arXiv subjects

Can Berk Saner

Publications and source records attributed to Can Berk Saner.

3 recordsLinked to original sources

arXiv2026

Fairness-Guaranteed Online Power Allocation Policies for EV Fast Charging Stations

The rapid expansion of electric vehicles (EVs) necessitates scalable fast charging station (FCS) infrastructure. These stations are often oversubscribed, with total port rating exceeding a station-level power cap. In such settings, fair real-time power allocation is essential to secure equitable access while maximizing infrastructure utilization. Existing methods typically assume conventional FCS architectures, depend on prior battery or charge curve data, and lack theoretical guarantees in online settings. To address these limitations, this paper proposes computationally efficient, fairness-guaranteed online power allocation policies for both conventional and modular FCSs that use only instantaneous power requests. Drawing from fair division theory, we formalize a fairness framework with envy-freeness, Pareto efficiency, and proportionality as per-slot criteria, together with a fairness criterion defined over the charging session. For conventional FCSs, Fair-Opap-C follows the classical progressive filling algorithm and guarantees a fair allocation, and we prove that, under a mild condition on the charge curve, it also satisfies the session-level fairness criterion. For modular FCSs, we propose Fair-Opap-M, a novel policy that guarantees a fair allocation, and establish bounds on its session-level fairness under conditions on EV power requests. Simulations against seven benchmarks from EV charging and fair division literature show that the proposed policies satisfy all per-slot fairness criteria and attain the highest session-level fairness among policies that fully utilize station capacity. Both scale as O(n log n) in the number of connected EVs and run below 1.3 ms with 500 connected EVs, orders of magnitude faster than optimization-based approaches.

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arXiv2025

A TSO-DSO Coordination Framework via Analytical Representation and Monetization of PQV-Based Distribution System Flexibility

As the role of distribution system (DS) flexibility in transmission system operator (TSO) network management becomes increasingly vital, data privacy concerns hinder seamless interoperability. The notion of the feasible operating region (FOR), defined in the PQ domain, has emerged as a promising privacy-preserving approach. However, effectively leveraging FOR in TSO operations remains challenging due to three key factors: its accurate determination in large-scale, meshed DS networks; its tractable analytical representation; and its economic valuation. In the present paper, we propose a novel AC optimal power flow (OPF)-based method to construct a three-dimensional PQV-FOR, explicitly accounting for voltage variability and diverse flexibility-providing unit (FPU) characteristics. The construction process employs a two-stage sampling strategy that combines bounding box projection and Fibonacci direction techniques to efficiently capture the FOR. We then introduce an implicit polynomial fitting approach to analytically represent the FOR. Furthermore, we derive a quadratic cost function over the PQV domain to monetize the FOR. Thus, the proposed framework enables single-round TSO-DSO coordination: the DSO provides an analytical FOR and cost model; the TSO determines operating point at the point of common coupling (PCC) within the FOR-based AC-OPF; and the DSO computes FPU dispatch by solving its local OPF, without computationally intensive disaggregation or iterative coordination. Case studies on meshed DS with up to 533 buses, integrated into TS, demonstrates the method's efficiency compared to standard AC-OPF. On average, the proposed approach yields negligible cost deviations of at most 0.058% across test cases, while reducing computation times by up to 58.11%.

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arXiv2025

Privacy-Preserving Utilization of Distribution System Flexibility for Enhanced TSO-DSO Interoperability: A Novel Machine Learning-Based Optimal Power Flow Approach

Due to the transformation of the power system, the effective use of flexibility from the distribution system (DS) is becoming crucial for efficient network management. Leveraging this flexibility requires interoperability among stakeholders, including Transmission System Operators (TSOs) and Distribution System Operators (DSOs). However, data privacy concerns among stakeholders present significant challenges for utilizing this flexibility effectively. To address these challenges, we propose a machine learning (ML)-based method in which the technical constraints of the DSs are represented by ML models trained exclusively on non-sensitive data. Using these models, the TSO can solve the optimal power flow (OPF) problem and directly determine the dispatch of flexibility-providing units (FPUs), in our case, distributed generators (DGs), in a single round of communication. To achieve this, we introduce a novel neural network (NN) architecture specifically designed to efficiently represent the feasible region of the DSs, ensuring computational effectiveness. Furthermore, we incorporate various PQ charts rather than idealized ones, demonstrating that the proposed method is adaptable to a wide range of FPU characteristics. To assess the effectiveness of the proposed method, we benchmark it against the standard AC-OPF on multiple DSs with meshed connections and multiple points of common coupling (PCCs) with varying voltage magnitudes. The numerical results indicate that the proposed method achieves performant results while prioritizing data privacy. Additionally, since this method directly determines the dispatch of FPUs, it eliminates the need for an additional disaggregation step. By representing the DSs technical constraints through ML models trained exclusively on non-sensitive data, the transfer of sensitive information between stakeholders is prevented.

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