基于新工科理念的应用化学专业创新创业能力培养体系建设

为了适应新技术、新业态的发展需要,增强学生创新知识转化能力和创新创业能力,推进辽宁省和精细化工行业的技术发展和产业升级,我校依据专业优势、地域特色及社会需求,建立了适应新工科人才培养要求的创新创业培养体系,并对应用化学专业的办学定位、人才培养目标以及教学模式、平台建设、创新创业教育进行了系统设计和全方位改革,取得了一些成效和经验。

Evolutionary Game-theoretic Modeling of Massive Distributed Renewable Energy Deployment Towards Low-carbon Distribution Networks

This paper proposes an evolutionary game-theoretic model of massive distributed renewable energy deployment in order to shed light on the self-organization sustainable developments of renewable energies in distribution networks towards low-carbon targets. Since neighboring buses can interact in terms of energy exchanges, the return matrices of individual buses in the evolutionary game are established based on profiles of loads and renewable energy generation. More specifically, an evolutionary strategy is proposed based on the return matrices for individual buses to determine whether or not to deploy renewable energies in the next round of the game. Then, a dynamic model is derived for analyzing the renewable energy penetration rate in the distribution network throughout the multi-round evolutionary game. In theory, this model can reveal the self-organization process of renewable energy deployment in the distribution network. With this model, the distribution network operator would be aided in designing the incentives for buses deploying renewable energies toward a pre-defined low-carbon target. Numerical results on an actual 141-bus system and a synthetic 2000-bus system have demonstrated the validity and efficiency of the proposed model.

Cross-Modal Hashing Retrieval Based on Deep Residual Network

In the era of big data rich inWe Media,the single mode retrieval system has been unable to meet people’s demand for information retrieval.This paper proposes a new solution to the problem of feature extraction and unified mapping of different modes:A Cross-Modal Hashing retrieval algorithm based on Deep Residual Network(CMHR-DRN).The model construction is divided into two stages:The first stage is the feature extraction of different modal data,including the use of Deep Residual Network(DRN)to extract the image features,using the method of combining TF-IDF with the full connection network to extract the text features,and the obtained image and text features used as the input of the second stage.In the second stage,the image and text features are mapped into Hash functions by supervised learning,and the image and text features are mapped to the common binary Hamming space.In the process of mapping,the distance measurement of the original distance measurement and the common feature space are kept unchanged as far as possible to improve the accuracy of Cross-Modal Retrieval.In training the model,adaptive moment estimation(Adam)is used to calculate the adaptive learning rate of each parameter,and the stochastic gradient descent(SGD)is calculated to obtain the minimum loss function.The whole training process is completed on Caffe deep learning framework.Experiments show that the proposed algorithm CMHR-DRN based on Deep Residual Network has better retrieval performance and stronger advantages than other Cross-Modal algorithms CMFH,CMDN and CMSSH.

Grid Strength Assessment for Inhomogeneous Multi-infeed HVDC Systems via Generalized Short Circuit Ratio

Generalized short circuit ratio(g SCR)for grid strength assessment of multi-infeed high-voltage direct current(MIDC)systems is a rigorous theoretical extension of the traditional SCR,which enables SCR to be extended to MIDC systems.However,g SCR is originally based on the assumption of homogeneous MIDC systems,in which all high-voltage direct current(HVDC)converters have an identical control configuration,thus presenting challenges to applications of g SCR to inhomogeneous MIDC systems.To weaken this assumption,this paper applies matrix perturbation theory to explore the possibility of utilization of g SCR into inhomogeneous MIDC systems.Results of numerical experiments show that in inhomogeneous MIDC systems,the previously proposed g SCR can still be used without modification.However,critical g SCR(Cg SCR)must be redefined by considering the characteristics of control configurations of HVDC converter.Accordingly,the difference between g SCR and redefined Cg SCR can effectively quantify the pertinent AC grid strength in terms of the static-voltage stability margin.The performance of the proposed method is demonstrated in a triple-infeed inhomogeneous line commutated converter based high-voltage direct current(LCC-HVDC)system.

Analytical and numerical analysis on the torsional performance of elliptically deformed tunnels in the longitudinal direction

