Tobias Gebhard

M.Sc. Tobias Gebhard

Residential Power Demand Modeling

S3|10 310
Landgraf-Georg-Str. 4
64283 Darmstadt

Research Interest

  • Modeling of Residential Energy Demand
  • Modeling of Consumer Dependencies/Synchronization
  • Probabilistic Modeling
  • Data Analytics
  • Digital Twins

 

The modeling of energy demand is important for predictions and ensuring power grid stability. The residential sector exhibits much uncertainty created by human behavior and increasing electricity demand, e.g. due to more and more heat pumps (HPs) and electric vehicles (EVs). In demand modeling, consumers are often considered independent from each other because peaks at different times mostly balance out. However, the synchronized change of power demands can quickly pose a problem to power grids (also known as the "TV pick-up" effect). This could potentially lead to blackouts, as distribution grids are not designed for every household to receive maximal power simultaneously. Therefore, a better understanding of consumer dependencies and the early detection of such anomalies is necessary.

These ideas are investigated in an interdisciplinary context together with the research center emergenCITY, funded by the LOEWE initiative (Hesse, Germany) and the DLR, Institute for the Protection of Terrestrial Infrastructures. Based on the digital city of the future, we tackle the questions on how to maintain its functionalities even in extreme situations, crises and disasters and to increase the reliability and resilience of critical infrastructures. We build demonstrators that simulate a city (for example Darmstadt) as a socio-technical system, considering the interdependencies of energy, communication and water infrastructure.

 

 

Open theses

Supervisor: Tobias Gebhard
Earliest start: Immediately
Type: Master Thesis


With the rapid electrification of mobility and heating, as well as the rise of solar PV and flexible demand, distribution grids face unprecedented challenges. The increasing energy demand often requires expansion of infrastructure, which is costly. Existing design practices are based on simple, outdated rules, often leading to over-dimensioning of capacities because reliability margins cannot be quantified sufficiently. First, they usually treat power demand as a fixed maximum value, ignoring the inherently probabilistic nature of electric loads. Second, the type of consumers, their individual consumption patterns (e.g. daily/seasonal), and load correlations with other consumers are neglected, but can have a significant impact on the maximum load. For a successful and cost-efficient energy transition, new data-driven approaches for grid planning and optimization are needed.
This thesis aims to change the way, low-voltage (LV) grids are designed by developing a data-driven, probabilistic, correlation-aware methodology. An optimization problem for capacity design, topological transformer placement, and switch placement/configuration is defined and analyzed. The approach is based on multivariate statistical modeling (e.g. normal distribution). To test and evaluate the method, a data analysis of electricity usage patterns from heterogeneous consumers (e.g. residential, commercial, retail, etc) is carried out.
  • Literature review of current practices and methods for LV grid planning
  • Research for electricity demand datasets of small public/commercial buildings (e.g. shops, retail, hotel, bakery etc)
  • Analyze consumer demand correlations of power demand time series data
  • Develop a probabilistic methodology for capacity design, optimal transformer placement, and/or consumer partition by considering the correlations
  • Implement and test the algorithm and evaluate the performance by comparing it with traditional, deterministic approaches

Requirements:
  • Interest in optimization and statistical modeling
  • Basic experience with programming and data analysis (e.g. Python)
  • Attendance in lecture “Data-driven Modeling / datengetriebene Modellierung (Machine Learning)” helpful
  • Attendance in lecture “Energy Management & Optimization” helpful
  • If the thesis is done as B.Sc, very good grades and self-organized acquisition of the prerequisites are expected

Short Bio

  • Since 2021: PhD Student at EINS and researcher at DLR
  • 2018 - 2021: M.Sc. Mechatronik at TU Darmstadt
  • 2014 - 2018: B.Sc. Mechatronik at TU Darmstadt
     

Publications

Explainable Anomaly Detection for Grid Monitoring using Probabilistic Load Forecasting

[Conference]
Tobias Gebhard, Andrea Tundis, Florian Steinke:
*accepted* Explainable Anomaly Detection for Grid Monitoring using Probabilistic Load Forecasting.
To appear in: IEEE SmartGridComm 2025, Toronto, Canada, 2025

Modeling and monitoring social media dynamics to predict electricity demand peaks

[Journal]
Isabella Nunes Grieser, Tobias Gebhard, Andrea Tundis, Jens Kersten, Tobias Elßner, Florian Steinke:
Modeling and monitoring social media dynamics to predict electricity demand peaks.
In: Elsevier Energy Reports 13 , P. 1548-1557, 2025

Automated Generation of Urban Medium-voltage Grids using OpenStreetMap Data

[Conference]
Tobias Gebhard, Andrea Tundis, Florian Steinke:
Automated Generation of Urban Medium-voltage Grids using OpenStreetMap Data.
In: IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), Dubrovnik, Croatia, 2024

Monitoring Electricity Demand Synchronization Using Copulas

[Conference]
Tobias Gebhard, Eva Brucherseifer, Florian Steinke:
Monitoring Electricity Demand Synchronization Using Copulas.
In: IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), Novi Sad, Serbia, 2022