A review of microgrid development in the United States – A decade of progress on policies, demonstrations, controls, and software tools

Microgrids have become increasingly popular in the United States. Supported by favorable federal and local policies, microgrid projects can provide greater energy stability and resilience within a project site or community. This paper reviews major federal, state, and utility-level policies driving microgrid development in the United States. Representative U.S. demonstration projects are selected and their technical characteristics and non-technical features are introduced. The paper discusses trends in the technology development of microgrid systems as well as microgrid control methods and interactions within the electricity market. Software tools for microgrid design, planning, and performance analysis are illustrated with each tool's core capability. Finally, the paper summarizes the successes and lessons learned during the recent expansion of the U.S. microgrid industry that may serve as a reference for other countries developing their own microgrid industries

ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning

To relieve the pain of manually selecting machine learning algorithms and tuning hyperparameters, automated machine learning (AutoML) methods have been developed to automatically search for good models. Due to the huge model search space, it is impossible to try all models. Users tend to distrust automatic results and increase the search budget as much as they can, thereby undermining the efficiency of AutoML. To address these issues, we design and implement ATMSeer, an interactive visualization tool that supports users in refining the search space of AutoML and analyzing the results. To guide the design of ATMSeer, we derive a workflow of using AutoML based on interviews with machine learning experts. A multi-granularity visualization is proposed to enable users to monitor the AutoML process, analyze the searched models, and refine the search space in real time. We demonstrate the utility and usability of ATMSeer through two case studies, expert interviews, and a user study with 13 end users.Comment: Published in the ACM Conference on Human Factors in Computing Systems (CHI), 2019, Glasgow, Scotland U

FedHM: Efficient Federated Learning for Heterogeneous Models via Low-rank Factorization

One underlying assumption of recent federated learning (FL) paradigms is that all local models usually share the same network architecture and size, which becomes impractical for devices with different hardware resources. A scalable federated learning framework should address the heterogeneity that clients have different computing capacities and communication capabilities. To this end, this paper proposes FedHM, a novel heterogeneous federated model compression framework, distributing the heterogeneous low-rank models to clients and then aggregating them into a full-rank model. Our solution enables the training of heterogeneous models with varying computational complexities and aggregates them into a single global model. Furthermore, FedHM significantly reduces the communication cost by using low-rank models. Extensive experimental results demonstrate that FedHM is superior in the performance and robustness of models of different sizes, compared with state-of-the-art heterogeneous FL methods under various FL settings. Additionally, the convergence guarantee of FL for heterogeneous devices is first theoretically analyzed

Gridless Evolutionary Approach for Line Spectral Estimation with Unknown Model Order

Gridless methods show great superiority in line spectral estimation. These methods need to solve an atomic $l_0$ norm (i.e., the continuous analog of $l_0$ norm) minimization problem to estimate frequencies and model order. Since this problem is NP-hard to compute, relaxations of atomic $l_0$ norm, such as nuclear norm and reweighted atomic norm, have been employed for promoting sparsity. However, the relaxations give rise to a resolution limit, subsequently leading to biased model order and convergence error. To overcome the above shortcomings of relaxation, we propose a novel idea of simultaneously estimating the frequencies and model order by means of the atomic $l_0$ norm. To accomplish this idea, we build a multiobjective optimization model. The measurment error and the atomic $l_0$ norm are taken as the two optimization objectives. The proposed model directly exploits the model order via the atomic $l_0$ norm, thus breaking the resolution limit. We further design a variable-length evolutionary algorithm to solve the proposed model, which includes two innovations. One is a variable-length coding and search strategy. It flexibly codes and interactively searches diverse solutions with different model orders. These solutions act as steppingstones that help fully exploring the variable and open-ended frequency search space and provide extensive potentials towards the optima. Another innovation is a model order pruning mechanism, which heuristically prunes less contributive frequencies within the solutions, thus significantly enhancing convergence and diversity. Simulation results confirm the superiority of our approach in both frequency estimation and model order selection.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Orbit- and Atom-Resolved Spin Textures of Intrinsic, Extrinsic and Hybridized Dirac Cone States

Combining first-principles calculations and spin- and angle-resolved photoemission spectroscopy measurements, we identify the helical spin textures for three different Dirac cone states in the interfaced systems of a 2D topological insulator (TI) of Bi(111) bilayer and a 3D TI Bi2Se3 or Bi2Te3. The spin texture is found to be the same for the intrinsic Dirac cone of Bi2Se3 or Bi2Te3 surface state, the extrinsic Dirac cone of Bi bilayer state induced by Rashba effect, and the hybridized Dirac cone between the former two states. Further orbit- and atom-resolved analysis shows that s and pz orbits have a clockwise (counterclockwise) spin rotation tangent to the iso-energy contour of upper (lower) Dirac cone, while px and py orbits have an additional radial spin component. The Dirac cone states may reside on different atomic layers, but have the same spin texture. Our results suggest that the unique spin texture of Dirac cone states is a signature property of spin-orbit coupling, independent of topology

The scalars from the topcolor scenario and the spin correlations of the top pair production at the LHC

The topcolor scenario predicts the existences of some new scalars. In this paper, we consider the contributions of these new particles to the observables, which are related to the top quark pair ($t\bar{t}$) production at the LHC. It is found that these new particles can generate significant corrections to the $t\bar{t}$ production cross section and the $t\bar{t}$ spin correlations.Comment: 23 pages, 4 figures; discussions and references added; agrees with published versio

Sulforaphane induces adipocyte browning and promotes glucose and lipid utilization

Scope: Obesity is closely related to the imbalance of white adipose tissue storing excess calories, and brown adipose tissue dissipating energy to produce heat in mammals. Recent studies revealed that acquisition of brown characteristics by white adipocytes, termed “browning,” may positively contribute to cellular bioenergetics and metabolism homeostasis. The goal was to investigate the putative effects of natural antioxidant sulforaphane (1-isothiocyanate-4-methyl-sulfonyl butane; SFN) on browning of white adipocytes. Methods and Results: 3T3-L1 mature white adipocytes were treated with SFN for 48 h, and then the mitochondrial content, function, and energy utilization were assessed. SFN was found to induce 3T3-L1 adipocytes browning based on the increased mitochondrial content and activity of respiratory chain enzymes, whereas the mechanism involved the upregulation of nuclear factor E2-related factor 2/ sirtuin1/ peroxisome proliferator-activated receptor gamma coactivator 1 alpha signaling. SFN enhanced uncoupling protein 1 expression, a marker for brown adipocyte, leading to the decrease in cellular ATP. SFN also enhanced glucose uptake and oxidative utilization, lipolysis and fatty acid oxidation in 3T3-L1 adipocytes. Conclusion: SFN-induced browning of white adipocytes enhanced the utilization of cellular fuel, and the application of SFN is a promising strategy to combat obesity and obesity-related metabolic disorder