Population-based Multi-agent Evaluation for Large-scale Voltage Control

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.