Two-step Optimal Allocation of Stationary and Mobile Energy Storage Systems in Resilient Distribution Networks

Energy storage systems (ESSs) are acknowledged to be a promising option to cope with issues in high penetration of renewable energy and guarantee a highly reliable power supply. In this paper, a two-step optimal allocation model is proposed to obtain the optimal allocation (location and size) of stationary ESSs (SESSs) and mobile ESSs (MESSs) in the resilient distribution networks (DNs). In the first step, a mixed-integer linear programming (MILP) problem is formulated to obtain the preselected location of ESSs with consideration of different scenarios under normal operation conditions. In the second step, a two-stage robust optimization model is established to get the optimal allocation results of ESSs under failure operation conditions which are solved by column-and-constraint generation (C&CG) algorithm. A hybrid ESS allocation strategy based on the subjective and objective weight analysis is proposed to give the final allocation scheme of SESSs and MESSs. Finally, the proposed two-step optimal allocation model is demonstrated on a modified IEEE 33-bus system to show its effectiveness and merits.

Parallel naive Bayes algorithm for large-scale Chinese text classification based on spark

The sharp increase of the amount of Internet Chinese text data has significantly prolonged the processing time of classification on these data.In order to solve this problem,this paper proposes and implements a parallel naive Bayes algorithm(PNBA)for Chinese text classification based on Spark,a parallel memory computing platform for big data.This algorithm has implemented parallel operation throughout the entire training and prediction process of naive Bayes classifier mainly by adopting the programming model of resilient distributed datasets(RDD).For comparison,a PNBA based on Hadoop is also implemented.The test results show that in the same computing environment and for the same text sets,the Spark PNBA is obviously superior to the Hadoop PNBA in terms of key indicators such as speedup ratio and scalability.Therefore,Spark-based parallel algorithms can better meet the requirement of large-scale Chinese text data mining.

Multi-stage Defender-Attacker-Defender Model for Distribution System Resilience Enhancement in Ice Storms with Line Hardening,Mobile Device and Repair Crew Dispatching

This paper proposes a co-optimal strategy using line hardening,mobile devices(mobile ice-melting device,mobile emergency generator,mobile energy storage system),and repair crew dispatching to improve distribution system resilience during ice storms.A multi-stage defender-attacker-defender model is established to take into account interactions and coupling relationships between different measures.In our proposed model,ice storms will attack the distribution and transportation system in a worst-case scenario,affecting system performance from various perspectives.Corresponding to the different operating states in the distribution system affected by ice storms,aiming at minimizing the weighted load shedding value,this paper applies various measures to different stages to improve the response and defense capabilities to ice storms and realize restoration of the distribution system ultimately.The nested column-and-constraint generation algorithm is used to solve the model efficiently.The effectiveness of the proposed model and solution method for enhancing the distribution system resilience is verified on the modified IEEE 33-bus distribution system and modified realworld zone of Caracas 141-bus distribution system.

Distributed Resilient Fusion Filtering for Nonlinear Systems with Random Sensor Delays:Optimized Algorithm Design and Boundedness Analysis

This paper is concerned with the distributed resilient fusion filtering(DRFF)problem for a class of time-varying multi-sensor nonlinear stochastic systems(MNSSs)with random sensor delays(RSDs).The phenomenon of the RSDs is modeled by a set of random variables with certain statistical features.In addition,the nonlinear function is handled via Taylor expansion in order to deal with the nonlinear fusion filtering problem.The aim of the addressed issue is to propose a DRFF scheme for MNSSs such that,for both RSDs and estimator gain perturbations,certain upper bounds of estimation error covariance(EEC)are given and locally minimized at every sample time.In the light of the obtained local filters,a new DRFF algorithm is developed via the matrix-weighted fusion method.Furthermore,a sufficient condition is presented,which can guarantee that the local upper bound of the EEC is bounded.Finally,a numerical example is provided,which can show the usefulness of the developed DRFF approach.

Internal Combustion Engine as a New Source for Enhancing Distribution System Resilience

Enhancing distribution system resilience is a new challenge for researchers.Supplying distribution loads,especially the residential customers and high-priority loads after disasters,is vital for this purpose.In this paper,the internal combustion engine(ICE)vehicles are firstly introduced as valuable backup energy sources in the aftermath of disasters and the use of this technology is explained.Then,the improvement of distribution system resilience is investigated through supplying smart residential customers and injecting extra power to the main grid.In this method,it is assumed that the infrastructure of distribution system is partially damaged(common cases)and it can be restored in less than one day.The extra power of residential customer can be delivered to other loads.A novel formulation for increasing the injected power of the smart home to the main grid using ICE vehicles is proposed.Moreover,the maximum backup duration in case of extensive damages in the distribution system is calculated for some commercial ICE vehicles.In this case,the smart home cannot deliver extra energy to the main grid because of its survivability.Simulation results demonstrate the effectiveness of the proposed method for increasing backup power during power outages.It is also shown that ICE vehicles can supply residential customers for a reasonable amount of time during a power outage.

