Physical-Layer Secret Key Generation for Dual-Task Scenarios

Physical-layer secret key generation(PSKG)provides a lightweight way for group key(GK)sharing between wireless users in large-scale wireless networks.However,most of the existing works in this field consider only group communication.For a commonly dual-task scenario,where both GK and pairwise key(PK)are required,traditional methods are less suitable for direct extension.For the first time,we discover a security issue with traditional methods in dual-task scenarios,which has not previously been recognized.We propose an innovative segment-based key generation method to solve this security issue.We do not directly use PK exclusively to negotiate the GK as traditional methods.Instead,we generate GK and PK separately through segmentation which is the first solution to meet dual-task.We also perform security and rate analysis.It is demonstrated that our method is effective in solving this security issue from an information-theoretic perspective.The rate results of simulation are also consistent with the our rate derivation.

Stochastic optimization based on a novel scenario generation method for midstream and downstream petrochemical supply chain

A two-stage mixed integer linear programming model(MILP)incorporating a novel method of stochastic scenario generation was proposed in order to optimize the economic performance of the synergistic combination of midstream and downstream petrochemical supply chain.The uncertainty nature of the problem intrigued the parameter estimation,which was conducted through discretizing the assumed probability distribution of the stochastic parameters.The modeling framework was adapted into a real-world scale of petrochemical enterprise and fed into optimization computations.Comparisons between the deterministic model and stochastic model were discussed,and the influences of the cost components on the overall profit were analyzed.The computational results demonstrated the rationality of using reasonable numbers of scenarios to approximate the stochastic optimization problem.

Simulation-based automatic generation of risk scenarios

A methodology for automatically generating risk scenarios is presented.Its main idea is to let the system model ＂express itself＂ through simulation.This is achieved by having the simulation model driven by an elaborated simulation engine,which：（i） manipulates the generation of branch points,i.e.event occurrence times;（ii） employs a depth-first systematic exploration strategy to cover all possible branch paths at each branch point.In addition,a backtracking technique,as an extension,is implemented to recover some missed risk scenarios.A widely discussed dynamic reliability example（a holdup tank） is used to aid in the explanation of and to demonstrate the effectiveness of the proposed methodology.

Simulation and uniform design-based automatic generation of risk scenarios

This paper presents a methodology for automatically generating risk scenarios for dynamic reliability applications in which some dynamic characteristics(e.g.,the order,timing and magnitude of events,the value of relevant process parameters and initial conditions) have a significant influence on the evolution of the system.The main idea of the methodology is:(i) making the system model "express itself" through simulation by having the model driven by an elaborated simulation engine;(ii) exploiting uniform design to pick out a small subset of representative design points from the space of relevant dynamic characteristics;(iii) for each selected design point,employing a depth-first systematic exploration strategy to cover all possible scenario branches at each branch point.A highly dynamic example adapted from the literature(a chemical batch reactor) is studied to test the effectiveness of the proposed methodology.

Optimal Operation Strategy Analysis with Scenario Generation Method Based on Principal Component Analysis,Density Canopy,and K-medoids for Integrated Energy Systems

The operation of integrated energy systems(IESs)is confronted with great challenges for increasing penetration rate of renewable energy and growing complexity of energy forms.Scenario generation is one of ordinary methods to alleviate the system uncertainties by extracting several typical scenarios to represent the original high-dimensional data.This paper proposes a novel representative scenario generation method based on the feature extraction of panel data.The original high-dimensional data are represented by an aggregated indicator matrix using principal component analysis to preserve temporal variation.Then,the aggregated indicator matrix is clustered by an algorithm combining density canopy and K-medoids.Together with the proposed scenario generation method,an optimal operation model of IES is established,where the objective is to minimize the annual operation costs considering carbon trading cost.Finally,case studies based on the data of Aachen,Germany in 2019 are performed.The results indicate that the adjusted rand index(ARI)and silhouette coefficient(SC)of the proposed method are 0.6153 and 0.6770,respectively,both higher than the traditional methods,namely K-medoids,K-means++,and density-based spatial clustering of applications with noise(DBSCAN),which means the proposed method has better accuracy.The error between optimal operation results of the IES obtained by the proposed method and all-year time series benchmark value is 0.1%,while the calculation time is reduced from 11029 s to 188 s,which verifies that the proposed method can be used to optimize operation strategy of IES with high efficiency without loss of accuracy.

