Northeast Electric Power University

A community integrated energy system (CIES) with an electric vehicle charging station (EVCS) provides a new way for tackling growing concerns of energy efficiency and environmental pollution, it is a critical task to coordinate flexible demand response and multiple renewable uncertainties. To this end, a novel bi-level optimal dispatching model for the CIES with an EVCS in multi-stakeholder scenarios is established in this paper. In this model, an integrated demand response program is designed to promote a balance between energy supply and demand while maintaining a user comprehensive satisfaction within an acceptable range. To further tap the potential of demand response through flexibly guiding users energy consumption and electric vehicles behaviors (charging, discharging and providing spinning reserves), a dynamic pricing mechanism combining time-of-use and real-time pricing is put forward. In the solution phase, by using sequence operation theory (SOT), the original chance-constrained programming (CCP) model is converted into a readily solvable mixed-integer linear programming (MILP) formulation and finally solved by CPLEX solver. The simulation results on a practical CIES located in North China demonstrate that the presented method manages to balance the interests between CIES and EVCS via the coordination of flexible demand response and uncertain renewables.

By modeling the uncertainty of spinning reserves provided by energy storage with probabilistic constraints, a new optimal scheduling mode is proposed in this paper for minimizing the operating costs of an isolated microgrid (MG) by using chance-constrained programming. The model is transformed into a readily solvable mixed integer linear programming formulation in general algebraic modeling system (GAMS) via a proposed discretized step transformation approach and finally solved by applying the CPLEX solver. By properly setting the confidence levels of the spinning reserve probability constraints, the MG operation can achieve a tradeoff between reliability and economy. The test results on the modified Oak Ridge National Laboratory Distributed Energy Control and Communication lab MG test system reveal that the proposal significantly exceeds the commonly used hybrid intelligent algorithm with much better and more stable optimization results and significantly reduced calculation times.

The complex refractive indices of polymers have important applications in the analysis of their components and the study of radiation endothermic mechanisms. Since these materials have high transmittance in the visible to near-infrared ranges, it is difficult to accurately measure their complex refractive indices. At present, the data for complex refractive indices of polymers are seriously lacking, which greatly limits the applications of these materials in the field of thermal radiation. In this work, spectroscopic ellipsometry (SE) combined with the ray tracing method (RTM) is used to measure the complex refractive indices of five polymers, polydimethylsiloxane, poly(methyl methacrylate) (PMMA), polycarbonate, polystyrene, and polyethylene terephthalate, in the spectral range of 0.4-2 µm. The double optical pathlength transmission method (DOPTM) is used to measure the complex refractive indices of three polymers, PMMA, polyvinyl chloride, and polyetherimide, in the 0.4-2 µm range. The complex refractive index of PMMA measured by the DOPTM almost coincides with the data measured by SE combined with the RTM. The results show that the trends of the complex refractive indices spectra for the seven polymers in the 0.4-2 µm range are similar. This work makes up for the lack of complex refractive indices in the 0.4-2 µm range for these seven materials and points out the direction for accurate measurements of the complex refractive indices of polymers with weak absorption.

The ‘You Only Look Once’ v3 (YOLOv3) method is among the most widely used deep learning-based object detection methods. It uses the k-means cluster method to estimate the initial width and height of the predicted bounding boxes. With this method, the estimated width and height are sensitive to the initial cluster centers, and the processing of large-scale datasets is time-consuming. In order to address these problems, a new cluster method for estimating the initial width and height of the predicted bounding boxes has been developed. Firstly, it randomly selects a couple of width and height values as one initial cluster center separate from the width and height of the ground truth boxes. Secondly, it constructs Markov chains based on the selected initial cluster and uses the final points of every Markov chain as the other initial centers. In the construction of Markov chains, the intersection-over-union method is used to compute the distance between the selected initial clusters and each candidate point, instead of the square root method. Finally, this method can be used to continually update the cluster center with each new set of width and height values, which are only a part of the data selected from the datasets. Our simulation results show that the new method has faster convergence speed for initializing the width and height of the predicted bounding boxes and that it can select more representative initial widths and heights of the predicted bounding boxes. Our proposed method achieves better performance than the YOLOv3 method in terms of recall, mean average precision, and F1-score.

