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Georgios Pantazis

Publications and source records attributed to Georgios Pantazis.

9 recordsLinked to original sources
arXiv2026

Safety Verification for Voltage Control in Inverter Dominated Radial Distribution Networks under Actuator and Sensor Adversarial Attacks

This paper develops a safety verification framework for inverter-based radial distribution networks under sensor and actuator attacks. Each customer is equipped with a droop-based controller to regulate its voltage within a desirable bound of a reference, i.e., to achieve safety. In our setting, attacks and disturbances enter the droop-based controllers, while actuator attacks corrupt the reactive power injected into the network. Consequently, invariance of the controller state alone does not guarantee compliance with inverter capacities or customer voltage limits. To address this challenge, we derive conditions to guarantee that the reactive power state remains inside a predefined ellipsoid, while at the same time ensuring that the attacked reactive power injections and customer voltages respect predetermined operating limits that hold uniformly for the entire predefined ellipsoid. Together, these conditions guarantee safety under all admissible disturbances and state-dependent sensor and actuator attacks. Finally, we express the resulting containment condition as a linear matrix inequality (LMI) feasibility problem. A five-customer radial network based on the CIGRE benchmark corroborates our theoretical developments in simulations.

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arXiv2026

Data-driven Wasserstein distributionally robust generalized Nash equilibrium problems with heterogeneous uncertainty

We study stochastic generalized Nash equilibrium (GNE) problems with heterogeneous, stochastic uncertainty in the cost functions. To address the distributional uncertainty, we let each agent construct an individual Wasserstein ambiguity set around an empirical distribution obtained from private data. We establish finite-sample robustness guarantees for any distributionally robust (DR) GNE. Focusing on variational equilibria, we derive a DR variational inequality (VI) reformulation and establish asymptotic consistency with the nominal stochastic game. Under mild conditions, we derive an exact finite-dimensional generalized Nash equilibrium reformulation. We then quantify how heterogeneous worst-case distributions affect monotonicity, and derive conditions for uniqueness and equilibrium sensitivity. To compute a variational DR GNE, we propose an inexact projected method and establish convergence under vanishing oracle errors, an asymptotic error bound under bounded oracle errors, and linear convergence under exact oracle evaluations.

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arXiv2026

Certificate-based Synthesis of Coordinated Droop Control for Heterogeneous Radial Distribution Networks

Voltage certificates for droop-controlled radial distribution networks are often constructed from worst-case quantities. In heterogeneous radial networks, this approach can hide where voltage deviations are most likely. Furthermore, such certificates become increasingly conservative as the network sensitivities accumulate. Leveraging the structure of the linearized DistFlow model and slope-restricted droop controllers, we derive tighter deterministic voltage certificates that retain heterogeneous network, disturbance and inverter characteristics of each bus. The certificates reveal the buses and local limitations that dominate certified voltage performance. Worst-case bounds are recovered as a special case. Although tighter, the heterogeneous certificates can still deteriorate downstream due to the network structure. To address this, we leverage our certificates as design variables for coordinated voltage control. Specifically, we develop a virtual droop architecture with coordination and affine feedforward compensation to reshape the effective voltage sensitivity, and formulate a linear program that jointly synthesizes the controller and minimizes its heterogeneous voltage certificates under operational, communication and inverter placement constraints. The resulting controller guarantees the voltage and inverter bounds for all admissible disturbances. Evaluation on two network benchmarks, a five-customer residential feeder and a 26-customer rural network comprising four feeders, demonstrates improved certificates and voltage behaviour under inverter limits.

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arXiv2026

Data-driven Reachable Set Estimation with Tunable Adversarial and Wasserstein Distributional Guarantees

We study finite horizon reachable set estimation for unknown discrete-time dynamical systems using only sampled state trajectories. Rather than treating scenario optimization as a black-box tool, we show how it can be tailored to reachable set estimation, where one must learn a family of sets based on whole trajectories, while preserving probabilistic guarantees on future trajectory inclusion for the entire horizon. To this end, we formulate a relaxed scenario program with slack variables that yields a tunable trade-off between reachable set size and out-of-sample trajectory inclusion over the horizon, thereby reducing sensitivity to outliers. Leveraging the recent results in adversarially robust scenario optimization, we then extend this formulation to account for bounded adversarial perturbations of the observed trajectories and derive a posteriori probabilistic guarantees on future trajectory inclusion. When probability distribution shifts in the Wasserstein distance occur, we obtain an explicit bound on how gracefully the theoretical probabilistic guarantees degrade. For different geometries, i.e., $p$-norm balls, ellipsoids, and zonotopes, we derive tractable convex reformulations and corroborate our theoretical results in simulation.

