TY - GEN
T1 - Population-based Multi-agent Evaluation for Large-scale Voltage Control
AU - Jin, Cheng
AU - Zhang, Senlin
AU - Liu, Meiqin
AU - Zheng, Ronghao
AU - Dong, Shanling
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Under the purpose of achieving the optimal voltage control strategy in power grid system, multi-agent evaluation algorithms like a-rank are widely used. However, in large-scale systems with massive agents and strategies, these methods are not time feasible. Therefore, a two-stage population-based multi-agent evaluation algorithm is proposed to solve voltage control problem in large-scale power grid systems. For stage one, a population is first established for each agent. And then, individuals in the populations randomly combined to form joint strategies. Base on the max and mean reward from the interaction between joint strategies and the environment, populations evolve to a near-optimal joint strategy. Stage two takes the above near-optimal joint strategy as the starting point, and uses a strategy search algorithm with maximum transfer possibility to find the Markov-Conley chain in the system. Finally, the above two-stage method is simulated in 10 and 32-agent power grid systems to verify the effectiveness.
AB - Under the purpose of achieving the optimal voltage control strategy in power grid system, multi-agent evaluation algorithms like a-rank are widely used. However, in large-scale systems with massive agents and strategies, these methods are not time feasible. Therefore, a two-stage population-based multi-agent evaluation algorithm is proposed to solve voltage control problem in large-scale power grid systems. For stage one, a population is first established for each agent. And then, individuals in the populations randomly combined to form joint strategies. Base on the max and mean reward from the interaction between joint strategies and the environment, populations evolve to a near-optimal joint strategy. Stage two takes the above near-optimal joint strategy as the starting point, and uses a strategy search algorithm with maximum transfer possibility to find the Markov-Conley chain in the system. Finally, the above two-stage method is simulated in 10 and 32-agent power grid systems to verify the effectiveness.
KW - Markov-Conley chain
KW - a-rank
KW - multi-agent evaluation
KW - population evolution
KW - voltage control
UR - https://www.scopus.com/pages/publications/85187265238
U2 - 10.1109/SMC53992.2023.10394480
DO - 10.1109/SMC53992.2023.10394480
M3 - 会议稿件
AN - SCOPUS:85187265238
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 3882
EP - 3887
BT - 2023 IEEE International Conference on Systems, Man, and Cybernetics
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023
Y2 - 1 October 2023 through 4 October 2023
ER -