Internal curing is the process by which the hydration of cement occurs because of the availability of additional internal water that is not part of the mixing water. An equation has been developed in a previous study for calculating how much lightweight aggregate (LWA) is needed to supply water for internal curing of any given concrete mixture. This paper presents refinements for estimating the parameters of this equation that will provide a readily recognized means of choosing the proper amount of LWA and improving mixture proportioning. The two major factors to be considered are the variation of chemical shrinkage of cement with Portland cement phase composition and curing temperature, and the relevant value for the absorption of the LWA. In order to determine the amount of LWA needed, it is recommended that the mass composition of the cement clinker be obtained from either a detailed scanning electron microscope/X-ray image analysis or the Bogue calculation. Then, the expected chemical shrinkage of the cement at 25 deg C should be calculated. If the expected average curing temperature is above 25 deg C, the calculated value should be decreased by 0.0005 per deg C above 25 deg C. If the expected average curing temperature is below 25 deg C, the calculated value should be increased by 0.0005 per deg C below 25 deg C. The desorption of the LWA from a saturated state down to a relative humidity of relevance for the internal curing of concrete should then be measured. Finally, the determined values for chemical shrinkage and absorption of LWA should be substituted in the original equation to obtain the desired mass of lightweight fine aggregate in the concrete mixture.

As an important cyber-physical system (CPS), smart grid is highly vulnerable to cyber attacks. Amongst various types of attacks, false data injection attack (FDIA) proves to be one of the top-priority cyber-related issues and has received increasing attention in recent years. However, so far little attention has been paid to privacy preservation issues in the detection of FDIAs in smart grids. Inspired by federated learning, a FDIA detection method based on secure federated deep learning is proposed in this paper by combining Transformer, federated learning and Paillier cryptosystem. The Transformer, as a detector deployed in edge nodes, delves deep into the connection between individual electrical quantities by using its multi-head self-attention mechanism. By using federated learning framework, our approach utilizes the data from all nodes to collaboratively train a detection model while preserving data privacy by keeping the data locally during training. To improve the security of federated learning, a secure federated learning scheme is designed by combing Paillier cryptosystem with federated learning. Through extensive experiments on the IEEE 14-bus and 118-bus test systems, the effectiveness and superiority of the proposed method are verified.

Lignin represents the most abundant source of renewable aromatic resources, and the depolymerization of lignin has been identified as a prominent challenge to produce low-molecular-mass aromatic chemicals. Herein, we report a nanostructured MoOx/CNT, which can serve as an efficient catalyst in hydrogenolysis of enzymatic mild acidolysis lignins (EMALs) derived from various lignocellulosic biomass, thus giving monomeric phenols in high yields (up to 47 wt %). This catalyst showed high selectivity toward phenolic compounds having an unsaturated substituent, because the cleavage of C–O bonds in β-O-4 units is prior to reduction of double bonds by MoOx/CNT under a H2 atmosphere, which was confirmed by examination of lignin model compound reactions. The effects of some key parameters such as the influence of solvent, temperature, reaction time, and catalyst recyclability were also examined in view of monomer yields and average molecular weight. This method constitutes an economically responsible pathway for lignin valorization, which is comparable to the performance of precious-metal catalytic systems in terms of activity, reusability, and biomass feedstock compatibility.

In order to reduce the negative impact of the uncertainty of load and renewable energies outputs on microgrid operation, an optimal scheduling model is proposed for isolated microgrids by using automated reinforcement learning-based multi-period forecasting of renewable power generations and loads. Firstly, a prioritized experience replay automated reinforcement learning (PER-AutoRL) is designed to simplify the deployment of deep reinforcement learning (DRL)-based forecasting model in a customized manner, the single-step multi-period forecasting method based on PER-AutoRL is proposed for the first time to address the error accumulation issue suffered by existing multi-step forecasting methods, then the prediction values obtained by the proposed forecasting method are revised via the error distribution to improve the prediction accuracy; secondly, a scheduling model considering demand response is constructed to minimize the total microgrid operating costs, where the revised forecasting values are used as the dispatch basis, and a spinning reserve chance constraint is set according to the error distribution; finally, by transforming the original scheduling model into a readily solvable mixed integer linear programming via the sequence operation theory (SOT), the transformed model is solved by using CPLEX solver. The simulation results show that compared with the traditional scheduling model without forecasting, this approach manages to significantly reduce the system operating costs by improving the prediction accuracy.

