Hybrid Policy-Based Reinforcement Learning of Adaptive Energy Management for the Energy Transmission-Constrained Island Group
Lingxiao Yang, Xiaofeng Li, Mengwei Sun, Changyin Sun
Abstract
This article proposes a hybrid policy-based reinforcement learning (HPRL) adaptive energy management to realize the optimal operation for the island group energy system with energy transmission-constrained environment. An island energy hub (IEH) model that can realize the energy cascade utilization is proposed. Compared with the traditional model, the IEH can satisfy the special energy demand of island, meanwhile, ensure the energy supply of island. Moreover, an energy management model of islands group (EMIG) based on the IEH is formulated which comprehensively considers the inverse distribution of energy demand and resources, as well as the limited energy transmission. Since the environment model of the island is difficult to construct due to the increase of proportion of renewable energy generation and civilian load, the EMIG is transformed into a reinforcement learning (RL) task which features model-free. Considering the limitations of traditional RL in discrete-continuous hybrid action space, HPRL is proposed to achieve optimal operation without simplifying the model. Numerical simulations demonstrate the effectiveness of the proposed adaptive energy management.