arXiv subjects

Savvas Panagi

Publications and source records attributed to Savvas Panagi.

4 recordsLinked to original sources

arXiv2026

Dynamic Flexibility Requests in Local Flexibility Markets: Quantifying the DSO Willingness to Pay

Local Flexibility Markets (LFMs) require Distribution System Operators (DSOs) to determine both the quantity of flexibility to procure and the corresponding willingness to pay during market clearing. Existing approaches typically rely on unrealistic centralized AC-OPF clearing algorithms or strictly localized, static flexibility requests driven primarily by congestion management, while the economic value of flexibility is largely neglected. This paper proposes a dynamic flexibility-request methodology in which the DSO's willingness to pay is embedded directly into the market-clearing objective by monetizing transformer and cable aging, network losses, and voltage congestion. To enable computationally efficient clearing, exact convex piecewise-linear epigraph reformulations of the IEEE C57.91 transformer aging model and an Arrhenius-based cable aging model are developed and formally proven to preserve exactness. The proposed framework is validated on a modified CIGRE MV benchmark and compared with a recent state-of-the-art flexibility-request methodology. The results demonstrate significantly higher market liquidity, more efficient flexibility procurement, and improved network operations while preserving the market's transparency and non-discrimination principles.

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arXiv2026

A Benchmarking Case Study for Local Flexibility Markets: Network, Scenarios, and Open Inputs

External inputs to local flexibility markets, network models, demand scenarios, flexibility offers, and asset thermal, economic parameters do not change the clearing methodology, yet they are routinely defined under heterogeneous assumptions, which hinders reproducible benchmarking. This paper provides a complete, methodology-agnostic input set built around a modified CIGRE MV network with prescribed base-load and stress-load conditions, synthetic flexibility offers, wholesale prices, and transformer and cable parameters. Baseline AC operating points (voltages, loadings, and aging) are reported for both days so that alternative flexibility-request and clearing methods can be applied to the same inputs and compared fairly. The associated data are released openly on Zenodo.

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arXiv2026

Enhanced Optimal Power Flow Using a Trained Neural Network Surrogate for Distribution Grid Constraints

The growing penetration of distributed energy resources (DERs), electric vehicles (EVs), and heat pumps (HPs) in distribution networks underscores the need for secure, computationally efficient optimal power flow (OPF) solutions. Traditional OPF formulations often suffer from scalability limitations and may rely on relaxations/approximations whose exactness is not guaranteed. This paper proposes a framework in which a trained neural network (NN) surrogate is embedded directly within the OPF as a constraint replacement. Specifically, the nonlinear power-flow-to-voltage mapping is replaced by an exact mixed-integer linear encoding of the NN (i.e., the NN input-output map is represented without approximation), while all remaining OPF constraints are preserved. Using a realistic low-voltage network with integrated PV, EVs, and HPs, the proposed method achieves high voltage accuracy during post-solution AC power flow validation, with maximum deviations of less than 1.0 V in the examined test cases. The resulting NN-OPF problems are solved to global optimality within the MILP solver tolerance, and numerical results demonstrate substantially reduced computation time compared to nonlinear OPF models, with performance competitive with SOCP-based DistFlow formulations.

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arXiv2026

A Thermal-Electrical Co-Optimization Framework for Active Distribution Grids with Electric Vehicles and Heat Pumps

The growing electrification of transportation and heating through Electric Vehicles (EVs) and Heat Pumps (HPs) introduces both flexibility and complexity to Active Distribution Networks (ADNs). These resources provide substantial operational flexibility but also create tightly coupled thermal-electrical dynamics that challenge conventional network management. This paper proposes a unified co-optimization framework that integrates a calibrated 3R2C grey-box building thermal model into a network-constrained Optimal Power Flow (OPF). The framework jointly optimizes EVs, HPs, and photovoltaic systems while explicitly enforcing thermal comfort, Distributed Energy Resource (DER) limits, and full power flow physics. To maintain computational tractability, Second-Order Cone Programming (SOCP) relaxations are evaluated on a realistic low-voltage feeder. The analysis shows that, despite network heterogeneity violating some theoretical exactness conditions, the relaxation remains exact in practice. Comparative assessments of convex DistFlow, bus injection, and branch flow formulations reveal that convex DistFlow achieves sub-second runtimes and near-optimal performance even at high DER penetration levels. Simulations confirm the effectiveness of coordinated scheduling, yielding reductions of 41% in transformer aging, 54% in losses, and complete elimination of voltage violations, demonstrating the value of integrated thermal-electrical coordination in future smart grids.

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