Short-term load is a variable affected by many factors. It is difficult to forecast accurately with a single model. Taking advantage of the autoregressive integrated moving average (ARIMA) to forecast the linear basic part of load and of the support vector machines (SVMs) to forecast the non-linear sensitive part of load, a method based on hybrid model of ARIMA and SVMs is presented in this paper. It firstly uses ARIMA to forecast the daily load, and then uses SVMs, which is known for the great power to learn and generalize, to correct the deviation of former forecasting. Applying this hybrid model to a large sample prediction, the results show that it achieves the forecasting accuracy and has very good prospective in applications. So it can be used as a new load forecasting method.

As an important energy storage device, lithium-ion batteries (LIBs) have been widely used in various fields due to their remarkable advantages. The high level of precision in estimating the battery’s state of health greatly enhances the safety and dependability of the application process. In contrast to traditional model-based prediction methods that are complex and have limited accuracy, data-driven prediction methods, which are considered mainstream, rely on direct data analysis and offer higher accuracy. Therefore, this paper reviews how to use the latest data-driven algorithms to predict the SOH of LIBs, and proposes a general prediction process, including the acquisition of datasets for the charging and discharging process of LIBs, the processing of data and features, and the selection of algorithms. The advantages and limitations of various processing methods and cutting-edge data-driven algorithms are summarized and compared, and methods with potential applications are proposed. Effort was also made to point out their application methods and application scenarios, providing guidance for researchers in this area.

Abstract Bismuth is a promising anode material for state‐of‐the‐art rechargeable batteries due to its high theoretical volumetric capacity and relatively low working potential. However, its charge storage mechanism is unclear, hindering further improvement of the cell performance. Here, using in situ transmission electron microscopy and X‐ray diffraction techniques as well as theoretical analysis, it is found that a large anisotropic volume expansion of 142% occurs along the z ‐axis largely due to the alloy reaction during sodiation, significantly reducing the electrochemical performance of bismuth electrodes. To address this problem, ultrathin few‐layer bismuthene with a large aspect ratio is rationally synthesized, and can relieve the expansion strain along the z ‐axis. A free‐standing bismuthene/graphene composite electrode with tunable thickness achieves a strikingly stable and high areal sodium storage capacity of 12.1 mAh cm −2 , which greatly exceeds that of most reported electrode materials. The clarification of the charge storage mechanism and the superior areal capacity achieved should facilitate the development of bismuth‐based high‐performance anodes for practical electrochemical energy‐storage applications.

Histone modifications influence higher-order chromatin structures at individual epigenomic states and chromatin environments to regulate gene expression. However, genome-wide higher-order chromatin structures shaped by different histone modifications remain poorly characterized. With stochastic optical reconstruction microscopy (STORM), we characterized the higher-order chromatin structures at their epigenomic states, categorized into three major types in interphase: histone acetylation marks form spatially segregated nanoclusters, active histone methylation marks form spatially dispersed larger nanodomains, and repressive histone methylation marks form condensed large aggregates. These distinct structural characteristics are also observed in mitotic chromosomes. Furthermore, active histone marks coincide with less compact chromatin and exhibit a higher degree of co-localization with other active marks and RNA polymerase II (RNAP II), while repressive marks coincide with densely packed chromatin and spatially distant from repressive marks and active RNAP II. Taken together, super-resolution imaging reveals three distinct chromatin structures at various epigenomic states, which may be spatially coordinated to impact transcription.

