Antoine Lesage-Landry

Marco Mussetta

Sonia Leva

The increasing penetration of renewable energy sources in microgrids demands accurate day-ahead forecasting to support optimal battery energ… (see more)y storage system (BESS) scheduling. Yet the conventional predict-then-optimize paradigm trains forecasters on statistical accuracy metrics that are agnostic to the asymmetric and time-varying cost impacts of forecast errors. This work proposes a decision-focused learning (DFL) framework that trains gated recurrent unit forecasters for both load demand and photovoltaic generation directly on the downstream operational cost of a two-stage energy management system, where day-ahead BESS commitments are fixed prior to real-time balancing. Gradients are backpropagated from the real-time operational cost through both optimization stages to the forecaster weights via implicit differentiation, enabling the forecasters to internalize the penalty structure of the scheduling problem. The framework is validated on the MG2Lab experimental microgrid testbed at Politecnico di Milano across two operational scenarios of increasing grid constraint and eight forecasting configurations comprising single-source and combined-forecaster settings. Results illustrate that the DFL-trained pipeline reduces operational cost by 3.78% under normal grid conditions and by 30.60% under grid-constrained conditions relative to the accuracy- optimized baseline, while nearly eliminating load shedding events. Notably, the DFL forecasters exhibit higher statistical errors than their accuracy-trained counterparts yet achieve consistently lower operational costs, confirming that forecast accuracy and decision quality do not share a monotonic relationship in constrained microgrid scheduling.

2026-10-09

arXiv (preprint)

An Optimal Design of Time-of-Use Tariff to Mitigate Overloads From Residential Electric Vehicle Charging on Distribution Networks

Feng Li

Ilhan Kocar

High penetration of electric vehicle (EV) charging creates equipment overload risks in power distribution networks, potentially reducing ass… (see more)et lifetime. This article develops an optimal time-of-use (TOU) tariff strategy to mitigate overloads by shifting residential EV charging demand, without requiring communication infrastructure or direct load control policies. We formulate a bi-level optimization problem to determine optimal rate levels that restore equipment lifetime while minimizing utility costs. The problem is reformulated as a convex optimization with proven convergence properties, enabling an efficient solution for large-scale networks. The solution guarantees that no new overload occurs after shifting the EV loads. A novel time-series analysis method is used to estimate loading impacts and to evaluate the effectiveness of candidate strategies. The proposed strategy has a simple TOU structure consistent with utilities' rate structure in place, making it suitable for adoption. We validate the approach on the IEEE 8500-node test feeder. Results demonstrate that at 80% EV penetration rate, the strategy reduces transformer overload peaks by 20%, while maintaining equipment's nominal lifetime and achieving 42.2% cost savings for utilities compared to early equipment replacement.

2026-09-30

IEEE Transactions on Industrial Informatics (published)

AI-Enabled Wireless Propagation Modeling and Radio Environment Maps for 5G Aerial Wireless Networks

Gautham Reddy

Kürşat Tekbıyık

Bryton Petersen

Gunes Karabulut Kurt

Ismail Güvenç

With the gaining prominence of aerial mobility applications, their success depends on the seamless integration of terrestrial and non-terres… (see more)trial network connectivity. However, providing reliable connectivity from terrestrial telecommunication networks remains challenging due to multi-cell interference from base stations (BSs) under line-of-sight (LoS) conditions to unmanned aerial vehicles (UAVs), coverage holes caused by antenna sidelobe degradation, localized multipath fading effects, and the high-speed dynamics of aerial users. To model such complexities, often exacerbated by sparse real-world data, this work proposes a dual-stage radio environment map (REM) framework. Our approach physically decouples the channel modeling, where a spatial Transformer first anchors the deterministic, large-scale path loss geometry, while a gated recurrent unit (GRU) subsequently extrapolates the stochastic, localized fast- fading deviations. By reformulating 3D spatial interpolation as a 1D radial sequence prediction task, the framework inherently aligns with the physics of propagation. We evaluate the proposed framework against state-of-the-art baselines, including 3D Kriging, UNet, Mamba, and Inception, using empirical 5G datasets. The results demonstrate improved intra-site generalization across diverse altitudes, user dynamics, and reference signal received power (RSRP) datasets, achieving signal-strength predictions with errors near 3 dB and REM spatial-similarity indices exceeding 0.75. Finally, we examine the influence of REMs and channel rank conditions on UAV channel quality, underscoring the necessity of reliable channel modeling for robust aerial connectivity.

