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The rotational inertia and primary frequency response capabilities of power systems are declining as synchronous generators (SGs) are replaced by inverter-based variable renewable energy (VRE) resources. Therefore, when a high VRE proportion is present, it becomes essential to include frequency security constraints in power system planning. Under this circumstance, the decline in SGs would deteriorate the system frequency response performance; thus, inverter-based resources are supposed to provide frequency response support. This paper presents a frequency-constrained stochastic planning method for high VRE share systems. Frequency response support from wind farms is considered in the planning process for the first time, and the unique characteristics of wind power support are addressed. Specifically, the uncertainty of and variations in wind power support capabilities are modeled from the field-measured data, and the optimal combination of various support schemes can be determined by introducing binary variables with novel linear constraints into the planning process. Moreover, the nonlinear frequency constraints are transformed and embedded in the planning model by the proposed computationally efficient adaptive piecewise linearization method. The planning is formulated as a stochastic optimization model to consider the VRE power uncertainty. Finally, case studies on the IEEE 30-bus system validate the proposed method, with results indicating time savings of 22%. Further, compared with the case that only SGs provide frequency support, the results based on the East China system demonstrate that considering wind power support can improve the VRE share from 49.5% to 79.7%.

Renewable‐dominant mixtures with hydrogeneration and variable renewable energies (VREs) have been the focus for decarbonization of power systems. Hydrogeneration with large reservoir becomes the main regulating source. The long‐term generation scheduling (LTGS) on monthly reservoir regulation for the approaching year becomes more significant because reserving adequate water storage is related to intramonth balance, considering that hydrogeneration has to supplement deficit energy of VRE generation. However, conventional LTGS method that is based on monthly averages overestimates the hydrogeneration's intramonth energy supporting capability, whereas rolling short‐term generation scheduling method cannot account the long‐term effect of reservoir regulation decisions. This paper proposes a new LTGS method accounting intramonth imbalance risk that the energy deficit of VRE generation exceeds the maximum supporting energy of hydrogeneration. Since intramonth imbalance risk is coupled with LTGS in an uncertain and implicit way, intramonth extreme scenarios are introduced based on distribution quantiles of natural inflow of reservoirs and VRE generation, and the probabilistic intramonth imbalance risk constraints can be transformed as the hydrogeneration must supplement energy deficit of VRE generation during the extreme scenarios. A case study is conducted on a Wind‐PV‐Hydro energy base of southwestern China. The proposed method is found able to improve the feasibility and reliability of LTGS compared to other LTGS methods. Moreover, the high calculation efficiency of conventional LTGS method is inherited.

Battery energy storage systems (BESSs) can provide instantaneous support for frequency regulation (FR) because of their fast response characteristics. However, purely pursuing a better FR effect calls for continually rapid cycles of BESSs, which shortens their lifetime and deteriorates the operational economy. To coordinate the lifespan savings and the FR effect, this paper presents a control strategy for the FR of BESSs based on fuzzy logic and hierarchical controllers. The fuzzy logic controller improves the effect of FR by adjusting the charging/discharging power of the BESS with a higher response speed and precision based on the area control error (ACE) signal and the change rate of ACE in a non-linear way. Hierarchical controllers effectively reduce the life loss by optimizing the depth of discharge, which ensures that the state of charge (SOC) of BESS is always in the optimal operating range, and the total FR cost is the lowest at this time. The proposed method can achieve the optimal balance between ACE reduction and operational economy of BESS. The effectiveness of the proposed strategy is verified in a two-area power system.

This paper investigates scheduling methods for two-stage real-time systems (2S-RTSs) that execute aperiodic tasks with firm and soft end-to-end deadlines. 2S-RTSs are often used to capture task execution in reactive systems where the first stage is responsible for detecting and preprocessing irregularly occurring external events and the second stage for taking certain actions to react to the identified situations. Since aperiodic tasks have no regular arrival patterns, it is more appropriate for such 2S-RTS to adopt an online scheduler to make scheduling decisions according to the current system state. We prove that the 2S-RTS scheduling problem is NP-hard, and present a novel share-based heuristic scheduling algorithm, referred to as HS-2S-RTS, to accomplish efficient online scheduling. Different from traditional optimal schedulers that aim to ensure all hard and soft deadline constraints, the goal of HS-2S-RTS consists of ensuring all firm deadline constraints and maximizing the minimum processor share assigned to soft tasks. Since the processor resource assigned to soft tasks is optimized without affecting firm deadline constraints, the schedulability of both firm and soft tasks can be improved. To reduce the actual run-time overhead, we further propose a quasi-shared-based scheduling algorithm, referred to as qHS-2S-RTS, that can achieve equivalent scheduling performance to HS-2S-RTS. We also design a new admission control algorithm for HS-2S-RTS and qHS-2S-RTS, called HSAC-2S-RTS. Simulation results validate our theoretical analysis that qHS-2S-RTS and HS-2S-RTS are able to satisfy all feasible firm deadline constraints while improving the schedulability of soft tasks.

