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压缩空气储能是一种大规模物理储能技术，为实现电网负荷削峰填谷、缓解弃风弃光问题提供了崭新的解决思路。同时，未来电网也逐步呈现新能源高占比、“强直弱交”等形态，对储能系统的运行模式和功能特征有了更丰富而具体的要求。提出了压缩空气储能系统调相运行模式，从技术原理、设计思路、性能评估、算例分析等方面探索并验证了压缩空气储能系统调相运行的有效性。所提出的调相运行模式不仅能为电网提供动态无功支撑，而且能通过降低机组启停次数而减少运维量；提出的调相运行模式很好地扩展了系统功能，有望成为压缩空气储能推广应用的重要支撑技术。

作为一种储量大、储热温度高、环境友好的地热资源，干热岩的开发越来越受到人们的重视。青海共和盆地干热岩资源埋藏浅、温度高，具有较高的开发利用价值，且易于实现开发利用。针对共和盆地干热岩的特点，本文建立了增强型干热岩系统联合内回热有机朗肯循环发电系统的模型，研究了不同工质应用于该系统的热力学特性，分析了关键参数对系统效率及净发电功率的影响，并探讨了干热岩发电在电网储能调峰中的应用前景，为未来的工程建设提供技术支持。

随着人工智能技术的发展和海量司法数据的公开，面向“智慧司法”服务的司法判决预测（legal judgment prediction，LJP）任务受到了学术界和工业界的广泛关注，该任务旨在根据有限的案件事实描述文本来预测案件的罪名、法条和刑期。然而，现有工作缺乏对易混淆司法案件的智能决策的研究，且相关模型通常缺乏可解释性，这会导致模型预测严重依赖领域专家，阻碍LJP在不同法律体系中的应用。为此，提出了一种基于因果图分析的司法判决预测（prediction of legal judgment based on causal graph analysis， CGLJ）方法，首先从非结构化的法律事实描述文本中挖掘要素之间的因果关系，然后采用易混淆罪名聚类的构图方法构建因果图，既考虑了相似事实描述之间的差异，又增强了事实描述和法律法规之间的相互作用，最后将构建好的因果图融入深度神经网络进行联合推理，得到判决预测结果。此外，还对模型预测过程中的因果图推理过程进行了可视化，为判决结果提供了更好的可解释性。在2018中国“法研杯”司法人工智能挑战赛（CAIL2018）司法判决预测数据集上的实验结果表明，该方法相比基线模型取得了更好的效果。

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