Renewable Scenario Generation Using Controllable Generative Adversarial Networks with Transparent Latent Space

With the growing penetration of renewable energysources in power systems, it becomes increasingly important tocharacterize their inherent variability and uncertainty. Scenariogeneration is a key approach to provide a series of possible powerscenarios in the future for the system planner and operator tomake decisions. In this paper, a data-driven method is presentedfor renewable scenario generation using stable and controllablegenerative adversarial networks with transparent latent space(ctrl-GANs). The machine learning based algorithm can capturethe nonlinear and dynamic renewable patterns without the needfor modeling assumptions and complicated sampling techniques.The orthogonal regularization and spectral normalization areadopted to improve the training stabilization of the GAN model.To control the generation process, a relationship is built betweenfeatures of the generated scenarios and latent vectors on themanifold. Moreover, several new metrics for GANs are used toevaluate the quality of the scenarios. The proposed approachis applied to generate realistic time series data of wind andphotovoltaic power. The results demonstrate that our methodhas a better performance on numerical stabilization and is ableto control the generation process with latent space.

Forecasting Scenario Generation for Multiple Wind Farms Considering Time-series Characteristics and Spatial-temporal Correlation

Scenario forecasting methods have been widely studied in recent years to cope with the wind power uncertainty problem. The main difficulty of this problem is to accurately and comprehensively reflect the time-series characteristics and spatial-temporal correlation of wind power generation. In this paper, the marginal distribution model and the dependence structure are combined to describe these complex characteristics. On this basis, a scenario generation method for multiple wind farms is proposed. For the marginal distribution model, the autoregressive integrated moving average-generalized autoregressive conditional heteroskedasticity-t (ARIMA-GARCH-t) model is proposed to capture the time-series characteristics of wind power generation. For the dependence structure, a time-varying regular vine mixed Copula (TRVMC) model is established to capture the spatial-temporal correlation of multiple wind farms. Based on the data from 8 wind farms in Northwest China, sufficient scenarios are generated. The effectiveness of the scenarios is evaluated in 3 aspects. The results show that the generated scenarios have similar fluctuation characteristics, autocorrelation, and crosscorrelation with the actual wind power sequences.

Multi-dimensional scenario forecast for generation of multiple wind farms

A novel multi-dimensional scenario forecast approach which can capture the dynamic temporal-spatial interdependence relation among the outputs of multiple wind farms is proposed.In the proposed approach,support vector machine(SVM)is applied for the spot forecast of wind power generation.The probability density function(PDF)of the SVM forecast error is predicted by sparse Bayesian learning(SBL),and the spot forecast result is corrected according to the error expectation obtained.The copula function is estimated using a Gaussian copula-based dynamic conditional correlation matrix regression(DCCMR)model to describe the correlation among the errors.And the multidimensional scenario is generated with respect to the estimated marginal distributions and the copula function.Test results on three adjacent wind farms illustrate the effectiveness of the proposed approach.

Mitigating Carbon Emissions:A Comprehensive Analysis of Transitioning to Hydrogen-Powered Plants in Japan’s Energy Landscape Post-Fukushima