Tumor Mutation Burden(TMB) is a quantifiable clinical indicator that can be used to predict the responses to immunotherapy of a range of tumors. However, the current DNA sequencing-based TMB measurement method represented by Whole Exome Sequencing (WES) is expensive and time-consuming, which limits its utilization in clinical practice. In this paper, we design a method through deep learning in order to predict TMB from available H&E stained whole slide images of gastrointestinal cancer. Experimental results demonstrate that our approach is capable of distinguishing high and low TMB with an AUC higher than 0.75. We further performed post-processing to improve the accuracy on both test sets to above 0.7 (0.71 accuracy for TMB-STAD and 0.77 accuracy for TMB-COAD-DX). Furthermore, the predicted low and high TMB patients with gastric and colon cancer have different survival rates, with p values of 0.348 and 0.8113, respectively, which indicates that our study is potentially helpful for practical treatment.

The algorithm in this paper is called semantic information supplementary pyramid network(SIS-net). We choose Generative Adversarial Network (GAN) as its fundamental model. SIS-net's generator imitates the feature pyramid network (FPN) structure to recycle features spanning across multiple receptive scales to restore a sharp image. However, to solve the problem caused by the phenomenon of semantic dilution in the FPN network, we have innovatively designed a semantic information supplement (SIS) mechanism. SIS mechanism contains two essential components: semantic information storage box (info-box) and feature fusion expanding. In the process of feature fusion expanding, the semantic information features coming from the info-box is supplemented to make greater use of detailed clues. In addition, SIS-net uses the intermediate layer path to extract image features in a single time to obtain a multi-scale effect. The running speed of SIS-net has obvious advantages over other algorithms, and can basically complete real-time deblurring tasks. Extensive experiments show that our SIS-net achieves both qualitative and quantitative improvements against state-of-the-art methods. The code is available at https://github.com/yimingliu123/SIS-NET.

Fault diagnosis of rolling bearing has been the focus of research. Bearing signals are often accompanied by similar information, resulting in redundancy between data. Moreover, rolling bearing is often used in situations with large background noise, so extracting the characteristic value of rolling bearing signal and removing noise from the signal are of great significance. This paper presents a fault diagnosis model combining NAdam(Natural Adaptive Moment Estimation) algorithm and improved octave convolution. First, natural exponential decay function is proposed to replace the exponential decay function for parameter updating of Adam(Adaptive Moment Estimation). Compared with the exponential decay function, the natural exponential decay function can accelerate the convergence rate of the model. The internal structure in octave convolution is then improved. The improved structure can improve feature extraction and eliminate data redundancy. Finally, the dilated gate convolution layer is used to filter and classify the data. According to the simulation test of the case western reserve university data set and laboratory power equipment data set, the accuracy rate can reach more than 98%. Experiments with variable load and signal noise ratio are carried out to verify the noise resistance and generalization performance of the proposed method.

For the planning, operation and control of multiterminal voltage source converter (VSC) based high-voltage direct current (HVDC) (VSC-MTDC) systems, an accurate power flow formulation is a key starting point. Conventional power flow formulations assume the constant frequencies for all asynchronous AC systems. Therefore, a new feature about the complex coupling relations between AC frequencies, DC voltages and the exchanged power via VSC stations cannot be characterized if VSC-MTDC systems are required to provide cross-regional frequency responses. To address this issue, this paper proposes a comprehensive frequency-dependent power flow formulation. The proposed approach takes the frequencies of asynchronous AC systems as explicit variables, and investigates the novel bus models of the interlinking buses of VSC stations. The proposed approach accommodates different operation modes and frequency droop strategies of VSC stations, and considers the power losses of VSC stations. The effectiveness and generality of the developed approach are validated by a 6-terminal VSC-HVDC test system. The test system presents the characteristics of the coexistence of numerous VSC operation modes, the absence of slack buses in both AC and DC subsystems, and diversified grid configurations such as point-to-point integration of renewable energy sources and one AC system integrated with multiple VSC stations.

Highly multiplexed imaging technology is a powerful tool to facilitate understanding cells composition and interaction in tumor microenvironment at subcellular resolution, which is crucial for both basic research and clinical applications. Imaging mass cytometry (IMC), a multiplex imaging method recently introduced, can measure up to 40 markers simultaneously in one tissue section by using a high-resolution laser with a mass cytometer. However, due to its high resolution and large number of channels, how to process and interpret the image data from IMC remains a key challenge for its further applications. Accurate and reliable single cell segmentation is the first and a critical step to process IMC image data. Unfortunately, existing segmentation pipelines either produce inaccurate cell segmentation results, or require manual annotation which is very time-consuming. Here, we developed Dice-XMBD, a Deep learnIng-based Cell sEgmentation algorithm for tissue multiplexed imaging data. In comparison with other state-of-the-art cell segmentation methods currently used in IMC, Dice-XMBD generates more accurate single cell masks efficiently on IMC images produced with different nuclear, membrane and cytoplasm markers. All codes and datasets are available at https://github.com/xmuyulab/Dice-XMBD.

