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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Abstract	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.
AbstractList	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. Abstract 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.
ArticleNumber	1074
Author	Jiang, Shaoyan Du, Lifeng Su, Shaoying Zheng, Jiaxin Tan, Hanming
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SubjectTerms	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
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Title	Distribution network path planning method and system based on artificial intelligence optimization algorithm
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