Distribution network path planning method and system based on artificial intelligence optimization algorithm

Planning the most efficient, cost-effective, and reliable pathways for transmitting electricity from electrical substations to consumers is known as distribution network path planning. It’s not an easy task, but it’s necessary to meet the changing needs of the load and include renewable energy sourc...

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Vydáno v:Discover applied sciences Ročník 7; číslo 10; s. 1074 - 18
Hlavní autoři: Jiang, Shaoyan, Zheng, Jiaxin, Du, Lifeng, Su, Shaoying, Tan, Hanming
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cham Springer International Publishing 26.09.2025
Springer Nature B.V
Springer
Témata:
Accuracy
Algorithms
Alternative energy
Applied and Technical Physics
Artificial intelligence
Chemistry/Food Science
DDPG model
Deep learning
Disaster relief
Distribution centers
Distribution network
Earth Sciences
Efficiency
Electric cables
Electrical transmission
Electricity
Electricity distribution
Energy storage
Engineering
Environment
Genetic algorithms
Integer programming
Learning
Machine learning
Materials Science
Neural networks
Optimization
Optimization algorithm
Optimization techniques
Path planning
Power cables
Reinforcement
Renewable energy sources
Renewable resources
Route optimization
Route planning
Substations
Training
Training level
Optimization algorithm
Path planning
Distribution network
Artificial intelligence
DDPG model
ISSN:3004-9261, 2523-3963, 3004-9261, 2523-3971
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Shrnutí:Planning the most efficient, cost-effective, and reliable pathways for transmitting electricity from electrical substations to consumers is known as distribution network path planning. It’s not an easy task, but it’s necessary to meet the changing needs of the load and include renewable energy sources. A computerized model of the electrical system that includes power cables, nodes (such as transformers and substations), and the capacity, impedance, and position of each component. A basic topic with diverse applications, the route planning issue is a staple in many domains. Scholarly interest in finding a solution to the route optimization issue using deep reinforcement learning technologies has grown in recent years, making it a popular avenue for path planning problems. In this research, we will examine a power distribution optimization route approach, use deep reinforcement learning to address the continuous route planning issue, and do experiments in a Miniworld maze. In a study that compared Deep Deterministic Policy Gradient (DDPG) to genetic algorithms, Binary Swarm Optimization, while the historical average approach, it was found that the latter had a small and less than ideal accuracy rate, was easy to calculate, and showed little change in accuracy with increasing data. A neural network representation of the reward function is used to suggest a reward shaping DDPG algorithm that optimizes the reward functionality dynamically. The genetic algorithms accuracy hovers around 70%; it degrades with increasing training size. Eventually stabilizing at about 83%, the forecasting accuracy rate increased in tandem with the training system’s expansion, leading to a deeper learning model with a higher training level.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:3004-9261
2523-3963
3004-9261
2523-3971
DOI:10.1007/s42452-025-07699-3