The flexibility of power systems to manage variability and uncertainties is becoming increasingly important as the penetration of variable renewable energy continues to raise. Synergies from all parts of the existing system and new flexibility providers are required to balance demand and supply. However, because of fundamental limitations, existing test systems cannot fully support detailed and realistic studies. To fill this gap, this paper presents a 213-bus benchmark, called the FTS-213 system, dedicated for power system flexibility studies. This system has more than 38% of VRE penetration and includes real data at 15 minutes and 1 minute resolutions. The resources considered in the system cover all aspects of the generation, network, demand, energy storage and market. Specifically, to capture the intrinsic relationship between flexibility and renewable energy accommodation, the system contains integration grids, regional transmission grids and inter-regional tie lines. These assets reflect the full stack of utility-scale integration, transmission and consumption within and among regions. They cover multiple voltage levels, from 110 to 750 kV AC and $\pm$ 400 to $\pm$ 800 kV DC. The impacts of and the interactions between these flexibility factors are analyzed and three sets of planning results are provided.

Designing scheduling policies for multiprocessor real-time systems is challenging since the multiprocessor scheduling problem is NP-complete. The existing heuristics are customized policies that may achieve poor performance under some specific task loads. Thus, a new design pattern is needed to make the multiprocessor scheduling policies perform well under various task loads. In this paper, we investigate a new realtime scheduling policy based on reinforcement learning. For any given real-time task set, our policy can automatically derive a high performance by online learning. Specifically, we model the real-time scheduling process as a multi-agent cooperative game and propose multi-agent self-cooperative learning that overcomes the curse of dimensionality and credit assignment problems. Simulation results show that our approach can learn high-performance policies for various task/system models.

With the increase of converter-fed devices such as renewable energy sources, energy storage, electric vehicles, direct current transmissions and power electronic loads in the power system, the conventional synchronous generator (SG)-dominated power system has been gradually evolving toward a converter-dominated system. Power electronic interfaces (PEIs) can be controlled to emulate the frequency responses of SGs, but their inherent control mechanisms are quite different. As a result, the revolution of frequency regulation in the future converter-dominated power system is foreseeable from theory to practice. In this paper, the principle of frequency responses and the corresponding control strategies of PEIs are summarized. The major inherent differences between PEIs and SGs are clarified, and the distinctive releasable energy reserves and frequency response performances are revealed. The state-of-the-art frequency regulation strategies adopted by PEIs in different operation modes are reviewed. The multiple time-scale frequency behavior in converter-dominated power systems is presented. Some major technical challenges for future work are highlighted.

It is expected that billions of objects will be connected through sensors and embedded devices for pervasive intelligence in the coming era of Internet of Things (IoT). However, the performance of such ubiquitous interconnection highly depends on the supply of network resources in terms of both energy and spectrum. Librating IoT devices from the resource deficiency, we consider a green IoT network in which the IoT devices transmit data to a fusion node over multihop relaying. To achieve sustainable operation, IoT devices obtain energy from both ambient energy sources and power grid, while opportunistically access the licensed spectrum for data transmission. We formulate a stochastic problem to optimize the network utility minus the cost on on-grid energy purchasing. The problem formulation takes into account the different granularity in the changing of harvested energy, power price, and primary user activities. To address the problem, we propose a Lyapunov-based framework to decompose the problem into different time scales, based on which an online two time-scale resource allocation algorithm, is developed which determines the harvested and purchased energy in a large time scale, and the channel allocation and data collection in a small time scale. Furthermore, we analyze the required data buffer and energy buffer to support the proposed algorithm. Extensive simulation results validate the correctness of the analysis and the efficiency of the proposed algorithm.

High wind energy curtailment during winter and early spring in north China has drawn widespread concern in recent years, which, at source, results from the shortage of power system flexibility. Therefore, power system needs to consider the flexibility in planning stage. However, current flexibility metric is difficult to calculate and be applied in to the optimal planning. A non-sequential production simulation model with only input data of the cumulative probability function and/or probability density function of heat demand, electricity demand, and wind power is proposed, which is used to obtain the probability distribution of the production for generators according to least cost principle. Then the flexibility metric can be easily calculated based on the production results. Simulations were performed based on the actual data of a large wind power zone in north China. Multiple flexibility metric was evaluated and compared with the wind power curtailment, which shows the strong correlation between them. Finally, the impact of flexible resources on the flexibility metric was analyzed.

