Multi-objective transmission network expansion planning based on Reinforcement Learning

2020 ◽  
Author(s):  
Wei MingKui ◽  
Cai ShaoRong ◽  
Zhou Quan ◽  
Zhou Xu ◽  
Zhou Hong ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Transmission Network ◽  
Network Expansion ◽  
Multi Objective ◽  
Expansion Planning

Transmission Network Expansion Planning with Security Constraints and Uncertainty in Load Specifications

2008 ◽  
Vol 9 (4) ◽  
Author(s):  
Ashu Verma ◽  
Pradeep R. Bijwe ◽  
Bijaya Ketan Panigrahi
Keyword(s):  
Power Flow ◽  
Planning Problem ◽  
Transmission Network ◽  
Network Expansion ◽  
Security Issues ◽  
Expansion Planning ◽  
Dc Power ◽  
Sample Test ◽  
Binary Genetic Algorithm ◽  
Long Range Planning

Transmission network expansion planning is a very critical problem due to not only the huge investment cost involved, but also the associated security issues. Any long range planning problem is confronted with the challenge of non-statistical uncertainty in the data. Although large number of papers have been published in this area, the efforts to tackle the above mentioned security and uncertainty issues have been relatively very few, due to the formidable complexity involved. This paper tries to bridge this gap by proposing a technique to tackle these problems. Boundary DC power flow is used to ascertain the worst power flows on the lines. A simple basic binary Genetic algorithm is used to solve the optimization problem as an illustration. Results for two sample test systems have been obtained to demonstrate the potential of the proposed method.

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