Grids by only utilizing the historical smart meter measurements. Particularly, a probabilistic graphical model is utilized to capture the statistical dependencies amongst bus voltages. We prove that the bus connectivity and Grid Topology estimation problems, in radial and mesh structures, can be formulated as a linear regression with a least absolute shrinkage regularization on grouped variables (\textit{group lasso}). Simulations show highly accurate results in eight MV and LV distribution networks at different sizes and 22 Topology configurations using PG\&E residential smart meter data.
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distributed energy resources Topology identification via graphical modeling
IEEE Transactions on Power Systems, 2017Co-Authors: Yang Weng, Yizheng Liao, Ram RajagopalAbstract:Distributed energy resources (DERs), such as photovoltaic, wind, and gas generators, are connected to the Grid more than ever before, which introduces tremendous changes in the distribution Grid. Due to these changes, it is important to understand where these DERs are connected in order to sustainably operate the distribution Grid. But the exact distribution system Topology is difficult to obtain due to frequent distribution Grid reconfigurations and insufficient knowledge about new components. In this paper, we propose a methodology that utilizes new data from sensor-equipped DER devices to obtain the distribution Grid Topology. Specifically, a graphical model is presented to describe the probabilistic relationship among different voltage measurements. With power flow analysis, a mutual information-based identification algorithm is proposed to deal with tree and partially meshed networks. Simulation results show highly accurate connectivity identification in the IEEE standard distribution test systems and Electric Power Research Institute test systems.
Vassilis Kekatos - One of the best experts on this subject based on the ideXlab platform.
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graph algorithms for Topology identification using power Grid probing
IEEE Control Systems Letters, 2018Co-Authors: Guido Cavraro, Vassilis KekatosAbstract:To perform any meaningful optimization task, power distribution operators need to know the Topology and line impedances of their electric networks. Nevertheless, distribution Grids currently lack a comprehensive metering infrastructure. Although smart inverters are widely used for control purposes, they have been recently advocated as the means for an active data acquisition paradigm: reading the voltage deviations induced by intentionally perturbing inverter injections, the system operator can potentially recover the electric Grid Topology. Adopting inverter probing for feeder processing, a suite of graph-based Topology identification algorithms is developed here. If the Grid is probed at all leaf nodes but voltage data are metered at all nodes, the entire feeder Topology can be successfully recovered. When voltage data are collected only at probing buses, the operator can find a reduced feeder featuring key properties and similarities to the actual feeder. To handle modeling inaccuracies and load nonstationarity, noisy probing data need to be preprocessed. If the suggested guidelines on the magnitude and duration of probing are followed, the recoverability guarantees carry over from the noiseless to the noisy setup with high probability.
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voltage regulation algorithms for multiphase power distribution Grids
IEEE Transactions on Power Systems, 2016Co-Authors: Vassilis Kekatos, Georgios B Giannakis, Liang Zhang, Ross BaldickAbstract:Time-varying renewable energy generation can result in serious under-/over-voltage conditions in future distribution Grids. Augmenting conventional utility-owned voltage regulating equipment with the reactive power capabilities of distributed generation units is a viable solution. Local control options attaining global voltage regulation optimality at fast convergence rates is the goal here. In this context, novel reactive power control rules are analyzed under a unifying linearized Grid model. For single-phase Grids, our proximal gradient scheme has computational complexity comparable to that of the rule suggested by the IEEE 1547.8 standard, but it enjoys well-characterized convergence guarantees. Furthermore, adding memory to the scheme results in accelerated convergence. For three-phase Grids, it is shown that reactive power injections have a counter-intuitive effect on bus voltage magnitudes across phases. Nevertheless, when our control scheme is applied to unbalanced conditions, it is shown to reach an equilibrium point. Numerical tests using the IEEE 13-bus, the IEEE 123-bus, and a Southern California Edison 47-bus feeder with increased renewable penetration verify the properties of the schemes and their resiliency to Grid Topology reconfigurations.
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online energy price matrix factorization for power Grid Topology tracking
IEEE Transactions on Smart Grid, 2016Co-Authors: Vassilis Kekatos, Georgios B Giannakis, Ross BaldickAbstract:Grid security and open markets are two major smart Grid goals. Transparency of market data facilitates a competitive and efficient energy environment. But it may also reveal critical physical system information. Recovering the Grid Topology based solely on publicly available market data is explored here. Real-time energy prices are typically calculated as the Lagrange multipliers of network-constrained economic dispatch; that is, via a linear program (LP) typically solved every 5 min. Since the Grid Laplacian matrix is a parameter of this LP, someone apart from the system operator could try inferring this Topology-related matrix upon observing successive LP dual outcomes. It is first shown that the matrix of spatio-temporal prices can be factored as the product of the inverse Laplacian times a sparse matrix. Leveraging results from sparse matrix decompositions, Topology recovery schemes with complementary strengths are subsequently formulated. Solvers scalable to high-dimensional and streaming market data are devised. Numerical validation using synthetic and real-load data on the IEEE 30-bus Grid provide useful input for current and future market designs.
