The transition away from carbon-based energy sources poses several challenges for the operation of electricity distribution systems. Increasing shares of distributed energy resources (e.g. renewable energy generators, electric vehicles) and internet-connected sensing and control devices (e.g. smart heating and cooling) require new tools to support accurate, datadriven decision making. Modelling the effect of such growing complexity in the electrical grid is possible in principle using state-of-the-art power-power flow models. In practice, the detailed information needed for these physical simulations may be unknown or prohibitively expensive to obtain. Hence, datadriven approaches to power systems modelling, including feedforward neural networks and auto-encoders, have been studied to leverage the increasing availability of sensor data, but have seen limited practical adoption due to lack of transparency and inefficiencies on large-scale problems. Our work addresses this gap by proposing a data- and knowledge-driven probabilistic graphical model for energy systems based on the framework of graph neural networks (GNNs). The model can explicitly factor in domain knowledge, in the form of grid topology or physics constraints, thus resulting in sparser architectures and much smaller parameters dimensionality when compared with traditional machine-learning models with similar accuracy. Results obtained from a real-world smart-grid demonstration project show how the GNN was used to inform grid congestion predictions and market bidding services for a distribution system operator participating in an energy flexibility market.
翻译:离碳基能源的过渡对电力分配系统的运作构成若干挑战:增加分布式能源资源(例如可再生能源发电机、电动车辆)和互联网连接的遥感和控制装置(例如智能供暖和冷却)的份额需要新的工具来支持准确、数据驱动的决策; 利用最先进的电力-电力流模型,原则上可以模拟电网中这种日益复杂的影响; 实际上,这些物理模拟所需的详细信息可能是未知的或过于昂贵的; 因此,对电力系统建模的数据驱动方法,包括供养神经网络和自动装配器,进行了研究,以利用传感器数据不断增多的可用性,但由于在大规模问题上缺乏透明度和效率,实际采用的方法有限; 我们的工作缩小了这一差距,在图形神经神经网络框架(GNNPs)的基础上为能源系统提出了一个由数据和知识驱动的、以知识驱动的稳健的图形模型; 以电网表或物理制约的形式,从而在最小的能源结构结构结构中产生了数据驱动力结构,而采用较弱的智能的市场预测性标准,在使用传统模型和智能的市场演示模型时,则用了一个较小型的市场-电流流化的模型显示,其真实的市场-电压的市场-电路路路的精确的模型显示了如何显示。