Nowadays, the application of microgrids (MG) with renewable energy is becoming more and more extensive, which creates a strong need for dynamic energy management. In this paper, deep reinforcement learning (DRL) is applied to learn an optimal policy for making joint energy dispatch (ED) and unit commitment (UC) decisions in an isolated MG, with the aim for reducing the total power generation cost on the premise of ensuring the supply-demand balance. In order to overcome the challenge of discrete-continuous hybrid action space due to joint ED and UC, we propose a DRL algorithm, i.e., the hybrid action finite-horizon DDPG (HAFH-DDPG), that seamlessly integrates two classical DRL algorithms, i.e., deep Q-network (DQN) and deep deterministic policy gradient (DDPG), based on a finite-horizon dynamic programming (DP) framework. Moreover, a diesel generator (DG) selection strategy is presented to support a simplified action space for reducing the computation complexity of this algorithm. Finally, the effectiveness of our proposed algorithm is verified through comparison with several baseline algorithms by experiments with real-world data set.

翻译：如今，含可再生能源的微电网（MG）应用日益广泛，这催生了对动态能量管理的强烈需求。本文应用深度强化学习（DRL）学习最优策略，以实现孤立微电网中联合能量调度（ED）与机组组合（UC）决策，目标是在确保供需平衡的前提下降低总发电成本。为克服联合ED与UC导致的离散-连续混合动作空间挑战，我们提出一种DRL算法，即混合动作有限时域DDPG（HAFH-DDPG），该算法基于有限时域动态规划（DP）框架，无缝集成了两种经典DRL算法——深度Q网络（DQN）与深度确定性策略梯度（DDPG）。此外，提出一种柴油发电机（DG）选择策略以支持简化动作空间，从而降低算法计算复杂度。最后，通过基于真实数据集的实验与若干基线算法对比，验证了所提算法的有效性。