Multi-stage Dynamic Post-disaster Recovery Strategy for Distribution Networks Considering Integrated Energy and Transportation Networks

To improve the resilience of distribution networks(DNs),a multi-stage dynamic recovery strategy is proposed in this paper,which is designed for post-disaster DN considering an integrated energy system(IES)and transportation network(TN).First,the emergency response quickly increases the output of gas turbines(GTs)in the natural gas network(NGN),and responsively reconfigures the DN in microgrids,to maximize the amount of loads to be restored.The single-commodity flow model is adopted to construct spanning tree constraints.Then,in the second stage of energy storage recovery,mobile energy storage systems(MESSs)are deployed to cover the shortages of power demands,i.e.,to further restore the loads after evaluating the load recovery situation.The Floyd algorithm based dynamic traffic assignment(DTA)is selected to obtain the optimal path of the MESSs.In the third stage,the outputs of various post-disaster recovery measures are adjusted to achieve an economically optimized operation.Case studies demonstrate the effectiveness of the proposed dynamic post-disaster recovery strategy.

Attack-resilient Distributed Control for Islanded Single-/three-phase Microgrids Based on Distributed Adaptive Observers

This paper investigates the power sharing and voltage regulation issues of islanded single-/three-phase microgrids(S/T-MGs)where both sources and loads are unbalanced and the presence of adversarial cyber-attacks against sensors of distributed generator(DG)units is considered.Firstly,each DG unit is modeled as a heterogeneous linear dynamic agent with disturbances caused by sources and loads,then the problem is formulated as a distributed containment control problem.After that,to guarantee satisfactory power sharing and voltage control performance asymptotically achieved for the S/T-MGs,an attack-resilient distributed secondary control approach is developed by designing a distributed adaptive observer.With this approach,the effect of the cyber-attacks can be neutralized to ensure system stability and preserve bounded voltage synchronization.Simulation results are presented to demonstrate the effectiveness of the proposed control approach.

Improving the performance of query processing using proposed resilient distributed processing technique

Purpose-Resilient distributed processing technique(RDPT),in which mapper and reducer are simplified with the Spark contexts and support distributed parallel query processing.Design/methodology/approach-The proposed work is implemented with Pig Latin with Spark contexts to develop query processing in a distributed environment.Findings-Query processing in Hadoop influences the distributed processing with the MapReduce model.MapReduce caters to the works on different nodes with the implementation of complex mappers and reducers.Its results are valid for some extent size of the data.Originality/value-Pig supports the required parallel processing framework with the following constructs during the processing of queries:FOREACH;FLATTEN;COGROUP.

Cooperative distributed state estimation:resilient topologies against smart spoofers

A network of observers is considered,where through asynchronous(with bounded delay)communications,they cooperatively estimate the states of a linear time-invariant(LTI)system.In such a setting,a new type of adversary might affect the observation process by impersonating the identity of the regular node,which is a violation of communication authenticity.These adversaries also inherit the capabilities of Byzantine nodes,making them more powerful threats called smart spoofers.We show how asynchronous networks are vulnerable to smart spoofing attack.In the estimation scheme considered in this paper,information flows from the sets of source nodes,which can detect a portion of the state variables each,to the other follower nodes.The regular nodes,to avoid being misguided by the threats,distributively filter the extreme values received from the nodes in their neighborhood.Topological conditions based on strong robustness are proposed to guarantee the convergence.Two simulation scenarios are provided to verify the results.

A Scheduling Optimization Technique Based on Reuse in Spark to Defend Against APT Attack

Advanced Persistent Threat （APT） attack, an attack option in recent years, poses serious threats to the security of governments and enterprises data due to its advanced and persistent attacking characteristics. To address this issue, a security policy of big data analysis has been proposed based on the analysis of log data of servers and terminals in Spark. However, in practical applications, Spark cannot suitably analyze very huge amounts of log data. To address this problem, we propose a scheduling optimization technique based on the reuse of datasets to improve Spark performance. In this technique, we define and formulate the reuse degree of Directed Acyclic Graphs （DAGs） in Spark based on Resilient Distributed Datasets （RDDs）. Then, we define a global optimization function to obtain the optimal DAG sequence, that is, the sequence with the least execution time. To implement the global optimization function, we further propose a novel cost optimization algorithm based on the traditional Genetic Algorithm （GA）. Our experiments demonstrate that this scheduling optimization technique in Spark can greatly decrease the time overhead of analyzing log data for detecting APT attacks.