Under-frequency Load Shedding for Power Reserve Management in Islanded Microgrids

This paper introduces under-frequency load shedding (UFLS) schemes specially designed to fulfill the power reserve requirements in islanded microgrids (MGs), where only one grid-forming resource is available for frequency regulation. When the power consumption of the MG exceeds a pre-defined threshold, the MG frequency will be lowered to various setpoints, thereby triggering UFLS for different levels of load reduction. Three types of controllable devices are considered for executing UFLS: sectionalizers, smart meters, and controllable appliances. To avoid unnecessary UFLS activation, various time delay settings are analyzed, allowing short-lived power spikes caused by events like motor startups or cold-load pickups to be disregarded. We tested the proposed UFLS schemes on a modified IEEE 123-bus system on the OPAL-RT eMEGASIM platform. Simulation results verify the efficacy of the proposed approaches in restoring power reserves, maintaining phase power balance, and effectively handling short-lived power fluctuations. Furthermore, in comparison to sectionalizer-based UFLS, using smart meters or controllable loads for UFLS allows for a more accurate per-phase load shedding in a progressive manner. As a result, it leads to better balanced three-phase voltage and serves more loads.Comment: 10 pages, 15 figure

The Structure of Coronal Mass Ejections Recorded by the K-Coronagraph at Mauna Loa Solar Observatory

Previous survey studies reported that coronal mass ejections (CMEs) can exhibit various structures in white-light coronagraphs, and $\sim$30\% of them have the typical three-part feature in the high corona (e.g., 2--6 $R_\odot$), which has been taken as the prototypical structure of CMEs. It is widely accepted that CMEs result from eruption of magnetic flux ropes (MFRs), and the three-part structure can be understood easily by means of the MFR eruption. It is interesting and significant to answer why only $\sim$30\% of CMEs have the three-part feature in previous studies. Here we conduct a synthesis of the CME structure in the field of view (FOV) of K-Coronagraph (1.05--3 $R_\odot$). In total, 369 CMEs are observed from 2013 September to 2022 November. After inspecting the CMEs one by one through joint observations of the AIA, K-Coronagraph and LASCO/C2, we find 71 events according to the criteria: 1) limb event; 2) normal CME, i.e., angular width $\geq$ 30$^{\circ}$; 3) K-Coronagraph caught the early eruption stage. All (or more than 90\% considering several ambiguous events) of the 71 CMEs exhibit the three-part feature in the FOV of K-Coronagraph, while only 30--40\% have the feature in the C2 FOV (2--6 $R_\odot$). For the first time, our studies show that 90--100\% and 30--40\% of normal CMEs possess the three-part structure in the low and high corona, respectively, which demonstrates that many CMEs can lose the three-part feature during their early evolutions, and strongly supports that most (if not all) CMEs have the MFR structures.Comment: 10 pages, 4 figures, accepted for publication in ApJ

An Iterative Bidirectional Gradient Boosting Algorithm for CVR Baseline Estimation

This paper presents a novel iterative, bidirectional, gradient boosting (bidirectional-GB) algorithm for estimating the baseline of the Conservation Voltage Reduction (CVR) program. We define the CVR baseline as the load profile during the CVR period if the substation voltage is not lowered. The proposed algorithm consists of two key steps: selection of similar days and iterative bidirectional-GB training. In the first step, pre- and post-event temperature profiles of the targeted CVR day are used to select similar days from historical non-CVR days. In the second step, the pre-event and post-event similar days are used to train two GBMs iteratively: a forward-GBM and a backward-GBM. After each iteration, the two generated CVR baselines are reconciled and only the first and the last points on the reconciled baseline are kept. The iteration repeats until all CVR baseline points are generated. We tested two gradient boosting methods (i.e., GBM and LighGBM) with two data resolutions (i.e., 15- and 30-minute). The results demonstrate that both the accuracy and performance of the algorithm are satisfactory.Comment: 5 pages, 8 figures, 2 table

MultiLoad-GAN: A GAN-Based Synthetic Load Group Generation Method Considering Spatial-Temporal Correlations

This paper presents a deep-learning framework, Multi-load Generative Adversarial Network (MultiLoad-GAN), for generating a group of load profiles in one shot. The main contribution of MultiLoad-GAN is the capture of spatial-temporal correlations among a group of loads to enable the generation of realistic synthetic load profiles in large quantity for meeting the emerging need in distribution system planning. The novelty and uniqueness of the MultiLoad-GAN framework are three-fold. First, it generates a group of load profiles bearing realistic spatial-temporal correlations in one shot. Second, two complementary metrics for evaluating realisticness of generated load profiles are developed: statistics metrics based on domain knowledge and a deep-learning classifier for comparing high-level features. Third, to tackle data scarcity, a novel iterative data augmentation mechanism is developed to generate training samples for enhancing the training of both the classifier and the MultiLoad-GAN model. Simulation results show that MultiLoad-GAN outperforms state-of-the-art approaches in realisticness, computational efficiency, and robustness. With little finetuning, the MultiLoad-GAN approach can be readily extended to generate a group of load or PV profiles for a feeder, a substation, or a service area.Comment: Submitted to IEEE Transactions on Smart Gri

Using customer service dialogues for satisfaction analysis with context-assisted multiple instance learning

