Browsing by Author "Leibfried, Thomas"

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Battery Energy Storage System Models for Microgrid Stability Analysis and Dynamic Simulation
(Institute of Electrical and Electronics Engineers (IEEE), 2017-08-14) Farrokhabadi, Mostafa; Konig, Sebastian; Canizares, Claudio A.; Bhattacharya, Kankar; Leibfried, Thomas
With the increasing importance of battery energy storage systems (BESS) in microgrids, accurate modeling plays a key role in understanding their behavior. This paper investigates and compares the performance of BESS models with different depths of detail. Specifically, several models are examined: an average model represented by voltage sources; an ideal dc source behind a voltage source converter; a back-to-back buck/boost and bidirectional three-phase converter, with all models sharing the same control system and parameters; and two additional proposed models where the switches are replaced by dependent sources to help analyze the differences observed in the performance of the models. All these models are developed in PSCAD and their performances are simulated and compared considering various issues such as voltage and frequency stability and total harmonic distortion in a benchmark test microgrid. It is shown through simulation results and eigenvalue studies that the proposed models can exhibit a different performance, especially when the system is heavily loaded, highlighting the need for more accurate modeling under certain microgrid conditions.
Comparison of machine learning and MPC methods for control of home battery storage systems in distribution grids
(Elsevier, 2025-08-02) Mueller, Felicitas; de Jongh, Steven; Cañizares, Claudio A.; Leibfried, Thomas; Bhattacharya, Kankar
Control methods for Home Energy Management Systems implemented with traditional optimization techniques and state-of-the-art Machine Learning methods are presented and compared in this paper in the context of their impact on and interactions with Active Distribution Networks. Thus, model-based methods based on Model Predictive Control algorithms with different prediction qualities are first described and compared against model-free methods based on imitation learning and reinforcement learning. A practical, state-of-the-art, heuristic, rule-based controller is used as the baseline. An in-depth comparison is performed using metrics consisting of objective function values, grid constraint violations, and computational time. The results of applying these Home Energy Management Systems to a realistic German low voltage benchmark grid with 13 connected households, each containing solar generation, a battery storage system, and electrical loads are discussed. It is demonstrated that model-based and model-free methods can achieve improvements over typical rule-based methods, with varying performance in terms of objective function values and grid constraint violations depending on the forecasts, at the cost of higher computational complexity. Furthermore, model-free methods are shown to have in general low computational burden at higher objective function values with more grid constraint violations, with imitation-learning-based techniques proving to be the best compromise for practical applications.
Data-Driven Topology and Parameter Identification in Distribution Systems With Limited Measurements
(Institute of Electrical and Electronics Engineers (IEEE), 2024-11-05) de Jongh, Steven; Mueller, Felicitas; Osterberg, Fabian; Cañizares, Claudio A.; Leibfried, Thomas; Bhattacharya, Kankar
This manuscript presents novel techniques for identifying the switch states, phase identification, and estimation of equipment parameters in multi-phase low voltage electrical grids, which is a major challenge in long-standing German low voltage grids that lack observability and are heavily impacted by modelling errors. The proposed methods are tailored for systems with a limited number of spatially distributed measuring devices, which measure voltage magnitudes at specific nodes and some line current magnitudes. The overall approach employs a problem decomposition strategy to divide the problem into smaller subproblems, which are addressed independently. The techniques for identifying switch states and system phases are based on heuristics and a binary optimization problem using correlation analysis of the measured time series. The estimation of equipment parameters is achieved through a data-driven regression approach and by an optimization problem, and the identification of cable types is solved using a Mixed-Integer Quadratic Programming solver. To validate the presented methods, a realistic grid is used and the presented techniques are evaluated for their resilience to data quality and time resolution, discussing the limitations of the proposed methods.
Distribution Grid State Estimation With Limited Actual and Pseudo Measurements
(Institute of Electrical and Electronics Engineers (IEEE), 2025-06-25) de Jongh, Steven; Mueller, Felicitas; Cañizares, Claudio A.; Leibfried, Thomas; Bhattacharya, Kankar
Methods for distribution system state estimation in Low Voltage (LV) distribution grids are discussed in this paper, for systems with a high penetration of Distributed Energy Resources (DERs) such as solar generators and heatpumps. The proposed methods are specifically designed for LV grids with sparse measurement availability, such as feeders with measurements only at the distribution transformer, as is typically the case in some European LV grids. For these cases, device locations, temporal data, and weather data are used in the proposed techniques to estimate variables at unmeasured grid nodes. The impact of smart meters is also investigated by simulating the impact of individual smart meter measurements on the estimation results. The proposed methods are based on time series disaggregation of transformer measurements, such as thermoelectrical demand, baseload, and solar generation, enabling improvements over existing Pseudo-Measurement (PM) generation techniques. Furthermore, the paper presents approaches for estimating voltages and currents in the feeder using both actual and PMs, based on classical estimation methods and interval estimation techniques for unmeasured variables. The results for an realistic German LV grid show that the proposed disaggregation step allows to significantly improve the results of the state estimation results over state-of-the-art methods.
