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Andreas Ulbig

Publications and source records attributed to Andreas Ulbig.

At least 19 recordsLinked to original sources

Safety Screening for Voltage Control in Active Distribution Grids via Distributionally Robust Conformal Screening

Deploying a new control policy for voltage control in active distribution grids requires evidence that physical limits will be satisfied before the policy is tested on the physical grid. This assessment is difficult for two reasons. First, simulations cannot capture every disturbance, modeling error, and device interaction present in the real grid. Second, historical measurements reflect operation under existing control policies, whereas a new policy may drive the grid into different operating conditions. To address these challenges, we propose Distributionally Robust Conformal Safety Screening (DR-CSS), a policy-agnostic framework for pre-deployment, scenario-by-scenario screening of a new control policy using historical data and a nominal simulator. For each new scenario, the simulator predicts a future voltage trajectory for the whole grid; DR-CSS then constructs a conformal safety interval around this prediction using historical simulation-to-reality errors. The interval is further enlarged to account for closed-loop changes induced by the deployment of the new policy and its interactions with the remaining controllers. To the best of our knowledge, DR-CSS is the first framework in power systems to combine historical data from an existing control policy with an imperfect simulator for pre-deployment safety screening of a new policy. Experiments on the IEEE 33-bus and IEEE 141-bus systems evaluate the deployment of learning-based voltage control policies and show that DR-CSS identifies all unsafe test scenarios. To reduce unnecessary warnings on safe scenarios, we adapt the safety intervals to different operating conditions and gradually introduce new policies with recalibration after each stage. These extensions increase the informational value of the safety screening and support safer deployment decisions in active distribution grids.

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Influence of Controller Tuning on Cascaded Flexibility Provision with Feedback Optimization

The coordination of a large number of flexibility-providing units across various grid layers requires innovative control concepts. This is needed, e.g., to allow active distribution systems to provide ancillary services for the transmission system. A cascaded control structure based on Online Feedback Optimization (OFO) can be used to meet flexibility requests at the point of common coupling by tracking an active power set point at the point of common coupling. This paper investigates the practical influence of the parameterization of the individual controllers on the performance of the hierarchical flexibility provision in three case studies. One case study includes a two-level controller cascade acting on one medium and two low voltage grids, and the other one includes a three-level cascade acting on low to high voltage levels. The results show that the behavior of one controller is highly dependent on the choice of control parameters of the other controllers in the cascade. Additionally, the choice of parametrization has a significant impact on the accuracy and speed of flexibility provision. A third case study investigates the effects of model mismatch and measurement noise on the appropriate selection of parameters. Overall, careful tuning enables the efficient vertical coordination of flexibility-providing units with a cascaded structure based on OFO.

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Data-Driven Sequential Market Optimization for Front-of-the-Meter Battery Energy Storage Systems

The growing integration of Battery Energy Storage Systems into electricity markets has highlighted the importance of coordinated participation across energy and balancing services to fully exploit their operational flexibility. However, existing revenue-stacking models often simplify market sequences and neglect the impact of rolling forecasts, leading to unrealistic scheduling and overestimated revenues. This paper addresses this gap by introducing a sequential, data-driven optimization framework for Front-of-the-Meter Battery Energy Storage Systems that mirrors actual market operations. The framework explicitly models market mechanisms and its respective Gate Closure Times across Frequency Containment Reserve, automated Frequency Restoration Reserve, Day-Ahead Auction, and Intraday Continuous markets. Each market stage optimizes expected revenue over all remaining markets using updated price forecasts while maintaining feasibility within both technical and regulatory limits. A key contribution is the opportunity-cost-based bidding strategy, which endogenously derives market-consistent bid prices and quantities from residual capacity optimization. Validation for a representative operating day demonstrates that the framework yields consistent and feasible schedules, effectively adapts to updated forecasts, and minimizes deviations between planned and realized dispatch, thereby enhancing the realism and profitability of BESS operation.