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arXiv2026

Adversarially and Distributionally Robust Virtual Energy Storage Systems via the Scenario Approach

We study virtual energy storage services based on the aggregation of EV batteries in parking lots under time-varying, uncertain EV departures and state-of-charge limits. We propose a convex data-driven scheduling framework in which a parking lot manager provides storage services to a prosumer community while interacting with a retailer. The framework yields finite-sample, distribution-free guarantees on constraint violations and allows the parking lot manager to explicitly tune the trade-off between economic performance and operational safety. To enhance reliability under imperfect data, we extend the formulation to adversarial perturbations of the training samples and Wasserstein distributional shifts, obtaining robustness certificates against both corrupted data and out-of-distribution uncertainty. Numerical studies confirm the predicted profit-risk trade-off and show consistency between the theoretical certificates and the observed violation levels.

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arXiv2025

Wasserstein Distributionally Robust Nash Equilibrium Seeking with Heterogeneous Data: A Lagrangian Approach

We study a class of distributionally robust games where agents are allowed to heterogeneously choose their risk aversion with respect to distributional shifts of the uncertainty. In our formulation, heterogeneous Wasserstein ball constraints on each distribution are enforced through a penalty function leveraging a Lagrangian formulation. We then formulate the distributionally robust Nash equilibrium problem and show that under certain assumptions it is equivalent to a finite-dimensional variational inequality problem with a strongly monotone mapping. We then design an approximate Nash equilibrium seeking algorithm and prove convergence of the average regret to a quantity that diminishes with the number of iterations, thus learning the desired equilibrium up to an a priori specified accuracy. Numerical simulations corroborate our theoretical findings.

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arXiv2025

Nash equilibrium seeking for a class of quadratic-bilinear Wasserstein distributionally robust games

We consider a class of Wasserstein distributionally robust Nash equilibrium problems, where agents construct heterogeneous data-driven Wasserstein ambiguity sets using private samples and radii, in line with their individual risk-averse behaviour. By leveraging relevant properties of this class of games, we show that equilibria of the original seemingly infinite-dimensional problem can be obtained as a solution to a finite-dimensional Nash equilibrium problem. We then reformulate the problem as a finite-dimensional variational inequality and establish the connection between the corresponding solution sets. Our reformulation has scalable behaviour with respect to the data size and maintains a fixed number of constraints, independently of the number of samples. To compute a solution, we leverage two algorithms, based on the golden ratio algorithm. The efficiency of both algorithmic schemes is corroborated through extensive simulation studies on an illustrative example and a stochastic portfolio allocation game, where behavioural coupling among investors is modeled.

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arXiv2025

User-centric Vehicle-to-Grid Optimization with an Input Convex Neural Network-based Battery Degradation Model

We propose a data-driven, user-centric vehicle-to-grid (V2G) methodology based on multi-objective optimization to balance battery degradation and V2G revenue according to EV user preference. Given the lack of accurate and generalizable battery degradation models, we leverage input convex neural networks (ICNNs) to develop a data-driven degradation model trained on extensive experimental datasets. This approach enables our model to capture nonconvex dependencies on battery temperature and time while maintaining convexity with respect to the charging rate. Such a partial convexity property ensures that the second stage of our methodology remains computationally efficient. In the second stage, we integrate our data-driven degradation model into a multi-objective optimization framework to generate an optimal smart charging profile for each EV. This profile effectively balances the trade-off between financial benefits from V2G participation and battery degradation, controlled by a hyperparameter reflecting the user prioritization of battery health. Numerical simulations show the high accuracy of the ICNN model in predicting battery degradation for unseen data. Finally, we present a trade-off curve illustrating financial benefits from V2G versus losses from battery health degradation based on user preferences and showcase smart charging strategies under realistic scenarios.

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