This paper targets the enhancement of distribution power system's performance and operation through reducing system's real losses and improving overall voltage profile of the network taking into consideration topological and load constraints. For this target, two main techniques are employed in this paper; network reconfiguration and distributed generators installation. The paper proposes genetic algorithm (GA) along with an analytically developed load flow code to generate optimal network topology and find optimal sizing, locations and number of distributed generators to be installed considering different DG types. To demonstrate the effectiveness of the research, the IEEE 33 bus system is considered in this paper. This is a three phase radial balanced distribution system. Index Terms--Distribution system, network reconfiguration, distributed generators (DG), genetic algorithm optimization (GA), power loss reduction, voltage profile improvement.
This paper establishes a study for an accurate parameter modeling method for lithium-ion batteries. A precise state space model generated from an equivalent electric circuit is used to carry out the proposed identification process, where parameter identification is a nonlinear optimization process problem. The African vultures optimization algorithm (AVOA) is utilized to solve this problem by simulating African vultures’ foraging and navigating habits. The AVOA is used to implement this strategy and improve the quality of the solutions. Four scenarios are considered to take the effect of loading, fading, and dynamic analyses. The fitness function is selected as the integral square error between the estimated and measured voltage in these scenarios. Numerical simulations were executed on a 2600 mAhr Panasonic Li-ion battery to demonstrate the effectiveness of the suggested parameter identification technique. The proposed AVOA was fulfilled with high accuracy, the least error, and high closeness with the experimental data compared with different optimization algorithms, such as the Nelder–Mead simplex algorithm, the quasi-Newton algorithm, the Runge Kutta optimizer, the genetic algorithm, the grey wolf optimizer, and the gorilla troops optimizer. The proposed AVOA achieves the lowest fitness function level of the scenarios studied compared with relative optimization algorithms.
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