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Physics-Informed Neural Network for Modeling the Dynamic Behavior of Grid-Forming Converters

This paper investigates physics-informed neural networks for modeling the full dynamic behavior of droop-controlled grid-forming converters. The approach is trained on synthetic data generated via numerical solvers and benchmarked against both traditional integration methods and a vanilla neural network. Results show higher predictive accuracy than the vanilla network using the same training data and substantially reduced runtime compared with numerical solvers.

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Robustness of Reinforcement Learning-Based Congestion Management in Low-Voltage Grids

Increases in photovoltaic generation, charging of electric vehicles and heat-pump demand challenge operating limits in low-voltage distribution grids. This requires curative curtailment methods that can operate under sparse observability, noisy measurements, and imperfect grid models. Unlike prior end-to-end reinforcement-learning approaches for partially observable curtailment, this work decouples congestion detection and control by combining a random-forest violation pre-classifier with an actor-critic controller, and evaluates its robustness to measurement noise and grid-parameter mismatch. The framework is tested on a real low-voltage grid using synthetic future operating scenarios with low observability and controllability. With accurate grid parameters, the controller reduces total violation magnitude by 98.9%, and this performance remains nearly unchanged under the tested measurement-noise settings. Grid-model mismatch proves to be more challenging, but the controller still mitigates most violations under the tested mismatch assumptions.

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Momentum-Accelerated Online Feedback Optimization for Power System Flexibility

Flexibility is increasingly gaining importance in modern power system operation. This paper presents a controller framework based on Online Feedback Optimization for real-time coordination of power system flexibility. The proposed approach introduces a momentum-augmented projection-step to accelerate convergence and improve dynamic performance. We derive the controller formulation, and evaluate its performance and stability in two representative case studies. The first examines online congestion management in distribution feeders, and the second addresses multi-layer flexibility dispatch across system interfaces. Numerical results demonstrate that the momentum-based controller achieves faster convergence and maintains constraint satisfaction, highlighting its potential for real-time flexibility control in large-scale power systems.

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Dynamic Modeling, Analysis, and Validation of Dual-Port Grid-Forming Control for Hybrid AC/DC Systems

This work investigates the transient and dynamical behavior of hybrid AC/DC systems using dual-port grid-forming (GFM) control. A generalized modeling framework for hybrid AC/DC networks is first introduced that accounts for converter, control, and network circuit dynamics and arbitrary network topologies. This modeling framework is applied to low-voltage networks to analyze the performance of dual-port grid-forming (GFM) control. The results demonstrate that active damping by dual-port GFM control is effective at improving the transient response and mitigating oscillations. In contrast, the steady-state response characteristics can be adjusted independently with minimal impact on damping characteristics. The dynamic model and results are validated through hardware experiments for three prototypical system architectures. Furthermore, we demonstrate that low-voltage DC distribution interfaced by AC/DC converters using dual-port GFM control, can serve both as the sole interconnection between AC distribution systems and in parallel to an AC connection, thereby enhancing the operational flexibility of low- and medium-voltage distribution networks.

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Partially Observable Residual Reinforcement Learning for PV-Inverter-Based Voltage Control in Distribution Grids

This paper introduces an efficient Residual Reinforcement Learning (RRL) framework for voltage control in active distribution grids. Voltage control remains a critical challenge in distribution grids, where conventional Reinforcement Learning (RL) methods often suffer from slow training convergence and inefficient exploration. To overcome these challenges, the proposed RRL approach learns a residual policy on top of a modified Sequential Droop Control (SDC) mechanism, ensuring faster convergence. Additionally, the framework introduces a Local Shared Linear (LSL) architecture for the Q-network and a Transformer-Encoder actor network, which collectively enhance overall performance. Unlike several existing approaches, the proposed method relies solely on inverters' measurements without requiring full state information of the power grid, rendering it more practical for real-world deployment. Simulation results validate the effectiveness of the RRL framework in achieving rapid convergence, minimizing active power curtailment, and ensuring reliable voltage regulation.