Segmental lining structures constructed by shields/tunnel boring machines are often subjected to uneven longitudinal cross-section torsion as a result of eccentric external loads,which is extremely adverse to tunnel safety but has not received sufficient attention for a long time.To figure out the torsional performance of segmental tunnels,it is essential to assess the longitudinal torsional stiffness and active-torsion-rejection capability of a segmented tunnel.The aim of the paper is to derive an analytical solution to the longitudinal torsional stiffness of a segmental tunnel with existing elliptical deformation.The longitudinal torsional stiffness under different internal force combinations is deduced considering the longitudinal axial force and bending moment based on the equivalent continuous model and force balance equation.The validity of the analytical solution is verified by comparing it with finite element method results.Then,a parametric analysis,using the new analytical solution,is included to investigate the effect of the key parameters on torsional behaviors,including the segment size,the bolt size and the transverse bending stiffness,etc.It is found that:(1)the longitudinal torsional stiffness efficiency(LTSE)of the segmental tunnel decreases with the rise of segment thickness to diameter ratio but increases with the ring width to diameter ratio;(2)the LTSE reduces with the increase of the effective shear length but rises with the diameter of bolts;(3)the LTSE increases rapidly with the ratio of compression-torsion or bending-torsion.Furthermore,the envelope curve of the critical load(N0,M0)for a tunnel to actively resist a certain internal torque is given.The proposed solution can be easily utilized to determine the longitudinal torsional stiffness of segmental tunnels and is an effective tool for tunnel design and maintenance.

Multi-Time-Scale Resource Allocation Based on Long-Term Contracts and Real-Time Rental Business Models for Shared Energy Storage Systems

The push for renewable energy emphasizes the need for energy storage systems(ESSs)to mitigate the unpre-dictability and variability of these sources,yet challenges such as high investment costs,sporadic utilization,and demand mismatch hinder their broader adoption.In response,shared energy storage systems(SESSs)offer a more cohesive and efficient use of ESS,providing more accessible and cost-effective energy storage solutions to overcome these obstacles.To enhance the profitability of SESSs,this paper designs a multi-time-scale resource allocation strategy based on long-term contracts and real-time rental business models.We initially construct a life cycle cost model for SESS and introduce a method to estimate the degradation costs of multiple battery groups by cycling numbers and depth of discharge within the SESS.Subsequently,we design various long-term contracts from both capacity and energy perspectives,establishing associated models and real-time rental models.Lastly,multi-time-scale resource allocation based on the decomposition of user demand is proposed.Numerical analysis validates that the business model based on long-term contracts excels over models operating solely in the real-time market in economic viability and user satisfaction,effectively reducing battery degradation,and leveraging the aggregation effect for SESS can generate an additional increase of 10.7%in net revenue.

Cyber-secure decentralized energy management for IoT-enabled active distribution networks

This paper provides a strategic solution for enhancing the cybersecurity of power distribution system operations when information and operation technologies converge in active distribution network(ADN). The paper first investigates the significance of Internet of Things(IoT) in enabling fine-grained observability and controllability of ADN in networked microgrids. Given severe cybersecurity vulnerabilities embedded in conventionally centralized energy management schemes, the paper then proposes a cyber-secure decentralized energy management framework that applies a distributed decision-making intelligence to networked microgrids while securing their individual mandates for optimal operation. In particular,the proposed framework takes advantage of software-defined networking technologies that can secure communications among IoT devices in individual microgrids, and exploits potentials for introducing blockchain technologies that can preserve the integrity of communications among networked microgrids in ADN. Furthermore, the paperpresents the details of application scenarios where the proposed framework is employed to secure peer-to-peer transactive energy management based on a set of interoperable blockchains. It is finally concluded that the proposed framework can play a significant role in enhancing the efficiency, reliability, resilience, and sustainability of electricity services in ADN.

Intelligent data attacks against power systems using incomplete network information: a review

With the integration of information technologies, power system operations are increasingly threatened by cyber-attacks. It has even been revealed that an attacker can inject false data into real-time measurements stealthily without knowing the full configuration(e.g., network topology) of a power system. In this paper, we present a comprehensive review on false data injection attacks which utilize barrier conditions, blind identification techniques and data driven approaches to overcome limitations of incomplete network information. We also point out future research topics for facilitating the detection and prevention of such false data attacks.

Stable deep blue organic light emitting diodes with CIE of y<0.10 based on quinazoline and carbazole units

Achieving stable deep blue organic light emitting diodes(OLEDs)with narrow full width at half maximum(FWHM)and color gamut in the range of the commission International de L’Eclairage(CIE)ofy≤0.10 is still challenging in display and lighting applications.In this investigation,three donor-acceptor(D-A)deep-blue emitters were designed and synthesized via integrating asymmetric quinazoline(PQ)acceptor with weak donating carbazole(Cz)donor.The effect of the position and number of Cz group in PQunit are investigated,which is also first examples for syste matic research about the effect of different position of asymmetric PQ as acceptor on deep OLEDs.Their bandgaps of 3.12~3.19 eV and the singlet state energy levels of 3.12~3.19 eV were found to be sufficiently large to achieve deep blue light.As expected,these emitters-based OLEDs exhibit deep blue emission with the maximum wavelength≤450 nm and narrow FWHM≈60 nm.Especially,a CIE ofy=0.080 was achieved for 4 PQ-Cz-based OLED.Significantly,the deep blue electroluminescence(EL)spectra of these three emitters-based OLEDs are very stable and the corresponding CIE coordinates deviation(ΔCIE(x,y))can be negligible under the applied voltage ranging from 5 V to 9 V.