With the increase installed capacity of wind energy, conventional generation starts to be replaced by wind farms, which will results in erosion of system frequency. It is thus imperative to develop advanced wind turbine control method to maintain the frequency. Considering the calculating complication of full-order models, this paper proposed a reduced wind turbine model with optimal dynamic performance. In order to improve the performance of the whole wind farm in frequency control, this work proposes a coordinate method at constant power control model for the goal of optimal dynamic response of the wind farm, considering the differences in operation state and wind speed of each wind turbine. Finally, the correctness and effectiveness of the proposed method are validated by the simulation.

The operation risk may increase with the high penetration of wind power, but the calculation of risk indices is complex and nonlinear, which makes it difficult to incorporate the risk constraints into the conventional unit commitment (UC) model. This paper focuses on accurately handling the risk constraints instead of approximate exponential decay rules, and proposes an iteration-correction methodology to solve risked-constrained UC of power systems with wind power. The proposed method calculates the risk indices based on the PJM method, and modifies the minimum reserve demand in each iterative step if the risk indices exceed the limits. A dynamic iterative speed-up equation is given to decrease the elapsed time. The case studies based on the actual system of China are conducted to validate the effectiveness. Compared with the conventional reserve limit, the precise and optimal reserve decision is made under different risk levels, and the proposed UC has merits of less cost and lower risk.

Edge computing is envisioned as a prominent technology that provides high computing demand services by offloading computation-intensive and delay-sensitive task from mobile or Internet of Things(IoT) devices to nearby edge servers. However, more and more edge servers are deployed near the terminal devices. The uneven distribution of devices will lead to insufficient resource utilization of edge servers, so the social benefits of the edge computing system decrease. In this paper, we propose an effective task offloading strategy in the scenario of multi-users and multi-edge servers. Terminal devices broadcast task offloading request to edge servers, and the edge servers compete for tasks to improve their resource utilization. We use a non-cooperative game to describe the competition of edge servers, and model an optimization problem as the multi-edge servers resource allocation problem. We then design an iterative algorithm to solve the optimization problem and prove that the problem has an unique Nash equilibrium. Simulation results show that the proposed offloading strategy not only improves the resource utilization of edge servers, but also guarantees the demand of terminal devices.

With the increasing penetration level of renewable energy in power system, system inertia drops significantly, leading to a new challenge in frequency security. How to utilize the capability of wind plants in frequency regulation and incorporate it into the daily dispatch is not yet well addressed. In this paper, we proposed a frequency constrained unit commitment(UC) model with wind plants, combining different response strategies of wind plants and incorporating them into the UC model. A multi-stage integrated strategy is proposed with the combination of temporary response and persistent response of wind plants together with inertia and primary response of synchronous generators. Then a UC model with the incorporation of frequency dynamics and the response strategy is formulated. Piecewise Linearization and big-M methods are introduced to convert the nonconvex constraints into linear constraints. The numerical study validates the proposed method and shows the benefit of the proposed strategy in the aspects of wind integration and cost saving.

With the development of single-cell RNA sequencing (scRNA-seq) technology, it has become possible to perform large-scale transcript profiling for tens of thousands of cells in a single experiment. Many analysis pipelines have been developed for data generated from different high-throughput scRNA-seq platforms, bringing a new challenge to users to choose a proper workflow that is efficient, robust and reliable for a specific sequencing platform. Moreover, as the amount of public scRNA-seq data has increased rapidly, integrated analysis of scRNA-seq data from different sources has become increasingly popular. However, it remains unclear whether such integrated analysis would be biassed if the data were processed by different upstream pipelines. In this study, we encapsulated seven existing high-throughput scRNA-seq data processing pipelines with Nextflow, a general integrative workflow management framework, and evaluated their performance in terms of running time, computational resource consumption and data analysis consistency using eight public datasets generated from five different high-throughput scRNA-seq platforms. Our work provides a useful guideline for the selection of scRNA-seq data processing pipelines based on their performance on different real datasets. In addition, these guidelines can serve as a performance evaluation framework for future developments in high-throughput scRNA-seq data processing.

News recommendation is an important technique for personalized news service. Compared with product and movie recommendations which have been comprehensively studied, the research on news recommendation is much more limited, mainly due to the lack of a high-quality benchmark dataset. In this paper, we present a large-scale dataset named MIND for news recommendation. Constructed from the user click logs of Microsoft News, MIND contains 1 million users and more than 160k English news articles, each of which has rich textual content such as title, abstract and body. We demonstrate MIND a good testbed for news recommendation through a comparative study of several state-of-the-art news recommendation methods which are originally developed on different proprietary datasets. Our results show the performance of news recommendation highly relies on the quality of news content understanding and user interest modeling. Many natural language processing techniques such as effective text representation methods and pre-trained language models can effectively improve the performance of news recommendation. The MIND dataset will be available at https://msnews.github.io.

