  Multi-uncertainties from power sources and loads have brought significant
challenges to the stable demand supply of various resources at islands. To
address these challenges, a comprehensive scheduling framework is proposed by
introducing a model-free deep reinforcement learning (DRL) approach based on
modeling an island integrated energy system (IES). In response to the shortage
of freshwater on islands, in addition to the introduction of seawater
desalination systems, a transmission structure of "hydrothermal simultaneous
transmission" (HST) is proposed. The essence of the IES scheduling problem is
the optimal combination of each unit's output, which is a typical timing
control problem and conforms to the Markov decision-making solution framework
of deep reinforcement learning. Deep reinforcement learning adapts to various
changes and timely adjusts strategies through the interaction of agents and the
environment, avoiding complicated modeling and prediction of
multi-uncertainties. The simulation results show that the proposed scheduling
framework properly handles multi-uncertainties from power sources and loads,
achieves a stable demand supply for various resources, and has better
performance than other real-time scheduling methods, especially in terms of
computational efficiency. In addition, the HST model constitutes an active
exploration to improve the utilization efficiency of island freshwater.