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Stability and Performance of Online Feedback Optimization for Distribution Grid Flexibility

The integration of distributed energy resources (DERs) into sub-transmission systems has enabled new opportunities for flexibility provision in ancillary services such as frequency and voltage support, as well as congestion management. This paper investigates the stability and performance of Online Feedback Optimization (OFO) controllers in ensuring reliable flexibility provision. A hierarchical control architecture is proposed, emphasizing safe transitions between system states within the Feasible Operating Region (FOR). We evaluate the controller's stability and performance through simulations of transitions to the vertices of the FOR, analyzing the impact of tuning parameters. The study demonstrates that controller stability is sensitive to parameter tuning, particularly gain and sensitivity approximations. Results demonstrate that improper tuning can lead to oscillatory or unstable behavior, highlighting the need for systematic parameter selection to ensure reliable operation across the full flexibility range.

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Towards Safety and Security Testing of Cyberphysical Power Systems by Shape Validation

The increasing complexity of cyberphysical power systems leads to larger attack surfaces to be exploited by malicious actors and a higher risk of faults through misconfiguration. We propose to meet those risks with a declarative approach to describe cyberphysical power systems and to automatically evaluate security and safety controls. We leverage Semantic Web technologies as a well-standardized framework, providing languages to specify ontologies, rules and shape constraints. We model infrastructure through an ontology which combines external ontologies, architecture and data models for sufficient expressivity and interoperability with external systems. The ontology can enrich itself through rules defined in SPARQL, allowing for the inference of knowledge that is not explicitly stated. Through the evaluation of SHACL shape constraints we can then validate the data and verify safety and security constraints. We demonstrate this concept with two use cases and illustrate how this solution can be developed further in a community-driven fashion.

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Graph-based Impact Analysis of Cyber-Attacks on Behind-the-Meter Infrastructure

Behind-the-Meter assets are getting more interconnected to realise new applications like flexible tariffs. Cyber-attacks on the resulting control infrastructure may impact a large number of devices, which can result in severe impact on the power system. To analyse the possible impact of such attacks we developed a graph model of the cyber-physical energy system, representing interdependencies between the control infrastructure and the power system. This model is than used for an impact analysis of cyber-attacks with different attack vectors.

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Safe Trajectory Sets for Online Operation of Power Systems under Uncertainty

Flexibility provision from active distribution grids requires efficient and robust methods of optimization and control suitable to online operation. In this paper we introduce conditions for the safe operation of feedback optimization based controllers. We use the feasible operating region of a controlled system as bounds for safe system states and evaluate the trajectories of the controller based on the projection of the full system state onto the two-dimensional PQ-plane. We demonstrate the defined conditions for an exemplary sub-transmission system. We show that the proposed method is suitable to evaluate controller performance and robustness for systems subject to disturbances.

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Towards a Comprehensive Framework for Cyber-Incident Response Decision Support in Smart Grids

The modernization of power grid infrastructures necessitates the incorporation of decision support systems to effectively mitigate cybersecurity threats. This paper presents a comprehensive framework based on integrating Attack-Defense Trees and the Multi-Criteria Decision Making method to enhance smart grid cybersecurity. By analyzing risk attributes and optimizing defense strategies, this framework enables grid operators to prioritize critical security measures. Additionally, this paper incorporates findings on decision-making processes in intelligent power systems to present a comprehensive approach to grid cybersecurity. The proposed model aims to optimize the effectiveness and efficiency of grid cybersecurity efforts while offering insights into future grid management challenges.