Battery energy storage system (BESS) is one of the important solutions to improve the accommodation of large-scale grid connected photovoltaic (PV) generation and increase its operation economy. However, the strong intra-day volatility and severe curtailment of PV power sets a high demand of BESS charge-rate that is a key factor in operation models but ignored in current planning researches. This paper proposes a BESS capacity configuration model for PV generation systems which takes BESS’s ability to (dis)charge exceeds its rated power into account. The best charge-rate and power & energy capacity of BESS are optimized by particle swarm optimization (PSO) algorithm. Through an analysis of the annual output statistics of PV power station in the northwest of China, the results show that when considering the high charge-rate of BESS, the optimal BESS capacity configuration rises and comprehensive income of the PV-BESS system will increase.

In this paper, we propose a novel deep attention neural network. This network is designed for multi-task learning. The network is composed of multiple dual-attention layers of attention over neighbors and tasks. The neighbor attention layer represents each data point by paying attention over its neighboring data points, i.e., the output of this layer of a data point is the weighted average of its neighbors’ inputs, and the weighting scores are calculated according to the similarity between the data point and its neighbors. The task attention layer takes the output of the neighbor attention layer as input, and transfer it to multiple task-specific representations for an input data point, and uses attention mechanism to calculate the outputs for different tasks. The output of the input data points for a task is calculated by a weighted average over all the task-specific representations, and the weighting scores are based on the similarity between the target task and the other tasks. The outputs of the neighbor attention layer and task attention layer are concatenated as the output of one dual-attention. To train the parameters of the network, we minimize the classification losses and encourage the correlation among different tasks. The experiments over the benchmark of multi-task learning show the advantage of the proposed method.

In this paper, we introduce XGLUE, a new benchmark dataset to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora, and evaluate their performance across a diverse set of cross-lingual tasks. Comparing to GLUE (Wang et al.,2019), which is labeled in English and includes natural language understanding tasks only, XGLUE has three main advantages: (1) it provides two corpora with different sizes for cross-lingual pre-training; (2) it provides 11 diversified tasks that cover both natural language understanding and generation scenarios; (3) for each task, it provides labeled data in multiple languages. We extend a recent cross-lingual pre-trained model Unicoder (Huang et al., 2019) to cover both understanding and generation tasks, which is evaluated on XGLUE as a strong baseline. We also evaluate the base versions (12-layer) of Multilingual BERT, XLM and XLM-R for comparison.

Multi-terminal alternating current (AC)/direct current (DC) hybrid transmission technology is a promising alternative for the integration and transmission of large-scale wind power. Optimal power allocation (OPA), to distribute the active power between AC and DC transmission lines in order to minimise transmission power loss, is a significant problem to be solved. A new simplified OPA algorithm for multi-terminal AC/DC hybrid grids is proposed in this work. The power flow models of the AC grid and DC grid are separately established and linearised, and both AC and DC power losses are formulated in a simplified way. By doing so, the mathematical formula of the total power loss is expressed as a multivariate quadratic function, of which the minimum value can be directly obtained with the derivative method. Simulations based on a four-terminal AC/DC hybrid grid with wind power integration are carried out by using MATLAB, and the effectiveness of the proposed algorithm is validated by case studies.

Short‐term wind‐power forecasting methods like neural networks are trained by empirical risk minimization. The local optimum and overfitting problem is likely to occur in the model‐training stage, leading to the poor ability of reasoning and generalization in the prediction stage. To solve the problem, a model of short‐term wind power forecasting is proposed based on 2‐stage feature selection and a supervised random forest in the paper. First, in data preprocessing, some redundant features can be removed by a variable importance measure method and intimate samples can be selected based on relevant analysis, so that the efficiency of model training and the correlation degree between input and output samples can be enhanced. Second, an improved supervised random forest (RF) methodology is proposed to compose a new RF based on evaluating the performance of each decision tree and restructuring the decision trees. A new index of external validation in correlation with wind speed in numerical weather prediction has been proposed to overcome the shortcomings of the internal validation index that seriously depends on the training samples. The simulation examples have verified the rationality and feasibility of the improvement. Case studies of measured data from a wind farm have shown that the proposed model has a better performance than the original RF, back propagation neural network, Bayesian network, and support vector machine, in aspects of ensuring accuracy, efficiency, and robustness, and especially if there is high rate of noisy data and wind power curtailment duration in the historical data.

Taking advantage of various flexible resources to reduce or even eliminate renewable energy (RE) curtailment is an important issue in the operational planning of future power systems. Building a highly compatible flexibility evaluation method that incorporates a clear relation between flexibility indices and RE curtailment is crucial to addressing this issue. A novel flexibility evaluation methodology is proposed based on the probabilistic distribution of flexibility adequacy. The novel indices can reflect the direction, amount, frequency, and consequence of lack of flexibility and also have a linear relationship with RE curtailment, features that are beneficial for planners aiming to determine the type of flexible resources needed and to quantify their capacity. The conditional probability distribution functions of different types of flexible resources are first built. Then, a unified form of flexibility on different time-scales is proposed to represent and calculate their contributions to flexible indices using conditional probability convolution. The evaluation method is validated in a combined heat-and-electricity supply system. Four types of flexible resources are studied to demonstrate the quantitative capability of the proposed method in planning, including conventional units, electricity storage devices, heat storage devices, and electric boilers.

