Power Systems Intelligence In research and development projects, the Power Systems Intelligence Group is working on solutions for decentralised, decarbonised and cyber-resilient energy supply, specialising in machine learning and artificial intelligence.
Vision: The AI-empowered Smart Grid
The integration of distributed and supply-dependent primary energies represents a major challenge for the transformation of energy systems. Modern artificial intelligence and machine learning technologies make a significant contribution to meeting this challenge in many areas: in the semi-automatic management of electricity grids, in the insight-driven marketing of decentralised energy systems, in the forecasting of load and generation time series, to name but a few. The close linking of energy systems and ICT infrastructure in smart grids also requires an adaptive and autonomous "immune system" in order to deal with attacks against the infrastructure and failures of subsystems.
The Power Systems Intelligence Group therefore researches and develops solutions at the interface between the power grid, the energy market, artificial intelligence and a cyber-resilient system understanding. Our vision: the AI-empowered Smart Grid.
Machine Learning
Every autonomous, pro-active system needs a model of its environment and a prediction of future system states. This can be the active power input of a wind or photovoltaic park, the demand in energetic neighbourhoods, the available flexibility for the provision of system services or the price development of the energy market. The digitalization of the energy supply opens up data volumes that represent a suitable basis for machine learning. In order to draw meaningful conclusions from these large amounts of data and to train systems for autonomous operation, approaches from deep learning have established themselves as a worthwhile research subject with impressive results. Structures such as deep recurrent networks are suitable for predicting time series or market behaviour. They also allow any systems to be simulated and can independently model complex issues using reinforcement learning. This technique, known as a surrogate model, makes it possible to simulate connections in the power grid, derive and correct sensor values or determine operating parameters for network management such as reactive power compensation factors almost in real time.
Our research in the field of machine learning therefore aims to contribute domain knowledge about the power grid and the energy market in the form of architectures for artificial neural networks to artificial intelligence and to make use of the methods of deep learning to enable prototypes for cyber-resilient network management and economic, intelligent action in energy markets. We work closely with the Computational Intelligence Department at the Carl von Ossietzky University of Oldenburg and play a key role in shaping the Deep Learning Competence Cluster.
Distributed and learning systems
Increasing decentralisation of energy supply is leading to increasing decentralisation of control and monitoring intelligence. We take the spatial and topological distribution of system components into account by developing distributed, (partially) autonomous agent systems. We work closely with the Department of Energy Informatics at Leibniz Universität Hannover and the OFFIS group Simulation and Agents in Multiple Domains. Our focus is on upgrading individual agents, who learn operating and trading strategies independently and bring the flexibility of the components they represent optimally into the overall system. The issues of network stability and the provision of regional system services also play a key role in this context. Learning agents turn decentralised energy systems into valuable, pro-active assets for distribution network operation. This contributes to a reliable and efficient energy supply and contributes to mastering the increasing complexity of the overall system. Furthermore, the detection of misconduct and other anomalies in power and communication networks is another part of our work.
Our research in the field of distributed and learning systems therefore aims at learning network and system characteristics and AI-supported analysis of possible attack scenarios. The unique research field of Adversarial Resilience Learning enables us to make the power grid a self-adapting, secure and cyber-resilient overall system, even in the case of strong digitalization. This goal connects us with the Automation, Communication and Control group, which researches the stable and reliable operation of dynamic systems.
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Projects
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dashPORT
Port Energy Management Dashboard: Digital control room for analysis and control of energy flows in ports
Duration: 2019 - 2022F
FRESH
Flexibilitätsmanagement und Regelenergiebereitstellung von Schwerlastfahrzeugen im Hafen (sorry - only available in German)
Duration: 2019 - 2021P
Pyrate
Polymorphic agents as cross-sectional software technology for the analysis of the operational safety of cyber-physical systems
Duration: 2019 - 2022T
TRANSENSE
Transfer learning for AI business model innovations in digital, transparent distribution grids
Duration: 2020 - 2023 Publications
2020
Analyzing Power Grid, ICT, and Market Without Domain Knowledge Using Distributed Artificial Intelligence
Veith, Eric MSP and Balduin, Stephan and Wenninghoff, Nils and Tröschel, Martin and Fischer, Lars and Nieße, Astrid and Wolgast, Thomas and Sethmann, Richard and Fraune, Bastian and Woltjen, Torben; CYBER 2020, The Fifth International Conference on Cyber-Technologies and Cyber-Systems; October / 2020
Evaluating Different Machine Learning Techniques as Surrogate for Low Voltage Grids
Stephan Balduin and Tom Westermann and Erika Puiutta; 2020
Explainable Reinforcement Learning: A Survey
Puiutta, Erika and Veith, Eric M. S. P.; Machine Learning and Knowledge Extraction; March / 2020
Flexibility management and provisionof balancing services with battery-electricautomated guided vehicles in the Hamburgcontainer terminal Altenwerder
Stefanie Holly, Astrid Nieße, Martin Tröschel, Lasse Hammer, Christoph Franzius, Viktor Dmitriyev, Johannes Dorfner, Eric MSP Veith, Christine Harnischmacher, Maike Greve, Kristin Masuch, Lutz Kolbe, Boris Wulff & Julian Kretz ; Oct / 2020
Large-Scale Co-Simulation of Power Grid and Communication Network Models with Software in the Loop
Veith, Eric MSP and Kazmi, Jawad and Balduin, Stephan; ENERGY 2020, The Tenth International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies; September / 2020
Real-Time Capable Optimal Power Flow With Artificial Neural Networks
Wolgast, Thomas; Abstracts from the 9th DACH+ Conference on Energy Informatics; October / 2020
Robust and Deterministic Scheduling of Power Grid Actors
Frost, Emilie and Veith, Eric MSP and Fischer, Lars; 7th International Conference on Control, Decision and Information Technologies (CoDIT); June / 2020
The Spectrum of Proactive, Resilient Multi-Microgrid Scheduling: A Systematic Literature Review
Spiegel, Michael H and Veith, Eric and Strasser, Thomas I; Energies; 2020
2019
Adaptive Overlay Network Topologies of Smart Grid Services with Dynamic Constraint Hierarchies
Frauke Oest; Energy Informatics; 9 / 2019
Adversarial Resilience Learning — Towards Systematic Vulnerability Analysis for Large and Complex Systems
Fischer, Lars and Memmen, Jan-Menno and Veith, Eric M. S. P. and Tröschel, Martin; ENERGY 2019, The Ninth International Conference on Smart Grids, Green Communications and IT Energy-aware Technologies; 2019