Scenario generation is a fundamental and crucial tool for decision-making in power systems with high-penetration renewables. Based on big historical data, in this article, a novel federated deep generative learning framework, called Fed-LSGAN, is proposed by integrating federated learning and least square generative adversarial networks (LSGANs) for renewable scenario generation. Specifically, federated learning learns a shared global model in a central server from renewable sites at network edges, which enables the Fed-LSGAN to generate scenarios in a privacy-preserving manner without sacrificing the generation quality by transferring model parameters, rather than all data. Meanwhile, the LSGANs-based deep generative model generates scenarios that conform to the distribution of historical data through fully capturing the spatial-temporal characteristics of renewable powers, which leverages the least squares loss function to improve the training stability and generation quality. The simulation results demonstrate that the proposal manages to generate high-quality renewable scenarios and outperforms the state-of-the-art centralized methods. Besides, an experiment with different federated learning settings is designed and conducted to verify the robustness of our method.

Developing iron-based catalysts with superior activity and stability is a long-term goal for peroxymonosulfate (PMS) activation in advanced oxidation processes. Combining the confined interlayer growth strategy with melt infiltration under dry-chemical conditions, we successfully synthesized ultrathin 2D Fe3O4 nanosheets with a monolayer thickness of about 1 nm. Atomic force microscopy, CS-corrected high-resolution transmission electron microscopy, X-ray photoelectron spectroscopy, X-ray absorption fine structure, etc. jointly revealed that the 2D Fe3O4 nanosheets possessed special graphene-like morphology and enriched oxygen vacancies. As highly efficient AOP catalysts, a series of refractory organic pollutants, including phenolic compounds, antibiotics, and pharmaceuticals, were degraded and mineralized effectively via the activation of PMS. On the basis of radical quenching experiments, electrochemical analysis, and theory calculations, the radical generation (·OH and SO4·–) and mediated electron transfer were verified to be key mechanisms in the reaction. The oxygen vacancy-rich ultrathin 2D Fe3O4 mediated the electron transfer between pollutions and oxidants, prompted the redox cycle of Fe3O4, and remarkably lowered the energy barrier for interfacial charge transfer. This work could generate 2D metal oxides nanosheets with sufficient oxygen vacancies in a large scale, leading the insight for boosting the activity of iron-based catalysts.

Polyketides represent an important class of biologically active and structurally diverse compounds in nature. They are synthesized from acyl-coenzyme A substrates by polyketide synthases (PKSs). PKSs are classified into three groups: types I, II, and III. This article introduces recent studies on type III PKSs identified from plants, bacteria, and fungi, and describes the catalytic functions of these enzymes in detail. Plant type III PKSs have been widely studied, as exemplified by chalcone synthase, which plays an important role in the synthesis of plant metabolites. Bacterial type III PKSs fall into five groups, many of which were identified from Streptomyces, a genus that has been well known for its production of bioactive molecules and genetic alterability. Although it was believed that type III PKSs exist exclusively in plants and bacteria, recent fungal genome sequencing projects and biochemical studies revealed the presence of type III PKSs in filamentous fungi, which provides a new chance to study fungal secondary metabolism and synthesize "unnatural" natural products. Type III PKSs have been used for the biosynthesis of novel molecules through precursor-directed and structure-based mutagenesis approaches.

This essay performs a reliability constraint stochastic model for unit commitment problem by considering generation and transmission constraints with high wind penetration and volatility of load demands. This query is expressed as a MILP that is based on the linear direct current model. The proposed approach models uncertainty of wind generators output power, load demand fluctuations and stochastic elements outage of the system like generators and transmission lines. In this paper, stochastic interdependence between random variables like wind speed and load demand is recognized. To establish the probability distribution of these correlated random variables, Copula theory is applied. Correlation structure between wind speed of different locations and a group of loads existing in the same area is investigated and studied based on historical data. For representing these uncertainties in the stochastic unit commitment problem, possible scenarios are generated with Monte Carlo simulation method. The reliability constraints are utilized in each scenario to evaluate the feasibility of solutions from a reliability point. The introduced stochastic UC is executed on the RTS 96‐bus test system. Numerical results demonstrate the advantages of implementing stochastic programming on the UC problem by taking into account the intermittent behavior of wind energy and load inconstancy.