With the development of chip design technology, the complexity of the chip is getting bigger and bigger, and various IP (Intellectual Property) is integrated to form SoC (System on Chip). In order to improve the efficiency of designing SoC, IP reuse technology is often used, so the development of IP has a wide range of significance and use value. The SPI bus is a bus protocol often used in chip communication and has many application scenarios. Therefore, this research mainly develops the SPI bus IP. During the development process, the SPI bus design is carried out, then based on the UVM verification methodology the designed IP has been functionally verified, and finally, a fully functional SPI bus design is obtained.

VSC-HVDC plays an important role in transmitting large-scale new energy power. The fast reactive power regulation of the converter station and the unique topology of new energy cluster require special reactive power and voltage control strategy. This paper has proposed a multipriority hierarchical reactive power and voltage optimization strategy for a large-scale new energy island system delivered by VSC-HVDC. This strategy takes objectives of the dynamic reactive power margin of converter station, voltage safety margin of PCC point, reactive power coordination factor of each new energy station and system power loss into consideration, and has been verified based on the planning of Zhangbei VSC-HVDC project. The strategy can keep the dynamic reactive power margin and voltage safety margin at high level with acceptable power loss, and improve resolving speed at the same time.

The high-speed promotion of renewable energy bring about problems such as the construction of power grids lags behind that of renewable energy and there are not enough flexibility resources installed near the renewable generation stations. These lead to the inevitable situation that, besides consumed by local load, a proportion of renewable power need to be transmitted to external power systems through the local grid, which would result in the impact of renewable energy on the whole system. To investigate this impact on the system in such a situation, we propose a perspective of grid function for analysis. The functions of local load supply and external power transmission are explained and distinguished based on the power flow tracing method. The indicators of the coupling of the two function layers are proposed, considering both grid structural characteristics and coupling consequences. In addition, an economic dispatch model in AC/DC hybrid system considering system's mitigation effect on volatility is built as an evaluation method. The case study demonstrates the distinction of function layers and the influence factors such as penetration level of renewable generation and structure of the grid are investigated.

Mobile Edge Computing pushes the rich cloud resources to the edge of the network, solving some shortcomings of Cloud Computing. In the coming era of fifth generation (5G), mobile edge computing is a emergency platform to offer high experience to user. However, there are some limitation in mobile edge computing, i.e., the limitation of resources of edge servers. We consider a mobile edge computing system, consisting of smart mobile devices and edge servers. Each smart mobile device receives task and computes task at local, which its computation resource cannot completely compute task at one time slot. So each smart mobile device sends tasks to several edge servers. We formulate a stochastic problem to minimize the energy consumption and propose a Lyapunov-based framework to decompose the problem into two subproblems which determines the CPU-cycle frequency and the transmitted task in each time slots. Based on this, we developed a minimum energy consumption algorithm(ME) for mobile edge computing system. Extensive simulation results demonstrate the efficiency of our algorithm.

Under the background of extensive integration of fluctuating renewable energy sources, the coordination of multiple power sources poses many challenges to power system planning. Hydro generation has the advantages of cleanness, flexibility, and low cost. In particular, hydro generation plants with reservoir can provide peaking auxiliary services for power systems with fluctuating renewable energy. This paper proposes a probabilistic production simulation method considering operation performance of cascade hydropower stations for system planning and consumption assessment in the medium and long-term time scale. A probabilistic characterization model for the load and various power sources is established firstly, and the operational characteristics of hydro generation and the volatility of renewable energy are also taken into consideration in the improved probabilistic production simulation. The coupling relationship between renewable energy and other power sources is analyzed in detail to solve system planning problem and renewable energy accommodation. Based on the actual data of the northern China, the simulation results verify the effectiveness of the algorithm.

The explosive growth of wind power is accompanied with the problem of disorderly construction, which is difficult to take into consideration in planning stage and would lead to high cost of power grid expansion. To cope with the uncertainty of wind power construction, this paper proposed a coordinated flexible planning method based on the bi-level optimization model. A probabilistic method is used to analyze uncertainty of wind power. Considering variable flexible resources from generation, grid, demand side and energy storage, a bi-level optimization model is built for planning, with the upper layer making investment decisions considering both wind accommodation benefit and economic cost and lower layer simulating operation status in detail and maximizing flexibility supply. A maximum net benefit/increment ratio method is used to solve the model, basing on the unified evaluation indicator of different flexible resources. Case study shows that the bi-level model and the planning method can take wind power construction uncertainty into consideration and obtain an economic and credible planning result, thus achieving efficient accommodation of renewable energy.

