AI Modelling and Time-series Forecasting Systems for Trading Energy Flexibility in Distribution Grids

We demonstrate progress on the deployment of two sets of technologies to support distribution grid operators integrating high shares of renewable energy sources, based on a market for trading local energy flexibilities. An artificial-intelligence (AI) grid modelling tool, based on probabilistic graphs, predicts congestions and estimates the amount and location of energy flexibility required to avoid such events. A scalable time-series forecasting system delivers large numbers of short-term predictions of distributed energy demand and generation. We discuss the deployment of the technologies at three trial demonstration sites across Europe, in the context of a research project carried out in a consortium with energy utilities, technology providers and research institutions

Probabilistic Graphs for Sensor Data-driven Modelling of Power Systems at Scale

The growing complexity of the power grid, driven by increasing share of distributed energy resources and by massive deployment of intelligent internet-connected devices, requires new modelling tools for planning and operation. Physics-based state estimation models currently used for data filtering, prediction and anomaly detection are hard to maintain and adapt to the ever-changing complex dynamics of the power system. A data-driven approach based on probabilistic graphs is proposed, where custom non-linear, localised models of the joint density of subset of system variables can be combined to model arbitrarily large and complex systems. The graphical model allows to naturally embed domain knowledge in the form of variables dependency structure or local quantitative relationships. A specific instance where neural-network models are used to represent the local joint densities is proposed, although the methodology generalises to other model classes. Accuracy and scalability are evaluated on a large-scale data set representative of the European transmission grid

Open Power System Data - Frictionless data for electricity system modelling

The quality of electricity system modelling heavily depends on the input data used. Although a lot of data is publicly available, it is often dispersed, tedious to process and partly contains errors. We argue that a central provision of input data for modelling has the character of a public good: it reduces overall societal costs for quantitative energy research as redundant work is avoided, and it improves transparency and reproducibility in electricity system modelling. This paper describes the Open Power System Data platform that aims at realising the efficiency and quality gains of centralised data provision by collecting, checking, processing, aggregating, documenting and publishing data required by most modellers. We conclude that the platform can provide substantial benefits to energy system analysis by raising efficiency of data pre-processing, providing a method for making data pre-processing for energy system modelling traceable, flexible and reproducible and improving the quality of original data published by data providers.Comment: This is the postprint version of the articl

Multi-Source Data Fusion for Cyberattack Detection in Power Systems

Cyberattacks can cause a severe impact on power systems unless detected early. However, accurate and timely detection in critical infrastructure systems presents challenges, e.g., due to zero-day vulnerability exploitations and the cyber-physical nature of the system coupled with the need for high reliability and resilience of the physical system. Conventional rule-based and anomaly-based intrusion detection system (IDS) tools are insufficient for detecting zero-day cyber intrusions in the industrial control system (ICS) networks. Hence, in this work, we show that fusing information from multiple data sources can help identify cyber-induced incidents and reduce false positives. Specifically, we present how to recognize and address the barriers that can prevent the accurate use of multiple data sources for fusion-based detection. We perform multi-source data fusion for training IDS in a cyber-physical power system testbed where we collect cyber and physical side data from multiple sensors emulating real-world data sources that would be found in a utility and synthesizes these into features for algorithms to detect intrusions. Results are presented using the proposed data fusion application to infer False Data and Command injection-based Man-in- The-Middle (MiTM) attacks. Post collection, the data fusion application uses time-synchronized merge and extracts features followed by pre-processing such as imputation and encoding before training supervised, semi-supervised, and unsupervised learning models to evaluate the performance of the IDS. A major finding is the improvement of detection accuracy by fusion of features from cyber, security, and physical domains. Additionally, we observed the co-training technique performs at par with supervised learning methods when fed with our features