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voltage regulation algorithms for multiphase power distribution Grids
arXiv: Systems and Control, 2015Co-Authors: Vassilis Kekatos, Georgios B Giannakis, Liang Zhang, Ross BaldickAbstract:Time-varying renewable energy generation can result in serious under-/over-voltage conditions in future distribution Grids. Augmenting conventional utility-owned voltage regulating equipment with the reactive power capabilities of distributed generation units is a viable solution. Local control options attaining global voltage regulation optimality at fast convergence rates is the goal here. In this context, novel reactive power control rules are analyzed under a unifying linearized Grid model. For single-phase Grids, our proximal gradient scheme has computational complexity comparable to that of the rule suggested by the IEEE 1547.8 standard, but it enjoys well-characterized convergence guarantees. Adding memory to the scheme results in accelerated convergence. For three-phase Grids, it is shown that reactive injections have a counter-intuitive effect on bus voltage magnitudes across phases. Nevertheless, when our control scheme is applied to unbalanced conditions, it is shown to reach an equilibrium point. Yet this point may not correspond to the minimizer of a voltage regulation problem. Numerical tests using the IEEE 13-bus, the IEEE 123-bus, and a Southern California Edison 47-bus feeder with increased renewable penetration verify the convergence properties of the schemes and their resiliency to Grid Topology reconfigurations.
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online energy price matrix factorization for power Grid Topology tracking
arXiv: Machine Learning, 2014Co-Authors: Vassilis Kekatos, Georgios B Giannakis, Ross BaldickAbstract:Grid security and open markets are two major smart Grid goals. Transparency of market data facilitates a competitive and efficient energy environment, yet it may also reveal critical physical system information. Recovering the Grid Topology based solely on publicly available market data is explored here. Real-time energy prices are calculated as the Lagrange multipliers of network-constrained economic dispatch; that is, via a linear program (LP) typically solved every 5 minutes. Granted the Grid Laplacian is a parameter of this LP, one could infer such a Topology-revealing matrix upon observing successive LP dual outcomes. The matrix of spatio-temporal prices is first shown to factor as the product of the inverse Laplacian times a sparse matrix. Leveraging results from sparse matrix decompositions, Topology recovery schemes with complementary strengths are subsequently formulated. Solvers scalable to high-dimensional and streaming market data are devised. Numerical validation using real load data on the IEEE 30-bus Grid provide useful input for current and future market designs.
O.m. Yaghi - One of the best experts on this subject based on the ideXlab platform.
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a porous covalent organic framework with voided square Grid Topology for atmospheric water harvesting
Journal of the American Chemical Society, 2020Co-Authors: H.l. Nguyen, N. Hanikel, S.j. Lyle, D.m. Proserpio, O.m. YaghiAbstract:Atmospheric moisture is a ubiquitous water resource available at any time and any place, making it attractive to develop materials for harvesting water from air to address the imminent water shorta...
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A Porous Covalent Organic Framework with Voided Square Grid Topology for Atmospheric Water Harvesting
'American Chemical Society (ACS)', 2020Co-Authors: H.l. Nguyen, N. Hanikel, S.j. Lyle, C. Zhu, D.m. Proserpio, O.m. YaghiAbstract:Atmospheric moisture is a ubiquitous water resource available at any time and any place, making it attractive to develop materials for harvesting water from air to address the imminent water shortage crisis. In this context, we have been exploring the applicability of covalent organic frameworks (COFs) for water harvesting and report here a new porous, two-dimensional imine-linked COF with a voided square Grid Topology, termed COF-432. Unlike other reported COFs, COF-432 meets the requirements desired for water harvesting from air in that it exhibits an S-shaped water sorption isotherm with a steep pore-filling step at low relative humidity and without hysteretic behavior-properties essential for energy-efficient uptake and release of water. Further, it can be regenerated at ultra-low temperatures and displays exceptional hydrolytic stability, as demonstrated by the retention of its working capacity after 300 water adsorption-desorption cycles
