物联网驱动的智能孤立微电网中基于深度强化学习的动态能量调度
Dynamic Energy Dispatch Based on Deep Reinforcement Learning in IoT-Driven Smart Isolated Microgrids
AI总结：
本文针对物联网驱动的智能孤立微电网能量调度问题，提出有限时域深度确定性策略梯度和有限时域循环确定性策略梯度两种DRL算法，并通过真实数据验证其在不确定条件下的性能优势。
摘要：
微电网（MGs）是小型本地电网，可以独立于更大的公用事业电网运行。结合物联网（IoT），智能微电网可以利用传感数据和机器学习技术实现智能能源管理。本文重点研究基于深度强化学习（DRL）的能量调度，用于物联网驱动的智能孤立微电网，该微电网包含柴油发电机（DGs）、光伏（PV）板和电池。本文建立了一个有限时域部分可观测马尔可夫决策过程（POMDP）模型，并通过从历史数据中学习来求解，以捕捉未来电力消耗和可再生能源发电中的不确定性。为了解决DRL算法的不稳定性问题以及有限时域模型的独特特性，提出了两种新型DRL算法，即有限时域深度确定性策略梯度（FH-DDPG）和有限时域循环确定性策略梯度（FH-RDPG），用于在具有和不具有完全可观测状态信息的情况下推导能量调度策略。使用真实孤立微电网数据进行了案例研究，将所提算法的性能与其他基线DRL和非DRL算法进行了比较。此外，不确定性对微电网性能的影响被解耦为两个层级并分别进行评估。
英文摘要：
Microgrids (MGs) are small, local power grids that can operate independently from the larger utility grid. Combined with the Internet of Things (IoT), a smart MG can leverage the sensory data and machine learning techniques for intelligent energy management. This paper focuses on deep reinforcement learning (DRL)-based energy dispatch for IoT-driven smart isolated MGs with diesel generators (DGs), photovoltaic (PV) panels, and a battery. A finite-horizon Partial Observable Markov Decision Process (POMDP) model is formulated and solved by learning from historical data to capture the uncertainty in future electricity consumption and renewable power generation. In order to deal with the instability problem of DRL algorithms and unique characteristics of finite-horizon models, two novel DRL algorithms, namely, finite-horizon deep deterministic policy gradient (FH-DDPG) and finite-horizon recurrent deterministic policy gradient (FH-RDPG), are proposed to derive energy dispatch policies with and without fully observable state information. A case study using real isolated MG data is performed, where the performance of the proposed algorithms are compared with the other baseline DRL and non-DRL algorithms. Moreover, the impact of uncertainties on MG performance is decoupled into two levels and evaluated respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
