查看更多>>摘要：The research on reliability evaluation of an inte-grated energy system(IES)is of great significance to system planning and operations.The differences of multiple energy subsystems must be considered in reliability evaluation of an IES,in which energy quality differences of various energy resources is critical.Current reliability evaluation of an IES cannot uniformly evaluate the reliability of multiple energy subsystems due to neglecting the energy quality differences of various energy resources.To address this problem,a novel reliability evaluation method for IESs based on exergy is proposed for the first time in this paper.The exergy of an energy resource or a substance is a measure of its usefulness,quality or potential to cause change.The models of exergy not supplied minimization and exergy efficiency maximization are proposed to alleviate energy capacity deficiency and transmission component overload in the reliability evaluation of an IES.These two models are compared to analyze exergy efficiency for the proposed method.The energy supply priority strategy of an IES is proposed considering energy quality differences of various energy resources,in which electricity,gas and heating/cooling subsystems are supplied in an orderly manner.Furthermore,a reliability evaluation indices system of an IES based on exergy is proposed in this paper.An extensive case study on an actual IES demonstrates the feasibility and effectiveness of the proposed reliability evaluation method.
查看更多>>摘要：Hydrogen is becoming an important candidate for replacing fossil fuels in the future energy economy.As such,efficient hydrogen supply chain system planning and the promo-tion of its large-scale civilian and commercial applications are needed.To identify research gaps in the field,this study reviews models and methods for planning hydrogen supply chain systems that have been employed over the past 15 years.Significant decision aspects and objectives in the hydrogen supply system are presented.Thereafter,problem types,modeling techniques and solution methods are analyzed and classified based on their individual characteristics.Finally,potential directions promoted by economic factors are recommended for future research.
查看更多>>摘要：In order to improve the data quality,the big data cleaning method for distribution networks is studied in this paper.First,the Local Outlier Factor(LOF)algorithm based on DBSCAN clustering is used to detect outliers.However,due to the difficulty in determining the LOF threshold,a method of dynamically calculating the threshold based on the transformer districts and time is proposed.In addition,the LOF algorithm combines the statistical distribution method to reduce the misjudgment rate.Aiming at the diversity and complexity of data missing forms in power big data,this paper has improved the Random Forest imputation algorithm,which can be applied to various forms of missing data,especially the blocked missing data and even some completely missing horizontal or vertical data.The data in this paper are from real data of 44 transformer districts of a certain 10 kV line in a distribution network.Experimental results show that outlier detection is accurate and suitable for any shape and multidimensional power big data.The improved Random Forest imputation algorithm is suitable for all missing forms,with higher imputation accuracy and better model stability.By comparing the network loss prediction between the data using this data cleaning method and the data removing outliers and missing values,it can be found that the accuracy of network loss prediction has improved by nearly 4％using the data cleaning method identified in this paper.Additionally,as the proportion of bad data increased,the difference between the prediction accuracy of cleaned data and that of uncleaned data is more significant.
查看更多>>摘要：The competition among renewable power producers(RPPs)may cause the cleared power of RPPs to be less than the bidding power,while the impact of competition is neglected in the existing price-taker methods.To overcome the above deficiency,this paper develops an optimal bidding strategy,considering the competition among RPPs.First,a bivariate stochastic optimization(BSO)model for a bidding strategy is proposed by considering the variable power output of RPPs and the competition among RPPs.Particularly,the cleared power estimated by the demand-supply ratio is a random variable in the proposed BSO model.Then,the Newton method and particle swarm optimization(PSO)are combined to solve the BSO model in which various probability distribution functions(PDFs)of renewable energy generation are considered.Finally,the effectiveness of the proposed method is verified based on the results of a case study,which shows that the proposed model performed better than the traditional chance-constrained programming(CCP)model in power market competition.
查看更多>>摘要：With the development of energy internet technology,the operational optimization of regional electricity-heating-gas systems is becoming a key research area.Considering that the thermal system's quasi-dynamic characteristics will make the dispatching of regional multi-energy systems more accurate,the flexibility and energy efficiency of electricity-heating-gas system's operations can be improved.The quasi-dynamic characteristics of regional thermal networks are analyzed here,as well as the demand side of the heating system.Based on thermal inertia characteristics,the virtual thermal energy storage models of both thermal networks and buildings,considering the thermal comfort index,are formulated synthetically for a central heating system.By comparing the operating results of different adjustment methods,a quality adjustment method is found to have the most flexibility for regional heating system control.With the hot water supply network and heating load modeling considering virtual storage,an operational optimization model for a regional electricity-heating-gas system is presented with the purpose of reducing the total operating costs.Numerical results show the effectiveness of the proposed method.Through a case study,it is found that when considering the virtual energy storage specialty for both the heating supply and demand side,the heating load can be shifted across the time periods under a time-of-use(TOU)price,leading to the obvious economic improvement of multi-energy system operation.
