The large integration of variable energy resources is expected to shift a large part of the energy exchanges closer to real-time, where more accurate forecasts are available. In this context, the short-term electricity markets and in particular the intraday market are considered a suitable trading floor for these exchanges to occur. A key component for the successful renewable energy sources integration is the usage of energy storage. In this paper, we propose a novel modelling framework for the strategic participation of energy storage in the European continuous intraday market where exchanges occur through a centralized order book. The goal of the storage device operator is the maximization of the profits received over the entire trading horizon, while taking into account the operational constraints of the unit. The sequential decision-making problem of trading in the intraday market is modelled as a Markov Decision Process. An asynchronous version of the fitted Q iteration algorithm is chosen for solving this problem due to its sample efficiency. The large and variable number of the existing orders in the order book motivates the use of high-level actions and an alternative state representation. Historical data are used for the generation of a large number of artificial trajectories in order to address exploration issues during the learning process. The resulting policy is back-tested and compared against a number of benchmark strategies. Finally, the impact of the storage characteristics on the total revenues collected in the intraday market is evaluated.

可变能源的大规模整合预计将使大部分能源交换更接近实时，从而提供更准确的预测。在这种情况下，短期电力市场，特别是日内市场被认为是这些交易所发生的合适交易场所。可再生能源成功整合的一个关键组成部分是储能的使用。在本文中，我们提出了一种新的建模框架，用于储能在欧洲连续盘中市场的战略参与，其中通过集中订单簿进行交易。存储设备运营商的目标是在整个交易范围内获得利润最大化，同时考虑到设备的运营限制。日内市场交易的顺序决策问题被建模为马尔可夫决策过程。由于其样本效率，选择了拟合 Q 迭代算法的异步版本来解决此问题。订单簿中大量且可变的现有订单促使使用高级操作和替代状态表示。历史数据用于生成大量人工轨迹，以解决学习过程中的探索问题。由此产生的策略经过回溯测试并与许多基准策略进行比较。最后，评估存储特性对日内市场收集的总收入的影响。由于其样本效率，选择了拟合 Q 迭代算法的异步版本来解决此问题。订单簿中大量且可变的现有订单促使使用高级操作和替代状态表示。历史数据用于生成大量人工轨迹，以解决学习过程中的探索问题。由此产生的策略经过回溯测试并与许多基准策略进行比较。最后，评估存储特性对日内市场收集的总收入的影响。由于其样本效率，选择了拟合 Q 迭代算法的异步版本来解决此问题。订单簿中大量且可变的现有订单促使使用高级操作和替代状态表示。历史数据用于生成大量人工轨迹，以解决学习过程中的探索问题。由此产生的策略经过回溯测试并与许多基准策略进行比较。最后，评估存储特性对日内市场收集的总收入的影响。订单簿中大量且可变的现有订单促使使用高级操作和替代状态表示。历史数据用于生成大量人工轨迹，以解决学习过程中的探索问题。由此产生的策略经过回溯测试并与许多基准策略进行比较。最后，评估存储特性对日内市场收集的总收入的影响。订单簿中大量且可变的现有订单促使使用高级操作和替代状态表示。历史数据用于生成大量人工轨迹，以解决学习过程中的探索问题。由此产生的策略经过回溯测试并与许多基准策略进行比较。最后，评估存储特性对日内市场收集的总收入的影响。