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This paper proposed a reinforcement learning (RL) based energy management system of pelagic islands network microgrids (PINMGs) by ship swapping under the influence of environmental impacts. In addition, the day-ahead standard scheduling by proposing a novel method to maximize the usage of renewable energy (RE) proposes the energy-sharing structure between islands. Energy sharing among islands plays an important role in electrifying remote islands, which need energy due to the unavailability of renewable energy resources to meet local demand. The two-stage cooperative multi-agent deep RL has been presented with the deep Q-learning (DQN) based approach with central RL and island agents (IA) distributed over numerous islands to overcome this challenge. The deep RL-based approaches efficiently learn and optimize their behaviors through several epochs compared with other machine learning or conventional methods due to their in-depth learning capability. Hence, the centralized RL-based problem using dueling DQN was solved to schedule charge battery sharing from resource-rich islands (SI) to load island networks (LIN). In addition, the case study has compared the accuracy among different DQN methods and further scheduling based on the dueling DQN because of its accurate tracking. Due to fluctuating swapping demand and charging patterns, the need for LIN is also stochastic. Hence, the simulation results, including energy scheduling through the ship, are validated by maximizing RE usage through sharing between several islands, and the usefulness of the proposed algorithm is verified through state and action perturbation to verify the robustness.

Renewable Energy Maximization for Pelagic Islands Network of Microgrids Through Battery Swapping Using Deep Reinforcement Learning