One of the impacts of the Fukushima disaster was the shutdown of all nuclear power plants in Japan,reaching zero production in 2015.In response,the country started importing more fossil energy including coal,oil,and natural gas to fill the energy gap.However,this led to a significant increase in carbon emissions,hindering the efforts to reduce its carbon footprint.In the current situation,Japan is actively working to balance its energy requirements with environmental considerations,including the utilization of hydrogen fuel.Therefore,this paper aims to explore the feasibility and implications of using hydrogen power plants as a means to reduce emissions,and this analysis will be conducted using the energy modeling of the MARKAL-TIMES Japan framework.The hydrogen scenario(HS)is assumed with the extensive integration of hydrogen into the power generation sector,supported by a hydrogen import scheme.Additionally,this scenario will be compared with the Business as Usual(BAU)scenario.The results showed that the generation capacities of the BAU and HS scenarios have significantly different primary energy supplies.The BAU scenario is highly dependent on fossil fuels,while the HS scenario integrates hydrogen contribution along with an increase in renewable energy,reaching a peak contribution of 2,160 PJ in 2050.In the HS scenario,the target of reducing CO_(2) emissions by 80%is achieved through significant hydrogen penetration.By 2050,the total CO_(2) emissions are estimated to be 939 million tons for the BAU scenario and 261 million tons for the Hydrogen scenario.In addition,the contribution of hydrogen to electricity generation is expected to be 153 TWh,smaller than PV and wind power.

Scenario Generations for Renewable Energy Sources and Loads Based on Implicit Maximum Likelihood Estimations

Scenario generations for renewable energy sources and loads play an important role in the stable operation and risk assessment of integrated energy systems.This paper proposes a deep generative network based method to model time-series curves,e.g.,power generation curves and load curves,of renewable energy sources and loads based on implicit maximum likelihood estimations(IMLEs),which can generate realistic scenarios with similar patterns as real ones.After training the model,any number of new scenarios can be obtained by simply inputting Gaussian noises into the data generator of IMLEs.The proposed approach does not require any model assumptions or prior knowledge of the form in the likelihood function being made during the training process,which leads to stronger applicability than explicit density model based methods.The extensive experiments show that the IMLEs accurately capture the complex shapes,frequency-domain characteristics,probability distributions,and correlations of renewable energy sources and loads.Moreover,the proposed approach can be easily generalized to scenario generation tasks of various renewable energy sources and loads by fine-tuning parameters and structures.

Copula-Based Monte Carlo Scenarios Generation Method for STOPF Problem

This paper introduces a Monte Carlo scenario generation method based on copula theory for the stochastic optimal power flow （STOPF） problem with wind power. By using copula theory, the scenarios are simulated from multivariable joint distribution but only from their dependency matrix. Hence, the scenarios generated by proposed method can contain flail statistical information of multivariate. Here, the details of simu- lating scenarios for multi-wind-farm are explained with four steps： determine margin of one wind farm, fit the copulas, choose optimal copulas and simulate scenarios by Mote Carlo. Moreover, the producing process of scenarios is demonstrated by two adjacent actual wind farms in China. With the scenarios, the STOPF is con- verted into the same amount deterministic sub OPF models which can be solved by available technology per- fectly. Results using copula theory are compared against results from history samples based on two designs： IEEE 30-bus and IEEE 118-bus systems. The comparison results prove the accuracy of the proposed methodology.

Hybrid Scenario Generation Method for Stochastic Virtual Bidding in Electricity Market

Stochastic optimization can be used to generate optimal bidding strategies for virtual bidders in which the uncertain electricity prices are represented by using scenarios.This paper proposes a hybrid scenario generation method for electricity price using a seasonal autoregressive integrated moving average(SARIMA)model and historical data.The electricity price spikes are first identified by using an outlier detection method.Then,the historical data are decomposed into base and spike components.Next,the base and spike component scenarios are generated by using the SARIMA-and historical data-based methods,respectively.Finally,the electricity price scenarios are obtained by combining the base and spike component scenarios.Case studies are carried out for a virtual bidder in the PJM electricity market to validate the proposed method.The optimal bidding strategies of the virtual bidder are generated by solving a stochastic optimization problem using the electricity price scenarios generated by the proposed method,the SARIMA method,and a historical data-based method,respectively.Case study results show that the proposed method is better than the SARIMA method in preserving statistical properties of the electricity price in the generated scenarios and is better than the historical data-based method in predicting the future trend of the electricity price and,therefore,can help the virtual bidder earn more profit in the electricity market.

