Online distributed voltage control of an offshore MTdc network using reinforcement learning

This paper addresses one of the main challenges on the way to an offshore transnational multi-terminal dc (MTdc) network: its control and operation. The main objective is to demonstrate the feasibility of using reinforcement learning (RL) techniques to control, in real time, a multi-terminal dc network aimed at integrating offshore wind farms (OWFs). This method of controlling MTdc networks using RL techniques is called Online Distributed Voltage Control (ODVC). The ODVC strategy uses Continuous Action Reinforcement Learning Automata (CARLA) to optimize power flows in real time. To validate the effectiveness of the proposed control method, dynamic simulations are carried out using a MTdc grid model composed of six nodes, interconnecting three offshore wind farms to three European countries. The results obtained demonstrate the advantages of implementing an online distributed voltage control strategy to obtain feasible controlled power flows with low transmission losses. The results obtained demonstrate the feasibility of the proposed method to control, in real time, MTdc networks and that the RL techniques are well-suited for this problem due to their inherent advantages of coping with stochastic environments.