Due to the lack of flexible interconnection devices,power imbalances between networks cannot be relieved effectively.Meanwhile,increasing the penetration of distributed generators exacerbates the temporal power imbala...Due to the lack of flexible interconnection devices,power imbalances between networks cannot be relieved effectively.Meanwhile,increasing the penetration of distributed generators exacerbates the temporal power imbalances caused by large peak-valley load differences.To improve the operational economy lowered by spatiotemporal power imbalances,this paper proposes a two-stage optimization strategy for active distribution networks(ADNs)interconnected by soft open points(SOPs).The SOPs and energy storage system(ESS)are adopted to transfer power spatially and temporally,respectively.In the day-ahead scheduling stage,massive stochastic scenarios against the uncertainty of wind turbine output are generated first.To improve computational efficiency in massive stochastic scenarios,an equivalent model between networks considering sensitivities of node power to node voltage and branch current is established.The introduction of sensitivities prevents violations of voltage and current.Then,the operating ranges(ORs)of the active power of SOPs and the state of charge(SOC)of ESS are obtained from models between networks and within the networks,respectively.In the intraday corrective control stage,based on day-ahead ORs,a receding-horizon model that minimizes the purchase cost of electricity and voltage deviations is established hour by hour.Case studies on two modified ADNs show that the proposed strategy achieves spatiotemporal power balance with lower cost compared with traditional strategies.展开更多
Cloud Computing(CC)is the preference of all information technology(IT)organizations as it offers pay-per-use based and flexible services to its users.But the privacy and security become the main hindrances in its achi...Cloud Computing(CC)is the preference of all information technology(IT)organizations as it offers pay-per-use based and flexible services to its users.But the privacy and security become the main hindrances in its achievement due to distributed and open architecture that is prone to intruders.Intrusion Detection System(IDS)refers to one of the commonly utilized system for detecting attacks on cloud.IDS proves to be an effective and promising technique,that identifies malicious activities and known threats by observing traffic data in computers,and warnings are given when such threatswere identified.The current mainstream IDS are assisted with machine learning(ML)but have issues of low detection rates and demanded wide feature engineering.This article devises an Enhanced Coyote Optimization with Deep Learning based Intrusion Detection System for Cloud Security(ECODL-IDSCS)model.The ECODL-IDSCS model initially addresses the class imbalance data problem by the use of Adaptive Synthetic(ADASYN)technique.For detecting and classification of intrusions,long short term memory(LSTM)model is exploited.In addition,ECO algorithm is derived to optimally fine tune the hyperparameters related to the LSTM model to enhance its detection efficiency in the cloud environment.Once the presented ECODL-IDSCS model is tested on benchmark dataset,the experimental results show the promising performance of the ECODL-IDSCS model over the existing IDS models.展开更多
低频减载(under frequency load shedding,UFLS)是防止电力系统频率崩溃的有效手段之一,它通过在系统的某些地点切除过负荷量,达到维护系统稳定的目的。为此,计及负荷的电压调节效应,提出一种改进的功率不平衡量计算方法,针对不同的扰...低频减载(under frequency load shedding,UFLS)是防止电力系统频率崩溃的有效手段之一,它通过在系统的某些地点切除过负荷量,达到维护系统稳定的目的。为此,计及负荷的电压调节效应,提出一种改进的功率不平衡量计算方法,针对不同的扰动在线计算系统的减载总量;并利用邻接矩阵的稀疏性,结合邻接矩阵和比例分配原则提出了一种潮流追踪新算法,提高了潮流追踪算法的计算效率,将其应用于在线确定减载地点与分配减载量。仿真结果表明,新的自适应减载策略可靠性更高,能够有效防止欠切或过切,改善受扰系统减载后的频率恢复效果,并提高系统的电压稳定水平,对实际电力系统紧急控制研究具有重要意义。展开更多
基金supported by the Science and Technology Project of State Grid Corporation of China(No.5400-202199281A-0-0-00)。
文摘Due to the lack of flexible interconnection devices,power imbalances between networks cannot be relieved effectively.Meanwhile,increasing the penetration of distributed generators exacerbates the temporal power imbalances caused by large peak-valley load differences.To improve the operational economy lowered by spatiotemporal power imbalances,this paper proposes a two-stage optimization strategy for active distribution networks(ADNs)interconnected by soft open points(SOPs).The SOPs and energy storage system(ESS)are adopted to transfer power spatially and temporally,respectively.In the day-ahead scheduling stage,massive stochastic scenarios against the uncertainty of wind turbine output are generated first.To improve computational efficiency in massive stochastic scenarios,an equivalent model between networks considering sensitivities of node power to node voltage and branch current is established.The introduction of sensitivities prevents violations of voltage and current.Then,the operating ranges(ORs)of the active power of SOPs and the state of charge(SOC)of ESS are obtained from models between networks and within the networks,respectively.In the intraday corrective control stage,based on day-ahead ORs,a receding-horizon model that minimizes the purchase cost of electricity and voltage deviations is established hour by hour.Case studies on two modified ADNs show that the proposed strategy achieves spatiotemporal power balance with lower cost compared with traditional strategies.
基金The Deanship of Scientific Research(DSR)at King Abdulaziz University(KAU),Jeddah,Saudi Arabia has funded this project,under grant no.KEP-1-120-42.
文摘Cloud Computing(CC)is the preference of all information technology(IT)organizations as it offers pay-per-use based and flexible services to its users.But the privacy and security become the main hindrances in its achievement due to distributed and open architecture that is prone to intruders.Intrusion Detection System(IDS)refers to one of the commonly utilized system for detecting attacks on cloud.IDS proves to be an effective and promising technique,that identifies malicious activities and known threats by observing traffic data in computers,and warnings are given when such threatswere identified.The current mainstream IDS are assisted with machine learning(ML)but have issues of low detection rates and demanded wide feature engineering.This article devises an Enhanced Coyote Optimization with Deep Learning based Intrusion Detection System for Cloud Security(ECODL-IDSCS)model.The ECODL-IDSCS model initially addresses the class imbalance data problem by the use of Adaptive Synthetic(ADASYN)technique.For detecting and classification of intrusions,long short term memory(LSTM)model is exploited.In addition,ECO algorithm is derived to optimally fine tune the hyperparameters related to the LSTM model to enhance its detection efficiency in the cloud environment.Once the presented ECODL-IDSCS model is tested on benchmark dataset,the experimental results show the promising performance of the ECODL-IDSCS model over the existing IDS models.
文摘低频减载(under frequency load shedding,UFLS)是防止电力系统频率崩溃的有效手段之一,它通过在系统的某些地点切除过负荷量,达到维护系统稳定的目的。为此,计及负荷的电压调节效应,提出一种改进的功率不平衡量计算方法,针对不同的扰动在线计算系统的减载总量;并利用邻接矩阵的稀疏性,结合邻接矩阵和比例分配原则提出了一种潮流追踪新算法,提高了潮流追踪算法的计算效率,将其应用于在线确定减载地点与分配减载量。仿真结果表明,新的自适应减载策略可靠性更高,能够有效防止欠切或过切,改善受扰系统减载后的频率恢复效果,并提高系统的电压稳定水平,对实际电力系统紧急控制研究具有重要意义。