Scenario Generation for Cooling,Heating,and Power Loads Using Generative Moment Matching Networks

Scenario generations of cooling,heating,and power loads are of great significance for the economic operation and stability analysis of integrated energy systems.In this paper,a novel deep generative network is proposed to model cooling,heating,and power load curves based on generative moment matching networks(GMMNs)where an auto-encoder transforms highdimensional load curves into low-dimensional latent variables and the maximum mean discrepancy represents the similarity metrics between the generated samples and the real samples.After training the model,the new scenarios are generated by feeding Gaussian noises to the scenario generator of the GMMN.Unlike the explicit density models,the proposed GMMN does not need to artificially assume the probability distribution of the load curves,which leads to stronger universality.The simulation results show that the GMMN not only fits the probability distribution of multiclass load curves very well,but also accurately captures the shape(e.g.,large peaks,fast ramps,and fluctuation),frequency-domain characteristics,and temporal-spatial correlations of cooling,heating,and power loads.Furthermore,the energy consumption of generated samples closely resembles that of real samples.

Towards Intelligent Reflecting Surface Empowered 6G Terahertz Communications: A Survey

Terahertz(THz)communications have been widely envisioned as a promising enabler to provide adequate bandwidth and achieve ultra-high data rates for sixth generation(6G)wireless networks.In order to mitigate blockage vulnerability caused by serious propagation attenuation and poor diffraction of THz waves,an intelligent reflecting surface(IRS),which manipulates the propagation of incident electromagnetic waves in a programmable manner by adjusting the phase shifts of passive reflecting elements,is proposed to create smart radio environments,improve spectrum efficiency and enhance coverage capability.Firstly,some prospective application scenarios driven by the IRS empowered THz communications are introduced,including wireless mobile communications,secure communications,unmanned aerial vehicle(UAV)scenario,mobile edge computing(MEC)scenario and THz localization scenario.Then,we discuss the enabling technologies employed by the IRS empowered THz system,involving hardware design,channel estimation,capacity optimization,beam control,resource allocation and robustness design.Moreover,the arising challenges and open problems encountered in the future IRS empowered THz communications are also highlighted.Concretely,these emerging problems possibly originate from channel modeling,new material exploration,experimental IRS testbeds and intensive deployment.Ultimately,the combination of THz communications and IRS is capable of accelerating the development of 6G wireless networks.

Comparative Study for ECHAM5 and SDSM in Downscaling Temperature for a Geo-Climatically Diversified Region, Pakistan

Pakistan is a country with diversified features in terms of geography and climate. It is an agriculture based country, mainly dependent on Indus water system. In Pakistan, there are loftyplateaus to the north and Arabian Sea in the south, while the interior portion is covered with plateaus or agriculture plains. For such a region, any attempt to monitor/analyze climatic data requires some more specific details. A statistical software “SDSM” is utilized for downscaling daily temperature data of Pakistan and the results generated are compared with the output of a recommended model “ECHAM5”. After analysis, it revealed that comparatively SDSM produced much better results. The outputs from both the approaches were correlated with the observed data;SDSM-observed gave values for correlation coefficient R2 in the range of 81% - 94% whereas ECHAM5-observed produced 73% - 87% for different meteorological stations of Pakistan. On the basis of this study, SDSM can be recommended for future scenario generation of temperature data of Pakistan as well.

SceGAN:A method for generating autonomous vehicle cut-in scenarios on highways based on deep learning