Under the vision of high proportion renewable energy power system in the future, the renewable energy (RE) such as wind power/PV with variation and uncertainty, as the main energy supplier, brings great flexibility demand to the power system. Typical model considering only one type of flexible resource and the power planning method based on the net load envelope will no longer applicable in such new complex scenarios. This paper introduces flexibility index to quantitatively evaluate the regulatory potential of the source-load-storage multi-type flexible resources and incorporate into the planning model as an optimal decision variable. Thus, a high-proportion RE planning model with the goal of minimizing the cost of full-cycle investment is established. And through three process modules of production simulation, flexibility evaluation and power investment decision making, the optimal power planning scheme of best flexibility performance can be realized. Finally, case study is proceed basing on the 2050 long term planning of a provincial power grid in North east China, and above model and algorithm is verified.

Automatic voltage control (AVC) has been widely used in power grid dispatching system. With the high penetration of renewable generation, the reactive power uncertainty of source and load increases the voltage fluctuation, which brings new challenges to AVC. The traditional AVC strategy is based on the linear reactive voltage sensitivity method. When the system topology changes and the model update speed is not fast enough, the accuracy of AVC will be affected. This paper proposes a method for fitting the nonlinear relationship of reactive power and voltage based on historical data by Radial Basis Function (RBF) Neural Network, which can get more precise control even without precise parameters of model. We firstly obtain the voltage and reactive data of each node of the system with the inputs of the known generation and load data based on the simulation system. And then fit the nonlinear relationship between reactive power and voltage, which would replace the traditional secondary voltage control strategy. A 38 nodes sample system are applied to verify the control effects of the proposed method.

Wind power curve is a key tool to characterize wind power output feature, and is also the basis of wind power planning and operation research. The wind power curve is a high dimension matric data with local property. So it’s a vital task to find an effective method to reduce dimension of the curve. In this paper, the latest techniques of artificial intelligence and deep learning are introduced to probe a new method for reducing the dimension of wind power curve. The convolutional autoencoder of typical deep learning framework is redesigned, and it learns feature representation from massive history data. The experiment result shows that the proposed autoencoder is better fit the wind power curve dimensionality reduction study.

Photovoltaic (PV) is a crucial power source in future clean power system, which must be included in the system planning. But the correlation between variable PV power and loads is not involved in traditional planning method. This paper proposed a modified probabilistic production simulation (PPS) method considering the joint probability of PV output and load, especially the conditional dependence of them. The new PPS contains three parts of photovoltaic and load correlation characteristics modeling, photovoltaic probability calculation, and verification of production simulation method. Numerical study is finally proceeded, and the results verified the effectiveness and accuracy of the designed algorithm.

In this paper, a simple end face type magnetic encoder has been designed. This paper analyzed the negative effect caused by the calculation method based on the arctangent arithmetic. In addition, the negative effects caused by mechanical installation are illustrated in this paper. In order to solve these problems, a angle error compensation tabulation method has been proposed, and cross-intervals linear interpolation method has been presented to eliminate the noise signal in angle compensation table. The validity of the method proposed in this paper has been verified by experimental and simulation results. The precision of this kind of magnetic encoder could reach 0.02°.

Purpose: We previously developed an age-scalable 3D computational phantom that has been widely used for retrospective whole-body dose reconstructions of conventional two-dimensional historic radiation therapy (RT) treatments in late effects studies of childhood cancer survivors. This phantom is modeled in the FORTRAN programming language and is not readily applicable for dose reconstructions for survivors treated with contemporary RT whose treatment plans were designed using computed tomography images and complex treatment fields. The goal of this work was to adapt the current FORTRAN model of our age-scalable computational phantom into Digital Imaging and Communications in Medicine (DICOM) standard so that it can be used with any treatment planning system (TPS) to reconstruct contemporary RT. Additionally, we report a detailed description of the phantom’s age-based scaling functions, information that was not previously published. Method: We developed a Python script that adapts our phantom model from FORTRAN to DICOM. To validate the conversion, we compared geometric parameters for the phantom modeled in FORTRAN and DICOM scaled to ages 1 month, 6 months, 1, 2, 3, 5, 8, 10, 15, and 18 years. Specifically, we calculated the percent differences between the corner points and volume of each body region and the normalized mean square distance (NMSD) between each of the organs. In addition, we also calculated the percent difference between the heights of our DICOM age-scaled phantom and the heights (50th percentile) reported by the World Health Organization (WHO) and Centers for Disease Control and Prevention (CDC) for male and female children of the same ages. Additionally, we calculated the difference between the organ masses for our DICOM phantom and the organ masses for two reference phantoms (from International Comission on Radiation Protection (ICRP) 89 and the University of Florida/National Cancer Institute reference hybrid voxel phantoms) for ages newborn, 1, 5, 10, 15 and adult. Lastly, we conducted a feasibility study using our DICOM phantom for organ dose calculations in a commercial TPS. Specifically, we simulated a 6 MV photon right-sided flank field RT plan for our DICOM phantom scaled to age 3.9 years; treatment field parameters and age were typical of a Wilms tumor RT treatment in the Childhood Cancer Survivor Study. For comparison, the same treatment was simulated using our in-house dose calculation system with our FORTRAN phantom. The percent differences (between FORTRAN and DICOM) in mean dose and percent of volume receiving dose ≥5 Gy were calculated for two organs at risk, liver and pancreas. Results: The percent differences in corner points and the volumes of head, neck, and trunk body regions between our phantom modeled in FORTRAN and DICOM agreed within 3%. For all of the ages, the NMSDs were negliglible with a maximum NMSD of 7.80 × 10 − 2 mm for occiptital lobe of 1 month. The heights of our age-scaled phantom agreed with WHO/CDC data within 7% from infant to adult, and within 2% agreement for ages 5 years and older. We observed that organ masses in our phantom are less than the organ masses for other reference phantoms. Dose calculations done with our in-house calculation system (with FORTRAN phantom) and commercial TPS (with DICOM phantom) agreed within 7%. Conclusion: We successfully adapted our phantom model from the FORTRAN language to DICOM standard and validated its geometric consistency. We also demonstrated that our phantom model is representative of population height data for infant to adult, but that the organ masses are smaller than in other reference phantoms and need further refinement. Our age-scalable computational phantom modeled in DICOM standard can be scaled to any age at RT and used within a commercial TPS to retrospectively reconstruct doses from contemporary RT in childhood cancer survivors.

