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The transition away from carbon-based energy sources poses several challengesfor the operation of electricity distribution systems. Increasing shares ofdistributed energy resources (e.g. renewable energy generators, electricvehicles) and internet-connected sensing and control devices (e.g. smartheating and cooling) require new tools to support accurate, datadriven decisionmaking. Modelling the effect of such growing complexity in the electrical gridis possible in principle using state-of-the-art power-power flow models. Inpractice, the detailed information needed for these physical simulations may beunknown or prohibitively expensive to obtain. Hence, datadriven approaches topower systems modelling, including feedforward neural networks andauto-encoders, have been studied to leverage the increasing availability ofsensor data, but have seen limited practical adoption due to lack oftransparency and inefficiencies on large-scale problems. Our work addressesthis gap by proposing a data- and knowledge-driven probabilistic graphicalmodel for energy systems based on the framework of graph neural networks(GNNs). The model can explicitly factor in domain knowledge, in the form ofgrid topology or physics constraints, thus resulting in sparser architecturesand much smaller parameters dimensionality when compared with traditionalmachine-learning models with similar accuracy. Results obtained from areal-world smart-grid demonstration project show how the GNN was used to informgrid congestion predictions and market bidding services for a distributionsystem operator participating in an energy flexibility market.

Knowledge- and Data-driven Services for Energy Systems using Graph Neural Networks