<abstract>
<p>System islanding, relay tripping, and reverse power flow-like issues in the distribution network are all caused by randomly placed distributed energy resources. To minimize such problems, distributed energy resource (DER) optimal placement in the radial distribution network (RDN) is essential to reduce power loss and enhance the voltage profile. When placing DERs, consideration of constraints like size, location, number, type, and power factor (PF) should be considered. For optimal placement, renewable and nonrenewable DERs are considered. The effects of different types and PFs of DER placements have been tested on the IEEE 33 bus RDN to satisfy all limitations. Using various intelligent techniques, distributed energy resource units of optimal type, PF, size, quantity, and position were placed in the IEEE 33 bus RDN. These intelligent strategies for minimizing power loss, enhancing the voltage profile, and increasing the convergence rate are based on an adaptive neuro-fuzzy inference system, a genetic algorithm, and enhanced particle swarm optimization.</p>
</abstract>
E-Vehicles are used for transportation and, with a vehicle-to-grid optimization approach, they may be used for supplying a backup source of energy for renewable energy sources. Renewable energy sources are integrated to maintain the demand of consumers, mitigate the active and reactive power losses, and maintain the voltage profile. Renewable energy sources are not supplied all day and, to meet the peak demand, extra electricity may be supplied through e-Vehicles. E-Vehicles with random integration may cause system unbalancing problems and need a solution. The objective of this paper is to integrate e-Vehicles with the grid as a backup source of energy through the grid-to-vehicle optimization approach by reducing active and reactive power losses and maintaining voltage profile. In this paper, three case studies are discussed: (i) integration of renewable energy sources alone; (ii) integration of e-Vehicles alone; (iii) integration of renewable energy sources and e-Vehicles in hybrid mode. The simulation results show the effectiveness of the integration and the active and reactive power losses are minimum when we used the third case.
In Wireless Sensor Networks (WSNs), routing algorithms can provide energy efficiency. However, due to unbalanced energy consumption for all nodes, the network lifetime is still prone to degradation. Hence, energy efficient routing was developed in this article by selecting cluster heads (CH) with the help of adaptive whale optimization (AWOA) which was used to reduce time-consumption delays. The multi-objective function was developed for CH selection. The clusters were then created using the distance function. After establishing groupings, the supercluster head (SCH) was selected using the benefit of a fuzzy inference system (FIS) which was used to collect data for all CHs and send them to the base station (BS). Finally, for the data-transfer procedure, hop count routing was used. An Oppositional-based Whale optimization algorithm (OWOA) was developed for multi-constrained QoS routing with the help of AWOA. The performance of the proposed OWOA methodology was analyzed according to the following metrics: delay, delivery ratio, energy, NLT, and throughput and compared with conventional techniques such as particle swarm optimization, genetic algorithm, and Whale optimization algorithm.
In this paper, an adaptive scheme based on biogeography based optimization and particle swarm optimization method for optimal penetration and sizing of distributed generation system in the existing radial distribution network at the optimal bus is suggested. Power losses and voltage profile maintenance are the biggest restrictions of the existing power system. These power losses will be calculated by load flow analysis and the adaptive scheme has been applied for power loss reduction and enhancing voltage profile. The testing of the adaptive scheme for optimal penetration and sizing of distributed generation system will be done at the IEEE 69 bus radial distribution network. This adaptive scheme and IEEE 69 bus radial distribution network are modelled with the help of Matlab software.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.