Data-driven Probabilistic Static Security Assessment for Power System Operation Using High-order Moments

In this letter,a new formulation of Lebesgue integration is used to evaluate the probabilistic static security of power system operation with uncertain renewable energy generation.The risk of power flow solutions violating any pre-defined operation security limits is obtained by integrating a semialgebraic set composed of polynomials.With the high-order moments of historical data of renewable energy generation,the integration is reformulated as a generalized moment problem which is then relaxed to a semi-definite program(SDP).Finally,the effectiveness of the proposed method is verified by numerical examples.

Nearly 100% exciton utilization in highly efficient red OLEDs based on dibenzothioxanthone acceptor

Improving the utilization of excitons has always been an important topic for the development of electroluminescence devices.In this work,we designed and synthesized three red TADF emitters TPA-DBT12,TPA-DBT3 and DTPA-DBT by employing dibenzothioxanthone(DBT)acceptor framework to stabilize the locally excited triplet state to participate in the reverse intersystem crossing(RISC)process.The fast RISC process and singlet radiation decay process gave rise to evidently enhanced exciton utilization.All of the red OLEDs based on these materials showed maximum EQE over 11% and high exciton utilization close to 100%.This work not only extend the acceptor framework for red materials but also provide a new perspective for the design of highly efficient red TADF materials with 100% exciton utilization by managing locally excited triplet state.

A modeling system for drinking water sources and its application to Jiangdong Reservoir in Xiamen city

Drinking water sources are highly valued by authorities for safeguarding the life of a city. Models are widely applied as important and effective tools in the management of water sources. However, it is difficult to apply models in water source management because water managers are often not equipped with the professional knowledge and operational skills necessary for making use of the models. This paper introduces a drinking water source simulation and prediction system that consists of a watershed model, a hydrological model and a water quality model. This system provides methods and technical guidance for the conventional management of water sources and emergency water event response. In this study, the sub-models of the system were developed based on the data of the Jiangdong Reservoir in Xiamen, and the model validation was based on local monitoring data. The hydrological model and water quality model were integrated by computer programming, and the watershed model was indirectly integrated into the system through a network platform. Furthermore, three applications for Jiangdong Reservoir water protection utilizing the system were introduced in this paper, including a conventional simulation, an emergency simulation, and an emergency measures evaluation.

Topology-independent end-to-end learning model for improving the voltage profile in microgrids-integrated power distribution networks

With multiple microgrids(MGs)integrated into power distribution networks in a distributed manner,the penetration of renewable energy like photovoltaic(PV)power generation surges.However,the operation of power distribution networks is challenged by the issues of multiple power flow directions and voltage security.Accordingly,an efficient voltage control strategy is needed to ensure voltage security against ever-changing operating conditions,especially when the network topology information is absent or inaccurate.In this paper,we propose a novel data-driven voltage profile improvement model,denoted as system-wide composite adaptive network(SCAN),which depends on operational data instead of network topology details in the context of power distribution networks integrated with multiple MGs.Unlike existing studies that realize topology identification and decisionmaking optimization in sequence,the proposed end-to-end model determines the optimal voltage control decisions in one shot.More specifically,the proposed model consists of four modules,Pre-training Network and modified interior point methods with adversarial networks(Modified IPMAN)as core modules,and discriminator generative adversarial network(Dis-GAN)and Volt convolutional neural network(Volt-CNN)as ancillary modules.In particular,the generator in SCAN is trained by the core modules in sequence so as to form an end-to-end mode from data to decision.Numerical experiments based on IEEE 33-bus and 123-bus systems have validated the effectiveness and efficiency of the proposed method.

Analysis on Impact of Rumors on Electricity Market Operations with Volatile Renewables

In recent years,rumors have been shown to have a significant impact on individual and societal activities.As renewables play an increasingly significant role in electricity markets,certain rumors may deviate the bidding behavior of market entities and eventually affect the performance of market operations.In this study,we attempt to reveal the general threats caused by rumors in the context of day-ahead electricity markets considering the integration of volatile renewables.First,we model the propagation of rumors in the societal system considering the weight of propagation resistance,which principally reflects the communication accessibility of market entities.Second,we develop an integrated two-layer network model to uncover the inherent coupling mechanism between market operations and rumor propagation.In particular,the role of electricity market operations on rumor propagation is characterized by changes in the truthfulness of rumors associated with electricity prices.The rumors,in turn,affect the bidding quantities of market entities in electricity market operations.Finally,numerical experiments are conducted on modified IEEE 6-bus and 118-bus systems.The results demonstrate the potential threats of rumors to electricity market operations with different penetration levels of renewables.