  Active distribution networks (ADNs) incorporating massive photovoltaic (PV)
devices encounter challenges of rapid voltage fluctuations and potential
violations. Due to the fluctuation and intermittency of PV generation, the
state gap, arising from time-inconsistent states and exacerbated by imprecisely
known system delays, significantly impacts the accuracy of voltage control.
This paper addresses this challenge by introducing a framework for delay
adaptive Volt/Var control (VVC) in the presence of imprecisely known system
delays to regulate the reactive power of PV inverters. The proposed approach
formulates the voltage control, based on predicted system operation states, as
a robust VVC problem. It employs sample selection from the state prediction
interval to promptly identify the worst-performing system operation state.
Furthermore, we leverage the decentralized partially observable Markov decision
process (Dec-POMDP) to reformulate the robust VVC problem. We design Multiple
Policy Networks and employ Multiple Policy Networks and Reward Shaping-based
Multi-agent Twin Delayed Deep Deterministic Policy Gradient (MPNRS-MATD3)
algorithm to efficiently address and solve the Dec-POMDP model-based problem.
Simulation results show the delay adaption characteristic of our proposed
framework, and the MPNRS-MATD3 outperforms other multi-agent reinforcement
learning algorithms in robust voltage control.
