Distributed generation (DG) can be integrated into any radial distribution network (RDN) to meet the increasing demand for electric power. Instead of integrating a single large size DG, two or more than two small DGs (multi DGs) are usually placed. These DGs must be placed in appropriate buses/nodes with suitable size to ensure better performance of the system. The purpose of this paper is to implement an established hybrid optimization algorithm to place multi DGs with suitable size at each site. The proposed hybrid ABC-CS optimization algorithm combines the behaviour of the artificial bee colony (ABC) and cuckoo search (CS) algorithms. The multi DGs are optimally placed by considering objectives like power loss, penalty function (PE) and voltage profile (VP). The outcomes of two different types of radial distribution networks obtained by the suggested method for placement of multi DGs have been correlated to that obtained by using genetic algorithm (GA), particle swarm optimization (PSO) and GA-PSO. The proposed method has also been correlated with the other published methods to indicate its strength.

This paper suggests an innovative approach for the ideal placement and categorization of capacitors in radial distribution networks (RDNs) by applying symmetric fuzzy and improved bacterial foraging optimization algorithm (IBFOA) solutions. The reactive power reimbursement significantly enhances the function of the power system, and capacitor placement is an impressive technique used to reduce loss of the system. The capacitor allocation for distribution system problems involves determining the ideal location and size of the capacitor. In this work, load flow is performed at first to compute actual losses and voltages at different nodes without compensation. In the planned technique, the loss sensitivity factor (VSF) and voltage stability index (VSI) are utilized to determine the optimal location of capacitors in RDNs. Here, the IBFOA is used to determine the proper rating of the capacitor. The suggested scheme is applied on three different types of RDNs.

An efficient distribution network must be able to supply power with good voltage profile. The main objective of the proposed work is to allocate losses of the unbalanced distribution network by the firefly algorithm in regulated and deregulated environments before and after loss minimization. Reconfiguration is one of the methods for loss reduction of unbalanced distribution network. Further, optimal placement of distributed generation and capacitor in the reconfigured unbalanced distribution network can further reduce the loss. The results of reconfigured unbalanced distribution network in regulated environment have already been reported. In this paper reconfiguration of an unbalanced distribution network in a deregulated environment is also carried out using an established Fuzzy Firefly algorithm. Loss sensitivity factor of unbalanced distribution networks is used to get the appropriate location of distributed generation and capacitor to be placed in the unbalanced distribution network. Their ratings have been found out by using bacteria foraging optimization algorithm (BFOA). The suggested loss allocation method using Firefly algorithm is implemented at first on 13 node unbalanced distribution network to check the performance of the proposed loss allocation method when compared to other available method. Finally the proposed method has been implemented on 25 node unbalanced distribution network. Both of the implementations are carried out under MATLAB environment.

Cloud computing plays a major role in sharing data and resources to other devices through data outsourcing. During sharing resources, it is a challenging task to provide access control and secure write operations. The main issue is to provide secure read and write operations collaboratively and to reduce computational overload by effective key management. In this paper, a secure and an efficient data collaboration scheme blowfish hybridized weighted attribute-based Encryption (BH-WABE ) for secure data writing and proficient access control has been proposed. Here, weight is assigned to each attribute based on its importance and data are encrypted using access control policies. The cloud service provider stores the outsourced data and an attribute authority revokes or updates the attributes by assigning different attributes based on the weight. The receiver can access the data file corresponding to its weight in order to reduce the computational overload. The proposed BH-WABE provides collusion resistance, multiauthority security and fine-grained access control in terms of security, reliability, and efficiency. The performance is compared with the conventional hybrid attribute-based encryption (HABE) scheme in terms of data confidentiality, flexible access control, data collaboration, full delegation, partial decryption, verification, and partial signing.

A growing interest in renewable energy resources has been observed for several years, due to their pollution-free nature, availability all over the world, and continuity. These facts make these energy resources attractive for many applications. In this work, the hybrid combination of a photovoltaic-biomass system is investigated as an energy source. This paper determines optimal sizing and cost reduction of grid-integrated renewable energy resources by using an intelligence optimization technique, the dragonfly algorithm. The efficiency of the proposed methodology is also compared with an existing technique, which uses the artificial bee colony (ABC) algorithm. The scope of this work is to reduce the annual total cost of power with a reduced number of solar panels. The monthly average solar radiation is used to compute the obtained power. The outcome of the proposed technique proves that the grid-connected system with an optimal number of components satisfactorily meets the needs of the village at a reduced price. The simulation results are carried out under the MATLAB environment. The comparison of results clearly demonstrates that the proposed system is much more efficient than the existing one.