The bidirectional reflection distribution function is usually used to analyze the reflection characteristics of materials. In many cases, the BRDF models are optimized by fitting parameters. We introduced a hybrid par...
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The bidirectional reflection distribution function is usually used to analyze the reflection characteristics of materials. In many cases, the BRDF models are optimized by fitting parameters. We introduced a hybrid particle swarm algorithm (ga-pso) combining genetic algorithm and particle swarm algorithm to optimize the parameters of the five-parameter model. In order to verify the performance of the hybrid particle swarm optimization algorithm, we measured two different materials of space targets to get the experimental BRDF values. Then we simulated the parameters by using the genetic algorithm, particle swarm algorithm, and hybrid particle swarm algorithm respectively. The fitting results show that the hybrid particle swarm algorithm is better than genetic algorithm and particle swarm algorithm in accuracy and convergence speed under the same condition.
Due to the stochastic nature of renewable energy sources (RES) and electric vehicles (EV) load demand, large scale penetration of these resources in the power systems can stress the reliable network performance, such ...
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Due to the stochastic nature of renewable energy sources (RES) and electric vehicles (EV) load demand, large scale penetration of these resources in the power systems can stress the reliable network performance, such as reducing power quality, increasing power losses, and voltage deviations. These challenges must be minimized by optimal planning based on the variable output from RES to meet the additional demand caused by EV charging. In this paper, a novel method for optimal allocation and sizing of RES and EV charging stations simultaneously and managing vehicle charging process is provided. A multi-objective optimization problem is formulated to obtain objective variables in order to reduce power losses, voltage fluctuations, charging and demand supplying costs, and EV battery cost. In this optimization problem, the location and capacity of RES and EV charging stations are the optimization variables. Coefficients which are dependent on wind speed, solar radiation, and hourly peak demand ratio for the management of the EV charging pattern in off-peak load hours are introduced. Genetic algorithm-Particle Swarm Optimization (ga-pso) hybrid improved optimization algorithm is used to solve the optimization problem in five different scenarios. The performance of the proposed method on IEEE 33bus system has been investigated to validate the effectiveness of the novel ga-pso method to optimal sitting and sizing of RES and EV charging stations simultaneously.
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