No abstract

No abstract

No abstract

SUMMARYThis paper presents the application of Covariance matrix adapted evolution strategy (CMAES) to reactive power planning (RPP) problem by minimizing the combined total cost of energy loss and the allocation cost of additional reactive power sources. Voltage stability index (L-index) is considered as an additional constraint in order to include the system stability into account. To improve the search efficiency of CMAES, penalty parameter-less scheme is employed for constraint handling. To determine the performance of the CMAES method on reactive power planning problem, IEEE-30 bus test system and Tamil Nadu Electricity Board (TNEB)-69 bus system (a practical system in India) are taken. For comparison purposes, the results of real-coded GA (RGA), particle swarm optimization (PSO), sequential quadratic programming (SQP), and Broyden-Fletcher-Goldfarb-Shanno (BFGS) methods are considered. The simulation results reveal that the CMAES algorithm performs better in terms of energy cost, investment cost, and consistency in getting optimal solution. Karush-Kuhn-Tucker (KKT) conditions are also applied on the best solution obtained using CMAES algorithm to substantiate a claim on optimality.

This Paper presents the methodology of penetration of Micro-Grids (MG) in the radial distribution system (RDS). The aim of this paper is to minimize a total real power loss that descends the performance of the radial distribution system by integrating various renewable resources as Distributed Generation (DG). The combination of different types of renewable energy resources contributes a sustainable MG. These resources are optimally sized and located using evolutionary approach in various penetration levels. The optimal solutions are experimented with IEEE 33 radial distribution system using Particle Swarm Optimization (PSO) technique. The results are quite promising and authenticate its potential to solve problem in radial distribution system effectively. Keywords-MG, DG, PSO, RDSis the social component which moves the particle towards the best region.

This paper discusses the application of covariance matrix adapted evolution strategy (CMAES) algorithm on wind energy conversion systems. CMAES is a class of continuous evolutionary algorithm that generates new population members by sampling from a probability distribution that is constructed during the optimization process. Modified IEEE 14 bus system is considered for simulation purpose. The critical evaluation of maximum loadability of the system is determined. Statistical performance of CMAES algorithm reveals that the best value of maximum loadability is obtained when compared to primal dual interior point method. Even though CMAES takes higher computation time, this method gives the best loadability margin. V C 2014 AIP Publishing LLC.

This paper discusses application of Covariance Matrix Adapted Evolution Strategy (CMAES) algorithm for maximizing loadability margin of power system. CMAES is a class of continuous evolutionary algorithm that generates new population members by sampling from a probability distribution that is constructed during the optimization process. IEEE 14 bus , 30 bus and 118 bus systems are considered for simulation purpose. For comparison of the results, primal dual interior point (PDIP), continuation power flow (CPF), Particle swarm optimization algorithms are considered. Statistical performance of CMAES algorithm reveals that even the mean value of maximum loadability is better than maximum loadability obtained in other methods. Even though CMAES takes higher computation time due to the determination of covariance matrix, only this algorithm gives maximum loadability margin.

A solar energy application with maximum efficiency is the need of the day to meet out the power demands. Thermal Performance of a flat plate solar air heater is less and several approaches such as Genetic Algorithm, etc., have been attempted to solve this problem. The purpose of this work is to find the advantages of the application of Differential Evolution ( DE) for the optimization of a flat plate solar air heater. The thermal efficiency of flat plate solar air heater is tested by optimizing its construction and operating variables such as Reynolds number, tilt angle of the plate, emissivity of the plate and velocity of air etc., The impact of these parameters variation on thermal performance is analyzed. In this work, the climatic data for the city of Hamirpur is considered, which is taken from an already published work in which au thors applied Genetic Algorithm ( GA). The reported results ofGA are also considered for comparison with PSO and DE. Simulation results are quite promising and show that DE performs better than PSO and GA.