Energy Storage in Microgrids: Compensating for Generation and Demand Fluctuations While Providing Ancillary Services
(Institute of Electrical and Electronics Engineers (IEEE), 2017-08-16) Farrokhabadi, Mostafa; Solanki, Bharatkumar V.; Canizares, Claudio A.; Bhattacharya, Kankar; Koenig, Sebastian; Sauter, Patrick S.; Leibfried, Thomas; Hohmann, Soren
Driven by global environmental emission issues, energy access in remote communities, and tighter requirements for system resilience and reliability, electricity production is shifting from a centralized paradigm to a decentralized one. In this context, renewable energy sources (RESs) have proliferated over the past decade, exhibiting a steadily increasing trend. Thus, today, a large number of wind turbines and photovoltaic (PV) panels are connected to medium- (1-69 kV) and low-voltage (=1 kV) grids, with traditional integrated bulk power systems becoming decentralized in the presence of active distribution networks, where the flow of power is bidirectional between generators and "prosumers." In particular, with decreasing RES s costs, these technologies are becoming attractive solutions to bring energy to remote communities and/or replace expensive fossil-fuel-based generators. However, RES s such as wind and solar are intermittent sources of energy, difficult to predict, and prone to large output fluctuations-therefore, significantly affecting system voltage and frequency.
Energy Storage in Microgrids: Compensating for Generation and Demand Fluctuations While Providing Ancillary Services
(Institute of Electrical and Electronics Engineers (IEEE), 2020-11-05) Farrokhabadi, Mostafa; Solanki, Bharatkumar V.; Canizares, Claudio A.; Bhattacharya, Kankar; Koenig, Sebastian; Sauter, Patrick S.; Leibfried, Thomas; Hohmann, Soren
Driven by global environmental emission issues, energy access in remote communities, and tighter requirements for system resilience and reliability, electricity production is shifting from a centralized paradigm to a decentralized one. In this context, renewable energy sources (RESs) have proliferated over the past decade, exhibiting a steadily increasing trend. Thus, today, a large number of wind turbines and photovoltaic (PV) panels are connected to medium- (1-69 kV) and low-voltage (=1 kV) grids, with traditional integrated bulk power systems becoming decentralized in the presence of active distribution networks, where the flow of power is bidirectional between generators and "prosumers." In particular, with decreasing RES s costs, these technologies are becoming attractive solutions to bring energy to remote communities and/or replace expensive fossil-fuel-based generators. However, RES s such as wind and solar are intermittent sources of energy, difficult to predict, and prone to large output fluctuations-therefore, significantly affecting system voltage and frequency.
Modeling and optimal operation of sustainable thermoelectric microgrids with phase-change material thermal system
(Elsevier, 2025-08-05) Verdugo, Pablo; Cañizares, Claudio; Pirnia, Mehrdad; Leibfried, Thomas
This paper proposes an Energy Management System for a thermoelectric microgrid that incorporates the modeling of a unique Phase-Change Material-based thermal system, capable of operating in both active and passive modes to minimize operating costs while guaranteeing thermal comfort, while properly considering the microgrid’s thermal power requirements and indoor temperature control. The proposed model also includes a detailed thermal representation of buildings to consider relevant thermal sources and room heat exchange, as well as heat pumps, water tanks for thermal storage, and battery degradation. A Model Predictive Control approach is used to address uncertainties in demand and environmental conditions. The proposed Energy Management System model is applied to the Energy Smart Home Lab microgrid located at the Karlsruhe Institute of Technology, in Germany, taking into account the specific characteristics of the microgrid’s components, expected energy consumption, and indoor temperature control requirements. Simulation results demonstrate the feasible application of the developed Energy Management System for the optimal operation of the actual microgrid considered, illustrating the thermoelectric microgrid’s power balance and temperature fluctuations of the associated components, with particular emphasis on the operation of the Phase-Change Material system, to showcase its active and passive thermal contribution under extreme weather conditions.