Customers ask questions and customer service staffs answer their questions, which is the basic service model via multi-turn customer service (CS) dialogues on E-commerce platforms. Existing studies fail to provide comprehensive service satisfaction analysis, namely satisfaction polarity classification (e.g., well satisfied, met and unsatisfied) and sentimental utterance identification (e.g., positive, neutral and negative). In this paper, we conduct a pilot study on the task of service satisfaction analysis (SSA) based on multi-turn CS dialogues. We propose an extensible Context-Assisted Multiple Instance Learning (CAMIL) model to predict the sentiments of all the customer utterances and then aggregate those sentiments into service satisfaction polarity. After that, we propose a novel Context Clue Matching Mechanism (CCMM) to enhance the representations of all customer utterances with their matched context clues, i.e., sentiment and reasoning clues. We construct two CS dialogue datasets from a top E-commerce platform. Extensive experimental results are presented and contrasted against a few previous models to demonstrate the efficacy of our model

A Novel Feeder-level Microgrid Unit Commitment Algorithm Considering Cold-load Pickup, Phase Balancing, and Reconfiguration

This paper presents a novel 2-stage microgrid unit commitment (Microgrid-UC) algorithm considering cold-load pickup (CLPU) effects, three-phase load balancing requirements, and feasible reconfiguration options. Microgrid-UC schedules the operation of switches, generators, battery energy storage systems, and demand response resources to supply 3-phase unbalanced loads in an islanded microgrid for multiple days. A performance-based CLPU model is developed to estimate additional energy needs of CLPU so that CLPU can be formulated into the traditional 2-stage UC scheduling process. A per-phase demand response budget term is added to the 1st stage UC objective function to meet 3-phase load unbalance limits. To reduce computational complexity in the 1st stage UC, we replace the spanning tree method with a feasible reconfiguration topology list method. The proposed algorithm is developed on a modified IEEE 123-bus system and tested on the real-time simulation testbed using actual load and PV data. Simulation results show that Microgrid-UC successfully accounts for CLPU, phase imbalance, and feeder reconfiguration requirements.Comment: 10 pages, submitted to IEEE Transactions on Smart Gri

High-efficiency segmented thermoelectric power generation modules constructed from all skutterudites

Development of thermoelectric conversion technology for power generation can alleviate the demand for fossil energy and increase the efficiency of energy utilization. To achieve more efficient heat-to-electric conversion, it is desirable to maximize the figure of merit (zT) over a wide temperature range. Constructing a segmented thermoelectric device by serially connecting materials with high zT at different operating temperatures has been proven feasible. However, the issue of compatibility of different thermoelectric materials and the method of connecting different segments to ensure high interfacial stability remain unsolved. Herein, we demonstrate a full skutterudite-based segmented thermoelectric power generation module. The use of thermoelectric materials from the same parent avoids the difference in thermal expansion coefficients and compatibility factors and allows the preparation of thermoelectric junctions by a one-step sintering process. As a result, a high module efficiency of 10.4% is obtained owing to the rational design of the materials, device geometry, and interfaces and is the highest value among skutterudite-based modules reported so far

Transparent Power-Generating Windows Based on Solar-Thermal-Electric Conversion

Zhang Q, Huang A, Ai X, et al. Transparent Power-Generating Windows Based on Solar-Thermal-Electric Conversion. Advanced Energy Materials . 2021: 2101213.Integrating transparent solar-harvesting systems into windows can provide renewable on-site energy supply without altering building aesthetics or imposing further design constraints. Transparent photovoltaics have shown great potential, but the increased transparency comes at the expense of reduced power-conversion efficiency. Here, a new technology that overcomes this limitation by combining solar-thermal-electric conversion with a material's wavelength-selective absorption is presented. A wavelength-selective film consisting of Cs0.33WO3 and resin facilitates high visible-light transmittance (up to 88%) and outstanding ultraviolet and infrared absorbance, thereby converting absorbed light into heat without sacrificing transparency. A prototype that couples the film with thermoelectric power generation produces an extraordinary output voltage of approximate to 4 V within an area of 0.01 m(2) exposed to sunshine. Further optimization design and experimental verification demonstrate high conversion efficiency comparable to state-of-the-art transparent photovoltaics, enriching the library of on-site energy-saving and transparent power generation

Sonic Hedgehog Pathway Is Essential for Maintenance of Cancer Stem-Like Cells in Human Gastric Cancer

Abnormal activation of the Sonic hedgehog (SHH) pathway has been described in a wide variety of human cancers and in cancer stem cells (CSCs), however, the role of SHH pathway in gastric CSCs has not been reported. In this study, we investigated the possibility that abnormal activation of the SHH pathway maintained the characteristics of gastric CSCs. First, we identified cancer stem-like cells (CSLCs) from human gastric cancer cell lines (HGC-27, MGC-803 and MKN-45) using tumorsphere culture. Compared with adherent cells, the floating tumorsphere cells had more self-renewing capacity and chemoresistance. The cells expressing CSCs markers (CD44, CD24 and CD133) were also significantly more in tumorsphere cells than in adherent cells. More importantly, in vivo xenograft studies showed that tumors could be generated with 2×104 tumorsphere cells, which was 100-fold less than those required for tumors seeding by adherent cells. Next, RT-PCR and Western blot showed that the expression levels of Ptch and Gli1 (SHH pathway target genes) were significantly higher in tumorsphere cells than in adherent cells. The results of quantitative real-time PCR were similar to those of RT-PCR and Western blot. Further analysis revealed that SHH pathway blocked by cyclopamine or 5E1 caused a higher reduction in self-renewing capacity of HGC-27 tumorsphere cells than that of adherent cells. We also found that SHH pathway blocking strongly enhanced the efficacy of chemotherapeutic drugs in HGC-27 tumorsphere cells in vitro and in vivo but had no significant effect in adherent cells. Finally, we isolated the tumorspheres from gastric cancer specimen, these cells also had chemoresistance and tumorigenic capacity, and SHH pathway maintained the gastric CSLCs characteristics of tumorsphere cells from primary tumor samples. In conclusion, our data suggested that SHH pathway was essential for maintenance of CSLCs in human gastric cancer