The static var compensator (SVC) is a cost-effective device in flexible AC transmission system (FACTS) family.We introduce an improved artificial hummingbird algorithm (IAHA) for optimal allocation of SVCs in distribu...The static var compensator (SVC) is a cost-effective device in flexible AC transmission system (FACTS) family.We introduce an improved artificial hummingbird algorithm (IAHA) for optimal allocation of SVCs in distribution networks to maximize energy efficiency.Three loading levels (low,medium,and high) per day are investigated.The proposed IAHA is evaluated on the IEEE 33-bus distribution network (DN) and 69-bus DN.The proposed IAHA demonstrates notable improvements in cost savings and voltage profile compared with the conventional artificial hummingbird algorithm (AHA).In addition,it enhances energy savings across various loading conditions and outperforms the conventional AHA in both best and average performance metrics.Although raising the compensation limit initially increases cost savings,the benefits decrease beyond a threshold,highlighting the importance of balancing the compensation levels for maximum efficiency.展开更多
Clustering approaches are one of the probabilistic load flow(PLF)methods in distribution networks that can be used to obtain output random variables,with much less computation burden and time than the Monte Carlo simu...Clustering approaches are one of the probabilistic load flow(PLF)methods in distribution networks that can be used to obtain output random variables,with much less computation burden and time than the Monte Carlo simulation(MCS)method.However,a challenge of the clustering methods is that the statistical characteristics of the output random variables are obtained with low accuracy.This paper presents a hybrid approach based on clustering and Point estimate methods.In the proposed approach,first,the sample points are clustered based on the𝑙-means method and the optimal agent of each cluster is determined.Then,for each member of the population of agents,the deterministic load flow calculations are performed,and the output variables are calculated.Afterward,a Point estimate-based PLF is performed and the mean and the standard deviation of the output variables are obtained.Finally,the statistical data of each output random variable are modified using the Point estimate method.The use of the proposed method makes it possible to obtain the statistical properties of output random variables such as mean,standard deviation and probabilistic functions,with high accuracy and without significantly increasing the burden of calculations.In order to confirm the consistency and efficiency of the proposed method,the 10-,33-,69-,85-,and 118-bus standard distribution networks have been simulated using coding in Python®programming language.In simulation studies,the results of the proposed method have been compared with the results obtained from the clustering method as well as the MCS method,as a criterion.展开更多
基金Prince Sattam bin Abdulaziz University for funding this research work through the project number PSAU/2024/01/31685.
文摘The static var compensator (SVC) is a cost-effective device in flexible AC transmission system (FACTS) family.We introduce an improved artificial hummingbird algorithm (IAHA) for optimal allocation of SVCs in distribution networks to maximize energy efficiency.Three loading levels (low,medium,and high) per day are investigated.The proposed IAHA is evaluated on the IEEE 33-bus distribution network (DN) and 69-bus DN.The proposed IAHA demonstrates notable improvements in cost savings and voltage profile compared with the conventional artificial hummingbird algorithm (AHA).In addition,it enhances energy savings across various loading conditions and outperforms the conventional AHA in both best and average performance metrics.Although raising the compensation limit initially increases cost savings,the benefits decrease beyond a threshold,highlighting the importance of balancing the compensation levels for maximum efficiency.
文摘Clustering approaches are one of the probabilistic load flow(PLF)methods in distribution networks that can be used to obtain output random variables,with much less computation burden and time than the Monte Carlo simulation(MCS)method.However,a challenge of the clustering methods is that the statistical characteristics of the output random variables are obtained with low accuracy.This paper presents a hybrid approach based on clustering and Point estimate methods.In the proposed approach,first,the sample points are clustered based on the𝑙-means method and the optimal agent of each cluster is determined.Then,for each member of the population of agents,the deterministic load flow calculations are performed,and the output variables are calculated.Afterward,a Point estimate-based PLF is performed and the mean and the standard deviation of the output variables are obtained.Finally,the statistical data of each output random variable are modified using the Point estimate method.The use of the proposed method makes it possible to obtain the statistical properties of output random variables such as mean,standard deviation and probabilistic functions,with high accuracy and without significantly increasing the burden of calculations.In order to confirm the consistency and efficiency of the proposed method,the 10-,33-,69-,85-,and 118-bus standard distribution networks have been simulated using coding in Python®programming language.In simulation studies,the results of the proposed method have been compared with the results obtained from the clustering method as well as the MCS method,as a criterion.