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Simulation of Multi-Stage Attack and Defense Mechanisms in Smart Grids

The power grid is a critical infrastructure essential for public safety and welfare. As its reliance on digital technologies grows, so do its vulnerabilities to sophisticated cyber threats, which could severely disrupt operations. Effective protective measures, such as intrusion detection and decision support systems, are essential to mitigate these risks. Machine learning offers significant potential in this field, yet its effectiveness is constrained by the limited availability of high-quality data due to confidentiality and access restrictions. To address this, we introduce a simulation environment that replicates the power grid's infrastructure and communication dynamics. This environment enables the modeling of complex, multi-stage cyber attacks and defensive responses, using attack trees to outline attacker strategies and game-theoretic approaches to model defender actions. The framework generates diverse, realistic attack data to train machine learning algorithms for detecting and mitigating cyber threats. It also provides a controlled, flexible platform to evaluate emerging security technologies, including advanced decision support systems. The environment is modular and scalable, facilitating the integration of new scenarios without dependence on external components. It supports scenario generation, data modeling, mapping, power flow simulation, and communication traffic analysis in a cohesive chain, capturing all relevant data for cyber security investigations under consistent conditions. Detailed modeling of communication protocols and grid operations offers insights into attack propagation, while datasets undergo validation in laboratory settings to ensure real-world applicability. These datasets are leveraged to train machine learning models for intrusion detection, focusing on their ability to identify complex attack patterns within power grid operations.

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Encryption-Aware Anomaly Detection in Power Grid Communication Networks

The shift to smart grids has made electrical power systems more vulnerable to sophisticated cyber threats. To protect these systems, holistic security measures that encompass preventive, detective, and reactive components are required, even with encrypted data. However, traditional intrusion detection methods struggle with encrypted traffic, our research focuses on the low-level communication layers of encrypted power grid systems to identify irregular patterns using statistics and machine learning. Our results indicate that a harmonic security concept based on encrypted traffic and anomaly detection is promising for smart grid security; however, further research is necessary to improve detection accuracy.

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On Process Awareness in Detecting Multi-stage Cyberattacks in Smart Grids

This study delves into the role of process awareness in enhancing intrusion detection within Smart Grids, considering the increasing fusion of ICT in power systems and the associated emerging threats. The research harnesses a co-simulation environment, encapsulating IT, OT, and ET layers, to model multi-stage cyberattacks and evaluate machine learning-based IDS strategies. The key observation is that process-aware IDS demonstrate superior detection capabilities, especially in scenarios closely tied to operational processes, as opposed to IT-only IDS. This improvement is notable in distinguishing complex cyber threats from regular IT activities. The findings underscore the significance of further developing sophisticated IDS benchmarks and digital twin datasets in Smart Grid environments, paving the way for more resilient cybersecurity infrastructures.

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A cyber-physical digital twin approach to replicating realistic multi-stage cyberattacks on smart grids

The integration of information and communication technology in distribution grids presents opportunities for active grid operation management, but also increases the need for security against power outages and cyberattacks. This paper examines the impact of cyberattacks on smart grids by replicating the power grid in a secure laboratory environment as a cyber-physical digital twin. A simulation is used to study communication infrastructures for secure operation of smart grids. The cyber-physical digital twin approach combines communication network emulation and power grid simulation in a common modular environment, and is demonstrated through laboratory tests and attack replications.

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AI-based Attacker Models for Enhancing Multi-Stage Cyberattack Simulations in Smart Grids Using Co-Simulation Environments

The transition to smart grids has increased the vulnerability of electrical power systems to advanced cyber threats. To safeguard these systems, comprehensive security measures-including preventive, detective, and reactive strategies-are necessary. As part of the critical infrastructure, securing these systems is a major research focus, particularly against cyberattacks. Many methods are developed to detect anomalies and intrusions and assess the damage potential of attacks. However, these methods require large amounts of data, which are often limited or private due to security concerns. We propose a co-simulation framework that employs an autonomous agent to execute modular cyberattacks within a configurable environment, enabling reproducible and adaptable data generation. The impact of virtual attacks is compared to those in a physical lab targeting real smart grids. We also investigate the use of large language models for automating attack generation, though current models on consumer hardware are unreliable. Our approach offers a flexible, versatile source for data generation, aiding in faster prototyping and reducing development resources and time.

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