查看更多>>摘要：Although integrated energy systems(IES)are cur-rently modest in size,their scheduling faces strong challenges,stemming from both wind generation disturbances and the system's complexity,including intrinsic heterogeneity and pro-nounced non-linearity.For this reason,a two-stage algorithm called the Multi-Objective Group Search Optimizer with Pre-Exploration(MOGSOPE)is proposed to efficiently achieve the optimal solution under wind generation disturbances.The opti-mizer has an embedded trainable surrogate model,Deep Neural Networks(DNNs),to explore the common features of the multi-scenario search space in advance,guiding the population toward a more efficient search in each scenario.Furthermore,a multi-scenario Multi-Attribute Decision Making(MADM)approach is proposed to make the final decision from all alternatives in different wind scenarios.It reflects not only the decision-maker's(DM)interests in other indicators of IES but also their risk preference for wind generation disturbances.A case study conducted in Barry Island shows the superior convergence and diversity of MOGSOPE in comparison to other optimization algorithms.With respect to numerical performance metrics HV,IGD,and SI,the proposed optimizer exhibits improvements of 3.1036％,4.8740％,and 4.2443％over MOGSO,and 4.2435％,6.2479％,and 52.9230％over NSGAⅡ,respectively.What's more,the effectiveness of the multi-scenario MADM in making final decisions under uncertainty is demonstrated,particularly in optimal scheduling of IES under wind generation disturbances.
查看更多>>摘要：To further improve the utilization level of distributed photovoltaics(PV)and realize low-carbon travel,a novel opti-mization cooperative model that considers multi-stakeholders in-cluding integrated prosumers(IP),power to hydrogen(P2H),and grid company(GC)is proposed,based on the Nash bargaining theory.Electricity trading among the three and hydrogen trading between IP and P2H is considered in the model to maximize their own interests.Specifically,IP focus on distributed PV integration and low carbon travels,P2H strives to improve cost competitiveness of hydrogen,and electricity-hydrogen trading prices and quantities are optimized between them.Also,GC obtains profits by charging grid fees considering power flow constraints.The cooperation game model is transformed into a mixed-integer linear programming problem through linearization methods.Case studies show that electricity-hydrogen trading among the three has apparent advantages over pure electricity coupling or non-cooperation.With future reduction of investment prices of PV and P2H and maturity of the carbon trading market,the profit margin of this cooperation model can be further enhanced.
查看更多>>摘要：With the access to large amounts of renewable energy sources(RES),operation uncertainty of distribution networks increases significantly.Fortunately,adopting advanced information and communication technology,a cyber-physical distribution network(CPDS)provides the possibility to solve this problem via aggregative management of decentralized control-lable loads.However,information flow in cyber space deeply interacts with energy flow in physical space,leading to a complexity in modeling,design and analysis of the whole control process.To deal with this problem,a general hybrid flow model of CPDS is first proposed in this paper.In this model,the control process is abstracted into interactions among three types of cyber nodes through cyber branches.The mathematic model of cyber nodes and branches is developed as well as that of the controlled physical object for hybrid flow computation.Then,based on the hybrid model,an instantiated application to compensate feeder power deviation caused by RES fluctuation through aggregative control of large-scale air-conditioners(ACs)is investigated.In this application,coordinative control of the AC cluster is achieved through a decentralized control strategy with very little communication cost and very good privacy protection.Results of numerical examples verify the correctness and flexibility of the hybrid flow model in reflecting interactions between cyber flow and energy flow as well as system operations.The proposed decentralized control strategy of the AC cluster is also proven to be effective and robust in FCE compensation.
查看更多>>摘要：With the rapid growth of photovoltaic integration,the volatility and uncertainty of intermittent photovoltaic injec-tion will dramatically reduce system operation reliability from the generation side.The system operator may face certain financial risks brought by unexpected power failure under low operation reliability.Therefore,maintaining sufficient power reserve to meet system operation reliability and reduce risk,especially in an isolated system,is essential.However,the traditional reserve preparation strategy fails to consider the uncertainties of the power generation under the high penetration levels of emerging renewable energy resources.A novel reserve preparation strategy for an isolated system is developed in this paper using a two-stage model.In the first stage,the optimal hourly scheduling of an isolated system is determined.In the second stage,a minute level conditional value-at-risk(CVaR)based model is established where the uncertainty of the reserve requirement is introduced with the chance constraint.The proposed discretized step transformation(DST)and subtraction type convolution(STC)methods are utilized to convert the model into mixed-integer linear programming,and finally solved by applying the CPLEX solver.The IEEE 39-bus system is used as the test case to validate the feasibility and effectiveness of the proposed two-stage model.
查看更多>>摘要：With the advancement of new infrastructures,the digitalization of the substation communication network has rapidly increased,and its information security risks have become increasingly prominent.Accurate and reliable substation commu-nication network flow models and flow anomaly detection meth-ods have become an important means to prevent network security problems and identify network anomalies.The existing substation network analyzers and flow anomaly detection algorithms are usually based on threshold determination,which cannot reflect the inherent characteristics of substation automation flow based on IEC 61850 and have low detection accuracy.To effectively detect abnormal traffic,this paper fully explores the substation network traffic rules,extracts the frequency domain features of the station level network,and designs an abnormal traffic identification model based on the ResNeSt convolutional neural network.Transfer learning is used to solve the problem of insufficient abnormal traffic labeled samples in the substation.Finally,a new method of abnormal traffic detection in smart substation station level communication networks based on deep transfer learning is proposed.The T1-1 substation communica-tion network is constructed on OPNET for abnormal simulations,and the actual network traffic in a 110kV substation is fused with CIC DDoS2019 and KDD99 data sets for the algorithm performance test,respectively.The accuracy reached is 98.73％and 98.95％,indicating that the detection model proposed in this paper has higher detection accuracy than existing algorithms.
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