As the world seeks to become more sustainable, intelligent solutions are needed to increase the penetration of renewable energy. In this paper, the model-free deep reinforcement learning algorithm Rainbow Deep Q-Networks is used to control a battery in a small microgrid to perform energy arbitrage and more efficiently utilise solar and wind energy sources. The grid operates with its own demand and renewable generation based on a dataset collected at Keele University, as well as using dynamic energy pricing from a real wholesale energy market. Four scenarios are tested including using demand and price forecasting produced with local weather data. The algorithm and its subcomponents are evaluated against two continuous control benchmarks with Rainbow able to outperform all other method. This research shows the importance of using the distributional approach for reinforcement learning when working with complex environments and reward functions, as well as how it can be used to visualise and contextualise the agent's behaviour for real-world applications.

翻译：在本文中,无模型的深强化学习算法彩虹深Q-Networks被用来控制小型微型电网中的电池,以便进行能源套利,并更有效地利用太阳能和风能来源;电网以自己的需求和可再生能源运作,其依据是在基尔大学收集的数据集为基础,并利用真正的批发能源市场动态能源定价;对四种假设进行了测试,包括利用当地天气数据产生的需求和价格预测;对算法及其子组成部分进行了评价,根据两个连续的控制基准,而彩虹能够超越所有其他方法;这项研究表明,在与复杂环境和奖励功能合作时,使用分配方法加强学习的重要性,以及如何利用分配方法为现实世界的应用对代理人的行为进行视觉化和背景化。