Hüseyin K. Çakmak

Burak Dindar, Can Berk Saner, Hüseyin K. Çakmak et al.

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.

Haozhen Cheng, Hüseyin K. Çakmak, Veit Hagenmeyer

Accurate building energy models are crucial for analyzing sector-coupled energy systems, where buildings interact with electrified heating, energy storage, and advanced control across various scenarios. High-fidelity (HiFi) white-box models that resolve hydronic distribution and emitter dynamics can capture short-term transients, yet their numerical stiffness and computational burden limit long-term simulations and large-scale scenario exploration. Conversely, reduced-order low-fidelity (LoFi) representations enable rapid annual assessments but may fail to capture the hydronic- and control-induced dynamics that govern transient and peak behavior. This paper proposes a dual-fidelity, multi-zone white-box building modeling framework, which is called JanusBM, built on a novel topology-driven modeling tool RoomFlex6D, coupling a HiFi hydronic model and a LoFi ideal-load surrogate that removes explicit hydronic states in Modelica. To ensure applicability and physical consistency across time scales, we introduce a two-stage hybrid validation and calibration pipeline that uses complementary data: the IEA EBC Annex 60 benchmark for energy-scale validation and time-series measurements from real-world experimental buildings for hydronic dynamics-scale calibration. Results show that the generated LoFi models achieve a high degree of consistency with Annex 60 benchmark on the energy scale, and the proposed calibration workflow robustly improves loop-level return water temperature transients and zone-level temperature dynamics. Moreover, the LoFi model achieves orders-of-magnitude faster simulations suited to annual energy analyses, whereas the HiFi model becomes necessary when the required heat differs from the actual delivered heat due to distribution and control limitations, especially in transient and peak-oriented assessments.