Under the background of increasing renewable energy penetration and rigid demand for midterm generation planning, the accurate expression of wind power uncertainty becomes more and more important. Firstly, this paper analyses the problem of scenario generation models based on traditional Generative Adversarial Networks(GAN), point that the fluctuation of scenarios that it generated usually deviates greatly from the real one. And further proposes a convolutional structure, that called Temporal-correlation Enhancement block (TE block), which can solve the aforementioned problem by enhance the temporal correlation perception ability of convolutional layers. Then, the problems of traditional conditional Wasserstein GAN-Gradient Penalty(WGAN-GP) in mid-scale scenario generation are discussed, and a conditional WGAN-GP scenario generation model that is suitable for mid-term scenario generation is presented. This model-free non-parametric model can generate a large number of realistic wind scenarios efficiently according to the given conditions. In order to validation, we use the data collected in a province in northern China to train the model in the Case Study part, and compare it with the traditional model. Result shows the fluctuation distribution deviation problem is improved obviously on the model with TE block, and in the comparison of auto-correlation coefficient, the proposed model also outperform the traditional model. This verifies the superiority of the proposed model in temporal expression ability compared with the traditional model, as well as the feasibility of the mid-term wind scenario generation.

55GW distributed photovoltaic have been installed in China, but nearly half are connected to the low voltage level of 380V, without real-time power data acquisition. The sequential power data is needed to be reconstructed based on some related monitoring data. Current researches focus on outliers recovery, but not reconstruction from none. This paper explores the temporal and spatial correlation of power between adjacent centralized photovoltaic stations and proposes a large-scale missing power data reconstructing method based on the time-delay power correlation, the spatial geometric characteristics of stations and the thought of ensemble learning. Finally, we verify the effectiveness of the proposed method by simulation based on the real photovoltaic power data. The proposed method can get the better effect of data reconstructing compared with the traditional method, which only use the power curves of the nearest CP station to reconstruct the power curves of the DP station according the capacity conversion.

Medium and long-term weather sequence forecast becomes unreliable beyond two weeks since the weather is a chaotic system. Using values of same months for electricity prediction of wind power is the usual method. This approach defaults wind power output with annual cycle law. However, the periodic pattern can be very complicated in fact with multiple time scales. This paper proposes an approach with multi-scale periodic pattern considered. The application of parametric estimation on cumulative distribution function avoids the difficulty of predicting the power curve. Meteorological condition is considered to some extent via multi-scale periodic pattern explored basing on historical energy data. This work is an exploration for medium and long-term wind power forecasting that can well adapt to existing conditions. It has better prediction accuracy than the method without multi-scale periodicity considered.

The energy stored in the rotor of wind turbines (WTs) and VSC-HVDC capacitors could be utilized if large offshore wind farms (OWFs) through VSC-HVDC transmission are required to provide frequency support to the onshore AC system. This paper proposes a communication-free adaptive coordinated control strategy to allow both OWFs and VSC-HVDC to contribute to the frequency regulation in the onshore AC grid. By means of the proposed approach, the frequency extremes and rate of change of frequency in the onshore grid are reduced, and the offshore system frequency deviation is eliminated, which cannot be achieved by droop control-based strategies in previous studies. The VSC-HVDC is also shown to be able to coordinate with OWFs in the proposed strategy to avoid second frequency drop (SFD) during WT’s rotor speed restoration. The effectiveness of the proposed strategy is verified in case study.