The evolutionary game theory method has been widely used in the research works about different kinds of energy utilization fields, especially the clean energy utilizations which can facilitate the sustainable energy development. This paper presents a research review of the evolutionary game theory (EGT)-based studies on different energy-related aspects, including the traditional energy utilizations, energy saving and carbon emission reduction, new energy utilizations, new energy vehicles, electric power market, distributed energy systems, micro-grid, smart grid and energy storage. Typical research works based on the evolutionary game theory method and relevant algorithms are introduced and summarized. To promote the sustainable developments of energy utilization technologies as well as the usage of evolutionary game theory method, typical existing problems and several general recommendations about the further EGT-based research works are also proposed. The potential further works based on the evolutionary game theory approach may include the EGT-based studies considering more complex initial conditions and influential factors, verifications of the applicabilities and feasibilities of the evolutionary game theory method based on practical examples, research works with evolutionary game models consisting of more participants, and research and development (R&D) works of new EGT-based or hybrid algorithms.

The prediction accuracy of short-term load forecast (STLF) depends on prediction model choice and feature selection result. In this paper, a novel random forest (RF)-based feature selection method for STLF is proposed. First, 243 related features were extracted from historical load data and the time information of prediction points to form the original feature set. Subsequently, the original feature set was used to train an RF as the original model. After the training process, the prediction error of the original model on the test set was recorded and the permutation importance (PI) value of each feature was obtained. Then, an improved sequential backward search method was used to select the optimal forecasting feature subset based on the PI value of each feature. Finally, the optimal forecasting feature subset was used to train a new RF model as the final prediction model. Experiments showed that the prediction accuracy of RF trained by the optimal forecasting feature subset was higher than that of the original model and comparative models based on support vector regression and artificial neural network.

The community-integrated energy system (CIES) is an essential energy internet carrier that has recently been the focus of much attention. A scheduling model based on chance-constrained programming is proposed for integrated demand response (IDR) enabled CIES in uncertain environments to minimize the system operating costs, where an IDR program is used to explore the potential interaction ability of electricity–gas–heat flexible loads and electric vehicles. Moreover, power to gas (P2G) and microgas turbine (MT), as the links of multienergy carriers, are adopted to strengthen the coupling of different energy subsystems. Sequence operation theory and linearization methods are employed to transform the original model into a solvable mixed-integer linear programming model. The simulation results on a practical CIES in North China demonstrate an improvement in the CIES operational economy via the coordination of IDR and renewable uncertainties, with P2G and MT enhancing the system operational flexibility and user comprehensive satisfaction. The CIES operation is able to achieve a tradeoff between the economy and system reliability by setting a suitable confidence level for the spinning reserve constraints. Besides, the proposed solution method outperforms the hybrid intelligent algorithm in terms of both optimization results and calculation efficiency.

Low fault diagnosis accuracy in case of the insufficient and imbalanced samples is a major problem in the wind turbine fault diagnosis. The imbalance of samples refers to the large difference in the number of samples of different categories, or the lack of a certain fault sample, which requires good learning of the characteristics of a small number of samples. Sample generation in the deep learning generation model can effectively solve this problem. In this study, we proposed a novel multi-class wind turbine bearing fault diagnosis strategy based on the conditional variational generative adversarial network (CVAE-GAN) model combining multi-source signals fusion. This strategy converts multi-source one-dimensional vibration signals into two-dimensional signals, and the multi-source two-dimensional signals were fused by using wavelet transform. The CVAE-GAN model was developed by merging the variational auto-encoder (VAE) with the generative adversarial network (GAN). The VAE encoder was introduced as the front end of the GAN generator. The sample label was introduced as the model input to improve the model’s training efficiency. Finally, the sample set was used to train encoder, generator and discriminator in the CVAE-GAN model to supplement the number of the fault samples. In the classifier, the sample set is used to do experimental analysis under various sample circumstances. The results show that the proposed strategy can increase wind turbine bearing fault diagnostic accuracy in complex scenarios.