2026-09-21

arXiv (preprint)

Assessment of Stability and Control Interaction Risks in PV Parks with Two-Stage Grid-Forming Control

Dabeaurard Tho

Jean Mahseredjian

Nasim Rashidirad

Alireza Masoom

The stability of grid-forming (GFM) inverter-based resources (IBRs) is typically analyzed under the assumption that they are supplied by bat… (see more)teries or ideal DC sources, without accounting for variations in the DC link voltage. This paper contributes to small-signal stability analysis of photovoltaic (PV) parks operating in a two-stage GFM configuration: reserve grid-forming mode (R-GFM), which provides power reserves, and limited grid-forming mode (L-GFM), which delivers the maximum available power. A transition strategy between these modes is adopted and validated. Numerical simulations are conducted in EMTP® on a PV park connected to a weak grid under both standard and low irradiance conditions, comparing the two-stage GFM operation to the standard grid-following (GFL) control. Results show that the PV park preserves system stability in GFM modes, whereas instability occurs under GFL operation.

2026-07-18

IEEE Power & Energy Society General Meeting (published)

Impedance Model Domains for the Stability Analysis of Multi-Inverter-Based-Resources Systems

Keijo Jacobs

Renan Morais Furlaneto

Dabeaurard Tho

Nasim Rashidirad

Jean Mahseredjian

Modern power systems are increasingly dominated by inverter-based resources (IBRs), which rely on fast control systems for its power-electri… (see more)c converters. These IBRs are vulnerable to oscillating interactions with other grid components. The stability of such power systems can be studied through impedance-based stability assessment. Different impedance modelling domains offer varying trade-offs in terms of accuracy, ease of implementation, and interpretability. Each provides distinct insight into the dynamic behavior and control interactions of the system. This paper presents a concise comparative overview of impedance model types, illustrating their practical implications through representative case studies. All impedance models are measured from detailed electromagnetic transient models.

2026-07-18

IEEE Power & Energy Society General Meeting (published)

Optimal energy trading in residential prosumer clusters via graphon mean field games

Mohamad Aziz

Hussein Suprême

Hanane Dagdougui

2026-07-13

Electric Power Systems Research (published)

Copositive Characterizations of Convex Hull Pricing

Madhusudan Ghosh

Joshua Adam Taylor

Due to the nonconvex binary constraints of unit commitment (UC), no uniform linear pricing scheme supports the optimal dispatch. Convex hull… (see more) pricing (CHP) and copositive duality pricing (CDP) both address this problem. CHP derives the price from the subgradient of the value function of the convex hull relaxation of UC. CDP refers to several different pricing mechanisms that can be constructed from the dual multipliers of the completely positive programming reformulation. In this work, we define a centralized convex hull price over the joint feasible set of UC and prove that, under non-degeneracy, it coincides with the marginal copositive duality price. Numerical experiments on the Scarf example validate this equivalence and quantify the pricing gap introduced by the semidefinite restriction.

2026-07-12

arXiv (preprint)

Parallel versions of the mesh adaptive direct search algorithm

Sébastien Le Digabel

Samuel Mendoza

Christophe Tribes

This work surveys the different parallel variants of the mesh adaptive direct search (MADS) algorithm for constrained blackbox optimization.… (see more) These problems can inherently imply high computational costs due to the possible large number of variables and multi-modality of the search space. In addition, the potential time-intensive nature and time heterogeneity of the blackboxes defining the problem prompts the need for efficient implementations. Parallelism emerges as an actionable solution to mitigate computation time, as modern computer systems rely on multi-core architecture. The reviewed methods employ diverse levels of parallelism and distinct parallel strategies to effectively tackle each aspect outlined above. The manuscript details the practical implementations, provides computational results, and offers insights into the advantages and limitations of each MADS parallel method.

