Deriving fast and effectively coordinated control actions remains a grand challenge affecting the secure and economic operation of today's large-scale power grid. This paper presents a novel artificial intelligence (AI) based methodology to achieve multi-objective real-time power grid control for real-world implementation. State-of-the-art off-policy reinforcement learning (RL) algorithm, soft actor-critic (SAC) is adopted to train AI agents with multi-thread offline training and periodic online training for regulating voltages and transmission losses without violating thermal constraints of lines. A software prototype was developed and deployed in the control center of SGCC Jiangsu Electric Power Company that interacts with their Energy Management System (EMS) every 5 minutes. Massive numerical studies using actual power grid snapshots in the real-time environment verify the effectiveness of the proposed approach. Well-trained SAC agents can learn to provide effective and subsecond control actions in regulating voltage profiles and reducing transmission losses.
Distributed Deep Reinforcement Learning for Intelligent Load Scheduling in Residential Smart Grids
University of Oslo（奥斯陆大学）；Simula Metropolitan Center for Digital Engineering（Simula大都会数字工程中心）
家庭用电量多年来一直在不断增长。为了应对这种增长，有必要对家庭的消费情况进行智能管理，以便家庭可以节省电费，并可以减少高峰时段对电网的压力。然而，实现这样的方法是具有挑战性的，由于存在随机性的电价和消费的电器。为了应对这一挑战，我们采用了一种无模型的方法，该方法适用于有关不确定因素的有限信息的家庭。更具体地说，家庭和电网之间的相互作用可以被建模为一个非合作的随机对策，其中的电价被视为一个随机变量。为了寻找博弈的纳什均衡（NE），我们采用了一种基于分布式深度强化学习的方法。此外，所提出的方法可以保护家庭的隐私。然后，我们利用Pecan Street Inc.的真实数据，其包含超过1000个家庭的电力消耗概况，以评估所提出的方法的性能。平均而言，结果表明，我们可以实现约12%的峰均比（PAR）和11%的负载方差减少。通过这种方法，可以降低电网的运行成本和家庭的用电成本。
Grid-side Flexibility of Power Systems in Integrating Large-scale Renewable Generations: A Critical Review on Concepts, Formulations and Solution Approaches
Locational Marginal Emissions for Carbon-Aware Data Center Operations in Large-Scale Power Grids
Operations Research Center, Massachusetts Institute of Technology（麻省理工学院运筹学中心）；Sloan School of Management, Massachusetts Institute of Technology（麻省理工学院斯隆管理学院）
