Reinforcement learning for active distribution network planning based on Monte Carlo tree search

2022 ◽  
Vol 138 ◽  
pp. 107885
Author(s):  
Xi Zhang ◽  
Weiqi Hua ◽  
Youbo Liu ◽  
Jiajun Duan ◽  
Zhiyuan Tang ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Reinforcement Learning ◽  
Distribution Network ◽  
Network Planning ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Active Distribution Network ◽  
Distribution Network Planning

scholarly journals Towards efficient discovery of green synthetic pathways with Monte Carlo tree search and reinforcement learning

2020 ◽  
Vol 11 (40) ◽  
pp. 10959-10972
Author(s):  
Xiaoxue Wang ◽  
Yujie Qian ◽  
Hanyu Gao ◽  
Connor W. Coley ◽  
Yiming Mo ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Reinforcement Learning ◽  
Prediction Model ◽  
Tree Search ◽  
Monte Carlo Tree Search ◽  
Value Network ◽  
Synthesis Routes

A new MCTS variant with a reinforcement learning value network and solvent prediction model proposes shorter synthesis routes with greener solvents.

scholarly journals Multi-Criteria Analysis for decision making applied to active distribution network planning

2018 ◽  
Vol 164 ◽  
pp. 103-111 ◽  
Author(s):  
Gianni Celli ◽  
Nayeem Chowdhury ◽  
Fabrizio Pilo ◽  
Gian Giuseppe Soma ◽  
Matteo Troncia ◽  
...  
Keyword(s):  
Decision Making ◽  
Distribution Network ◽  
Network Planning ◽  
Active Distribution Network ◽  
Distribution Network Planning ◽  
Multi Criteria Analysis

scholarly journals Research on Active Distribution Network Planning with Electric Vehicles and Distributed Generation

2021 ◽  
Vol 257 ◽  
pp. 01010
Author(s):  
Lingyan Wei ◽  
Bing Wang ◽  
Xiaoyue Wu ◽  
Fumian Wang ◽  
Peng Chen
Keyword(s):  
Time Series ◽  
Distributed Generation ◽  
Distribution Network ◽  
Network Planning ◽  
Clean Energy ◽  
Space Time ◽  
Maintenance Cost ◽  
Active Distribution Network ◽  
Distribution Network Planning ◽  
Ev Charging

With the increasing number of Electric Vehicle (EV) and clean energy generation year by year, EV and distributed generation (DG) have become issues that have to be considered in active distribution network planning. Firstly, considering the time series characteristics of DG, the output time series model of DG is established; Secondly, the parking demand and space-time movement model of EV is established, and the Monta Carlo method is used to simulate the space-time distribution of EV charging load in different planning areas; Finally, taking the system investment and annual operation and maintenance cost, voltage index and environmental index as the objective function, and considering the node voltage, node current and DG installation capacity as constraints. The improved particle swarm optimization algorithm is used to solve the planning model, and the access location and capacity of EV charging station and DG are obtained. Taking a distribution network as an example, the rationality and effectiveness of the proposed model and algorithm are verified.

scholarly journals Regional active distribution network planning study based on robust optimization

2021 ◽  
Vol 7 ◽  
pp. 314-319
Author(s):  
Zhicheng Jiang ◽  
Qingguang Yu ◽  
Yufeng Xiong ◽  
Le Li ◽  
Yuming Liu
Keyword(s):  
Robust Optimization ◽  
Distribution Network ◽  
Network Planning ◽  
Planning Study ◽  
Active Distribution Network ◽  
Distribution Network Planning
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