Open Access

ARTICLE

Application of DSAPSO Algorithm in Distribution Network Reconfiguration with Distributed Generation

by Caixia Tao, Shize Yang^*, Taiguo Li

School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China

* Corresponding Author: Shize Yang. Email:

(This article belongs to the Special Issue: Key Technologies of Renewable Energy Consumption and Optimal Operation under )

Energy Engineering 2024, 121(1), 187-201. https://doi.org/10.32604/ee.2023.042421

Received 29 May 2023; Accepted 11 August 2023; Issue published 27 December 2023

Abstract

With the current integration of distributed energy resources into the grid, the structure of distribution networks is becoming more complex. This complexity significantly expands the solution space in the optimization process for network reconstruction using intelligent algorithms. Consequently, traditional intelligent algorithms frequently encounter insufficient search accuracy and become trapped in local optima. To tackle this issue, a more advanced particle swarm optimization algorithm is proposed. To address the varying emphases at different stages of the optimization process, a dynamic strategy is implemented to regulate the social and self-learning factors. The Metropolis criterion is introduced into the simulated annealing algorithm to occasionally accept suboptimal solutions, thereby mitigating premature convergence in the population optimization process. The inertia weight is adjusted using the logistic mapping technique to maintain a balance between the algorithm’s global and local search abilities. The incorporation of the Pareto principle involves the consideration of network losses and voltage deviations as objective functions. A fuzzy membership function is employed for selecting the results. Simulation analysis is carried out on the restructuring of the distribution network, using the IEEE-33 node system and the IEEE-69 node system as examples, in conjunction with the integration of distributed energy resources. The findings demonstrate that, in comparison to other intelligent optimization algorithms, the proposed enhanced algorithm demonstrates a shorter convergence time and effectively reduces active power losses within the network. Furthermore, it enhances the amplitude of node voltages, thereby improving the stability of distribution network operations and power supply quality. Additionally, the algorithm exhibits a high level of generality and applicability.

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Keywords

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