With the increasing level of automation of autonomous vehicles,it is important to conduct comprehensive and extensive testing before releasing autonomous vehicles into the market.Traditional public road and closed-field testing failed to meet the requirements of high testing efficiency and scenario coverage.Therefore,scenario-based autonomous vehicle simulation testing has emerged.Many scenarios form the basis of simulation testing.Generating additional scenarios from an existing scenario library is a significant problem.Taking the scenarios of a proceeding vehicle cutting into an adjacent lane on highways as an example,based on an autoencoder and a generative adversarial network(GAN),a method that combines Transformer to capture the features of a long-time series,called SceGAN,is proposed to model and generate scenarios of autonomous vehicles on highways.An evaluation system is established to analyze the reliability of SceGAN using discriminative and predictive scores and further evaluate the effect of scenario generation in terms of similarity and coverage.Experiments showed that compared with TimeGAN and AEGAN,SceGAN is superior in data fidelity and availability,and their similarity increased by 27.22%and 21.39%,respectively.The coverage increased from 79.84%to 93.98%as generated scenarios increased from 2,547 to 50,000,indicating that the proposed method has a strong generalization capability for generating multiple trajectories,providing a basis for generating test scenarios and promoting autonomous vehicle testing.

Distributed Robust Optimal Dispatch for the Microgrid Considering Output Correlation between Wind and Photovoltaic

As an effective carrier of integrated clean energy,the microgrid has attracted wide attention.The randomness of renewable energies such as wind and solar power output brings a significant cost and impact on the economics and reliability of microgrids.This paper proposes an optimization scheme based on the distributionally robust optimization(DRO)model for a microgrid considering solar-wind correlation.Firstly,scenarios of wind and solar power output scenarios are generated based on non-parametric kernel density estimation and the Frank-Copula function;then the generated scenario results are reduced by K-means clustering;finally,the probability confidence interval of scenario distribution is constrained by 1-norm and∞-norm.The model is solved by a column-and-constraint generation algorithm.Experimental studies are conducted on a microgrid system in Jiangsu,China and the obtained scheduling solution turned out to be superior under wind and solar power uncertainties,which verifies the effectiveness of the proposed DRO model.

First Order Convergence Analysis for Sparse Grid Method in Stochastic Two-Stage Linear Optimization Problem

Stochastic two-stage linear optimization is an important and widely used optimization model. Efficiency of numerical integration of the second stage value function is critical. However, the second stage value function is piecewise linear convex, which imposes challenges for applying the modern efficient spare grid method. In this paper, we prove the first order convergence rate of the sparse grid method for this important stochastic optimization model, utilizing convexity analysis and measure theory. The result is two-folded: it establishes a theoretical foundation for applying the sparse grid method in stochastic programming, and extends the convergence theory of sparse grid integration method to piecewise linear and convex functions.

Extreme Scenarios Based Data-adaptive Probability Uncertainty Set for Distributionally Robust Transmission Expansion Planning

Increasing penetration of renewable energy into power systems is the development trend of future energy systems.One of the main challenges is to plan the expansion scheme of transmission systems to accommodate uncertainties of wind power.In this letter,we propose a novel extreme scenarios(ESs)based data-adaptive probability uncertainty set for the transmission expansion planning problem.First,available historical data are utilized to identify data-adaptive ESs through the convex hull technology,and the probability uncertainty set with respect to the obtained ESs is then established,from which we draw the final expansion decision based on the worst-case distribution.The proposed distributionally robust transmission expansion planning(DRTEP)model can guarantee optimality of expected cost under the worst-case distribution,while ensuring feasibility of all possible wind power generation.Simulation studies are carried out on a modified IEEE RTS 24-bus system to verify the effectiveness of the proposed DRTEP model.

Generation of a scenario library for testing driver-automation cooperation safety under cut-in working conditions

To improve the efficiency of safety tests of driver-automation cooperation,a method for generating a scenario library is proposed that considers the probability of scenario occurrence and driver-handling challenges in real driving situations.First,the original scenario data under cut-in conditions stored in a time series are extracted from the scenario data set.Then,a mathematical performance index is used to model the scenario and a significance function in terms of the occurrence frequency of the scenario,and the performance challenge between the driver and the vehicle is established.Next,the important scenario set is extracted from the original scenario set by constructing and optimizing a significance auxiliary function.Finally,the extracted important scenario sets are filtered by using the significance function values of the scenarios to generate a scenario library.Simulation results show that the proposed method for scenario library generation can effectively identify scenarios with potential adventure during driver-automation cooperation and thus accelerate safety tests compared with traditional methods.