  This paper presents an approximate Reinforcement Learning (RL) methodology
for bi-level power management of networked Microgrids (MG) in electric
distribution systems. In practice, the cooperative agent can have limited or no
knowledge of the MG asset behavior and detailed models behind the Point of
Common Coupling (PCC). This makes the distribution systems unobservable and
impedes conventional optimization solutions for the constrained MG power
management problem. To tackle this challenge, we have proposed a bi-level RL
framework in a price-based environment. At the higher level, a cooperative
agent performs function approximation to predict the behavior of entities under
incomplete information of MG parametric models; while at the lower level, each
MG provides power-flow-constrained optimal response to price signals. The
function approximation scheme is then used within an adaptive RL framework to
optimize the price signal as the system load and solar generation change over
time. Numerical experiments have verified that, compared to previous works in
the literature, the proposed privacy-preserving learning model has better
adaptability and enhanced computational speed.
