Application of DSAPSO Algorithm in Distribution Network Reconfiguration with Distributed Generation

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, tra...

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Vydané v:Energy engineering Ročník 121; číslo 1; s. 187 - 201
Hlavní autori: Tao, Caixia, Yang, Shize, Li, Taiguo
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Atlanta Tech Science Press 2024
Predmet:
Algorithms
Complexity
Convergence
Distributed generation
Electric power loss
Energy resources
Energy sources
Nodes
Optimization
Pareto optimum
Particle swarm optimization
Reconfiguration
Simulated annealing
Solution space
ISSN:1546-0118, 0199-8595, 1546-0118
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Popis
Shrnutí: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.
ISSN:1546-0118
0199-8595
1546-0118
DOI:10.32604/ee.2023.042421