2026-07-08

arXiv (preprint)

Convex training of Lipschitz-regularized shallow neural networks

Chao Yin

In this work, we introduce a training procedure for shallow neural networks that promotes robustness against adversarial attacks. We solve a… (see more) non-convex Lipschitz-regularized training program by introducing a convex restriction that can be efficiently solved to global optimality. Our approach can be employed as a post-processing step by taking a pre-trained network as an initial solution to then solving the convex program whose optimal network is guaranteed to be no worse than the initial one. We illustrate the improvements of our training procedure with experiments using real world datasets for regression tasks under an adversarial setting. We show numerically that solving our proposed convex program yields networks with lower objective values on the Lipschitz-regularized program compared to existing methods. Additionally, we show that on certain datasets, networks obtained using our convex training program are both more accurate and robust with respect to adversarial attacks.

2026-06-16

arXiv (preprint)

Online Sketched Newton-Raphson

Jean-Luc Lupien

Yuen-Man Pun

Youssef Diouane

Iman Shames

In online convex optimization (OCO), a decision-maker is confronted with an unknown environment and seeks to play an optimal sequence of dec… (see more)isions on a short time-scale using only past information. Recent advances in second-order OCO methods have demonstrated tighter regret bounds and improved empirical performance over traditional first-order methods. However, this performance comes at a cost: a matrix inversion is now required, which scales with the cube of the size of the problem. In this work, we propose sketching to mitigate this limitation. Specifically, we present the online sketched Newton-Raphson method (OSNR) which preserves the tight regret bounds obtained with second-order methods while presenting a strict computational improvement in terms of complexity. We discuss three application scenarios of OSNR: online root finding, unconstrained OCO, and time-varying equality-constrained OCO, and present their respective regret and a constraint violation bound for the latter. In all three applications, OSNR achieves sublinear dynamic regret bounds. For the equality-constrained case, the extension OSNR with equality constraints OSNR-EC is shown to yield sublinear cumulative constraint violation. Finally, we illustrate the performance of OSNR and OSNR-EC on two numerical examples, viz., online position tracking and optimal power flow, and observe that OSNR and OSNR-EC exhibit high performance even at low sampling rates.

2026-05-26

arXiv (preprint)

PIKAN: Physics-Inspired Kolmogorov-Arnold Networks for Explainable UAV Channel Modelling

Kürşat Tekbıyık

Gunes Karabulut Kurt

Unmanned aerial vehicle (UAV) communications demand accurate yet interpretable air-to-ground (A2G) channel models that can adapt to non-stat… (see more)ionary propagation environments. While deterministic models offer interpretability and deep learning (DL) models provide accuracy, both approaches suffer from either rigidity or a lack of explainability. To bridge this gap, we propose the Physics-Inspired Kolmogorov-Arnold Network (PIKAN) that embeds physical principles (e.g., free-space path loss, two-ray reflections) into the learning process. Unlike physics-informed neural networks (PINNs), PIKAN is more flexible for applying physical information because it introduces them as adaptable inductive biases. Thus, it enables a more flexible training process. Experiments on UAV A2G measurement data show that PIKAN achieves comparable accuracy to DL models while providing symbolic and explainable expressions aligned with propagation laws. Remarkably, PIKAN achieves this performance with only 232 parameters, making it up to 37 times lighter than multilayer perceptron (MLP) baselines with thousands of parameters, without sacrificing correlation with measurements and also providing symbolic expressions. These results highlight PIKAN as an efficient, interpretable, and scalable solution for UAV channel modelling in beyond-5G and 6G networks.

2026-03-06

IEEE Aerospace Conference (published)

Reliability Assessment of Distribution Systems With Microgrids Using Discrete-Time Markov Chains

Jean-William Lauzon

Ilhan Kocar

Microgrids can improve the reliability and resiliency of modern distribution systems. The stochasticity of local non-dispatchable distribute… (see more)d energy resources (NDDERs), combined with the time-dependency of battery energy storage systems (BESSs) and load shedding strategies (LSSs), complicates the reliability assessment of distribution networks embedded with microgrids. In this work, we propose a minimal cut-set method using a discrete-time Markov chain to perform the time-series adequacy assessment. Our method offers an alternative to sequential Monte Carlo simulations (SMCSs) to account for the stochasticity of NDDERs and the time-dependency of BESSs and LSSs. Case studies on modified IEEE-RTBS Bus2 and IEEE 123-Test Feeder systems assess the accuracy of the method when compared with SMCSs.

2026-01-31

IEEE Transactions on Power Delivery (published)