Nonparametric Probabilistic Prediction of Regional PV Outputs Based on Granule-based Clustering and Direct Optimization Programming

Regional photovoltaic(PV) power prediction plays an important role in power system planning and operation. To effectively improve the performance of prediction intervals(PIs) for very short-term regional PV outputs, an efficient nonparametric probabilistic prediction method based on granulebased clustering(GC) and direct optimization programming(DOP) is proposed. First, GC is proposed to formulate and cluster the sample granules consisting of numerical weather prediction(NWP) and historical regional output data, for the enhanced hierarchical clustering performance. Then, to improve the accuracy of samples' utilization, an unbalanced extension is used to reconstruct the training samples consisting of power time series. After that, DOP is applied to quantify the output weights based on the optimal overall performance. Meanwhile, a balance coefficient is studied for the enhanced reliability of PIs. Finally, the proposed method is validated through multistep PIs based on the numerical comparison of real PV generation data.

Integrated Optimization of Smart Home Appliances with Cost-effective Energy Management System

Smart grid enables consumers to control and sched-ule the consumption pattern of their appliances,minimize energy cost,peak-to-average ratio(PAR)and peak load demand.In this paper,a general architecture of home energy management system(HEMS)is developed in smart grid scenario with novel restricted and multi-restricted scheduling method for the residen-tial customers.The optimization problem is developed under the time of use pricing(TOUP)scheme.To optimize the formulated problem,a powerful meta-heuristic algorithm called grey wolf optimizer(GWO)is utilized,which is compared with particle swarm optimization(PSO)algorithm to show its effectiveness.A rooftop photovoltaic(PV)system is integrated with the system to show the cost effectiveness of the appliances.For analysis,eight different cases are considered under various time scheduling algorithms.

An Algorithm for the Protection of Distribution Feeders Using the Stockwell and Hilbert Transforms Supported Features

Faults’recognition in the distribution feeders(DFs)is extremely important for improving the reliability of the distribution system.Therefore,this paper proposes a technique to identify the faults on the DF using the Stockwell Transform(ST)dependent variance feature and Hilbert transform(HT)by utilizing current signals.By element to element multiplication of the H-index,we compute using HT aided decompositions of current waveforms and VS-index,and calculate through ST aided decomposition of current waveforms.By utilizing the decision rules,various faults are classified.Different faults studied in this work are line to ground,double line,double line to ground and 3-Φto ground.For high fault impedance,this technique is effectively utilized.Furthermore,variations in the fault incidence angles are also utilized to test the performance of the proposed technique.To perform the proposed algorithm,a IEEE-13 bus system is developed in MATLAB/Simulink software.The algorithm effectively classified the faults with accuracy greater than 98%.The algorithm is also successfully validated on the IEEE-34 bus test system.Furthermore,the algorithm was successfully validated on the practical power system network.It is recognized that the developed method performed better than the discrete Wavelet transform(DWT)and ruled decision tree based protection scheme reported in various literature.

Performance Improvement of Very Short-term Prediction Intervals for Regional Wind Power Based on Composite Conditional Nonlinear Quantile Regression

Accurate regional wind power prediction plays an important role in the security and reliability of power systems.For the performance improvement of very short-term prediction intervals(PIs),a novel probabilistic prediction method based on composite conditional nonlinear quantile regression(CCNQR)is proposed.First,the hierarchical clustering method based on weighted multivariate time series motifs(WMTSM)is studied to consider the static difference,dynamic difference,and meteorological difference of wind power time series.Then,the correlations are used as sample weights for the conditional linear programming(CLP)of CCNQR.To optimize the performance of PIs,a composite evaluation including the accuracy of PI coverage probability(PICP),the average width(AW),and the offsets of points outside PIs(OPOPI)is used to quantify the appropriate upper and lower bounds.Moreover,the adaptive boundary quantiles(ABQs)are quantified for the optimal performance of PIs.Finally,based on the real wind farm data,the superiority of the proposed method is verified by adequate comparisons with the conventional methods.

Comprehensive Overview of Multi-agent Systems for Controlling Smart Grids

Agents are intelligent entities that act flexibly and autonomously and make wise decisions based on their intelligence and experience.A multi-agent system(MAS)contains multiple,intelligent,and interconnected collaborating agents for solving a problem beyond the ability of a single agent.A smart grid(SG)combines advanced intelligent systems,control techniques,and sensing methods with an existing utility power network.For controlling smart grids,various control systems with different architectures have already been developed.MAS-based control of power system operations has been shown to overcome the limitations of time required for analysis,relaying,and protection;transmission switching;communication protocols;and management of plant control.These systems provide an alternative for fast and accurate power network control.This paper provides a comprehensive overview of MASs used for the control of smart grids.The paper provides a wide-spectrum view of the status of smart grids,MAS-based control techniques and their implementation for the control of smart grids.Use of MASs in the control of various aspects of smart grids-including the management of energy,marketing energy,pricing,scheduling energy,reliability,network security,fault handling capability,communication between agents,SG-electrical vehicles,SG-building energy systems,and soft grids—have been critically reviewed.More than a hundred publications on the topic of MAS-based control of smart grids have been critically examined,classified,and arranged for fast reference.

Robust Switch Selection in Radial Distribution Systems Using Combinatorial Optimization

Selecting the best type of equipment among available switches with different prices and reliability levels is a significant challenge in distribution system planning.In this paper,the optimal type of switches in a radial distribution system is selected by considering the total cost and reliability criterion and using the weighted augmented epsilon constraint method and combinatorial optimization.A new index is calculated to assess the robustness of each Pareto solution.Moreover,for each failure,repair time is considered based on historical data.Monte Carlo simulations are used to consider the switch failure uncertainty and fault repair time uncertainty in the model.The proposed framework is applied to an RTBS Bus-2 test system.Furthermore,the model is also applied to an industrial system to verify the proposed method’s excellent performance in larger practical engineering problems.