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:目前,利用可再生能源的微型电网(MG)的应用正在变得越来越广泛,从而产生了对动态能源管理的强烈需要。在本文件中,深强化学习(DRL)被用于在孤立的MG中学习关于联合能源发送(ED)和单位承诺(UC)决策的最佳政策,目的是在确保供需平衡的前提下降低发电总成本。为了克服由于联合的ED和UC造成的离散、连续的混合行动空间的挑战,我们提出了一种DRL算法,即混合行动定额-正正数 DDPG(HAFH-DDPG),通过将两种经典的DRL算法,即深Q-网络(DQN)和深度的确定性政策梯度(DPG),以有限-正数动态规划框架为基础。此外,提出了柴油发电机(DG)选择战略,以支持简化行动空间,降低这一算法的复杂度。最后,我们提议的算法的有效性通过实际数据实验与若干基线算法的比较得到验证。