Regional Energy Systems
Regional Energy Systems deals with the analysis and evaluation of transformation pathways for regional level energy systems. Considered system levels range from individual consumers (buildings, industrial sites) to neighborhoods and cities/counties. The analyses focus on the computer-aided simulation and optimization of the energy systems. The basis for this is bottom-up modeling of energy demand for the household, commercial, and industrial sectors, as well as the determination of regional and building-specific potentials for the use of renewable energies. Research focuses on analyses of the role of hydrogen in regional and energy self-sufficient supply systems as well as on the decarbonisation of industrial processes.
Research topics include energy and water demand modeling, regional energy system research and optimisation, and building energy demand modeling and optimisation.
Dr. Noah Pflugradt
Building 03.2 / Room 3010
The implementation of the energy transition is decentralized to a large extent, on site in the municipalities. This requires comprehensive regional energy system planning that takes into account the region-specific boundary conditions as well as the in part considerable potential for the expansion of renewable energies.
Through energy system planning oriented to regional requirements and boundary conditions, municipalities can not only mitigate the effects of climate change, but also secure regional added value and create sustainable jobs, demonstrate a clear economic perspective in Corona times and promote the region in the long term.
The Regional Energy Systems group has two main areas of focus: Bottom-Up Load Modeling and Regional Energy Systems Modeling. For this purpose, a number of different models are maintained, which are briefly presented here:
Load modeling attempts to answer the following questions:
- When is energy consumed and how much?
- For what is large amounts of energy consumed?
- How does energy consumption change when boundary conditions change?
- What does the current building stock look like?
- Where do how many people live?
Electricity Demand and Water Demand for Households: LoadProfileGenerator.de (LPG)
The LoadProfileGenerator.de performs a behavioral simulation of each person in a household. This simulation is parameterized with statistical input data and then simulates when people eat, work, sleep, go on vacation, and more. This approach has the great advantage that every single household in a region behaves realistically, the sum of activities over a region fits the available data, but at the same time no detailed data collection is necessary or privacy concerns to be taken care of. Not only domestic activities are included but transport routes are as well mapped. In this way, electromobility can also be mapped in detail and statements can be made about, for example, optimal numbers and locations for charging stations. (License: MIT)
Building Energy Consumption: TSIB
Using the Python package TSIB (Time Series Initialization for Buildings), a Germany-wide database of type buildings, occupancy data from the LoadProfileGenerator, weather profiles and other data, hourly accurate heat demand profiles are calculated for arbitrary residential buildings. By using a simplified thermal simulation of the building envelope, different years of construction as well as renovation stages of the building fabric can be represented. (License: MIT)
Energy Consumption for Small and Medium-Sized Enterprises: SMELPG
The SMELPG provides a generic model for load simulation of small and medium enterprises. It simulates the employees and customers of a company to determine times of device activation. Taking into account specific device characteristics, aggregated load curves are generated for arbitrary consumption types. External influences, such as outside temperature or solar radiation, can be included in the calculation. The focus is on the highest possible parameterizability for generating individually adapted load data without concrete load measurements. (License: Internal)
Parameterization: Neighborhood Generator
The Neighborhood Generator was developed at IEK-3 for the parameterization of neighborhoods, cities, and regions. It combines statistical data from census, microcensus, OpenStreetMap, cadastral data, and building typologies to form a consistent model of a region and extrapolates results into the future. The resulting building-level information on building fabric and population distribution can be used to parameterize both LoadProfileGenerator and TSIB. Through the interaction of these tools, the number of occupants, energy consumption, appliance equipment, electric vehicle distribution, etc. can be estimated for each building.
Regional Energy Systems
Models provide as outputs, among others:
- Where future energy generation, storage, and infrastructure units should be placed?
- Which technologies are optimal with the boundary conditions of the specific region?
- What installed capacity of energy generation units and storage is needed, what is the optimal renovation rate, how should the heating structure should be designed, etc.?
- How the sector coupling is designed and which regional Power-to-X potentials exist for a regional hydrogen economy?
- How plants can be operated optimally with a high temporal resolution?
The framework for modeling, optimisation, and analysis of energy systems FINE serves as the basis for energy system optimisation for the entirety of IEK-3. In addition, the Regional Energy Systems group is working on a number of further models for energy system modeling.
Renewable Potentials: GLAES-Regional
TREP (Tool for Regional Renewable Potentials), together with the open-source tool GLAES, developed at IEK-3, can be used to determine suitable areas for renewable facilities through GIS analysis. Here, TREP takes into account existing houses, settlements, roads, forests, nature reserves, and much more. Through area analysis with TREP, onshore wind and ground-mounted PV potentials can be determined with high regional resolution. Capacity and generation potentials can be determined by placing and simulating turbines on the determined areas and detailed rooftop PV potentials are additionally determined by evaluating 3D models. By considering existing plants when determining renewable potentials, a brownfield analysis is therefore made possible.
Regional Optimization: FINE.Regional
FINE.Regional enables the optimisation of regional energy systems at the municipality and county level. High-resolution renewable potentials (onshore wind, ground-mounted PV, and rooftop PV) are incorporated in the models. By considering existing plants on the generation side, a brownfield approach is followed. On the demand side, mobility, heat, hydrogen, and electricity requirements are taken into account. This allows local self-sufficiency potentials to be determined for different communities, cities, or counties. The analysis can determine the necessary renewable expansion for specific self-sufficiency efforts. In addition, regional power-to-X potentials may also be identified.
Building Energy System Optimisation: FINE.Building
A detailed linear optimization model, which has been created in the FINE framework, is used to analyze supply concepts on the building level. The consideration of a single building allows a higher level of detail compared to regional models of the energy system. For example, different temperature levels of the heat supply and regulatory aspects, such as a subsidy through the EEG, can be represented.
Building Energy System Simulation: House Infrastructure Simulator (HiSim)
HiSim is a simulation framework for building energy systems and conducting techno-economic analyses. For this purpose, the system's energy requirements can be mapped either via its own load profiles or calculated via an integrated residential building model. Technologies for sustainable energy supply and storage are modeled to cover the electricity and heat requirements. In addition, databases were created in order to be able to transfer specific market-available technologies into the energy system. HiSim was developed as part of the PIEG-Strom project funded by the German Federal Ministry for Economic Affairs and Energy. As part of this project, some models and databases were created in cooperation with Emden/Leer University of Applied Sciences.