open_access
Refine
Year
- 2023 (15) (remove)
Publication Type
- Article (7)
- Report (5)
- Conference Proceeding (2)
- Part of Periodical (1)
Keywords
- renewable energy (3)
- PSH (1)
- PSHP (1)
- UPSH (1)
- UPSHP (1)
- decentralized storm water retention system (1)
- energy analysis (1)
- energy system model (1)
- energy system modelling (1)
- grid modelling (1)
Faculty
- Energie · Gebäude · Umwelt (EGU) (15) (remove)
The Spreadsheet Energy System Model Generator (SESMG) is a tool for modeling and optimizing energy systems with a focus on urban systems. The SESMG is easily accessible as it comes with a browser-based graphical user interface, spreadsheets to provide data entry, and detailed documentation on how to use it. Programming skills are not required for the installation or application of the tool. The SESMG includes advanced modeling features such as the application of the multi-energy system (MES) approach, multi-objective optimization, model-based methods for reducing computational requirements, and automated conceptualization and result processing of urban energy systems with high spatial resolution. Due to its accessibility and the applied modeling methods, urban energy systems can be modeled and optimized with comparatively low effort.
Stormwater tree pits with storage elements enable the irrigation of urban trees and can potentially act as decentralized rainwater retention basins. This paper mainly focuses on analyzing this potential. Field tests were conducted to investigate the irrigation behavior and the storage effect of a storm water tree pit system using Perl hoses as irrigation elements over a period of two years.
The rainfall, storage volumes, and soil moisture within the employed planting pit were measured.
With the help of system modeling, the retention ability of the storm water tree pit system was analyzed. The available storage volume was sufficient to irrigate trees for several days. During the measurement period, about 15% of the inflowing rainwater was fed to the root zone of the tree. With practical storage volumes of 200 to 300 m3/ha, a remarkable amount of water from heavy rainfall could be completely stored, thus significantly reducing the risk of flooding. The retention effect and irrigation behavior largely depend on the soil conditions and the technical possibilities of the equipment supplying the root area (in this case, Perl hoses). Further investigations are required to determine the influence on the growth conditions of trees and optimize of the system for discharge into the root zone.
This review paper presents a short overview of current power system modelling tools especially used for analysing energy and electricity systems for the supply and demand sector. The main focus of this review lies on open source tools and models which are written and used in the programming language “Python”. The modelling tools are represented in a comprehensive table with key information. Five modelling tools with an open source license can be filtered out. The modelling tool PyPSA can be considered as a high performing tool especially as the gap between power system analysis tool (PSAT) and energy system modelling tool.
Local and regional energy systems are becoming increasingly entangled. Therefore, models for optimizing these energy systems are becoming more and more complex and the required computing resources (run-time and random access memory usage) are increasing rapidly. The computational requirements can basically be reduced solver-based (mathematical optimization of the solving process) or model-based (simplification of the real-world problem in the model). This paper deals with identifying how the required computational requirements for solving optimization models of multi-energy systems with high spatial resolution change with increasing model complexity and which model-based approaches enable to reduce the requirements with the lowest possible model deviations. A total of 12 temporal model reductions (reduction of the number of modeled time steps), nine techno-spatial model reductions (reduction of possible solutions), and five combined reduction schemes were theoretically analyzed and practically applied to a test case. The improvement in reducing the usage of computational resources and the impact on the quality of the results were quantified by comparing the results with a non-simplified reference case. The results show, that the run-time to solve a model increases quadratically and memory usage increases linearly with increasing model complexity. The application of various model adaption methods have enabled a reduction of the run-time by over 99% and the memory usage by up to 88%. At the same time, however, some of the methods led to significant deviations of the model results. Other methods require a profound prior knowledge and understanding of the investigated energy systems to be applied. In order to reduce the run-time and memory requirements for investment optimization, while maintaining good quality results, we recommend the application of (1) a pre-model that is used to (1a) perform technological pre-selection and (1b) define reasonable technological boundaries, (2) spatial sub-modeling along network nodes, and 3) temporal simplification by only modeling every nth day (temporal slicing), where at least 20% of the original time steps are modeled. Further simplifications such as spatial clustering or larger temporal simplification can further reduce the computational effort, but also result in significant model deviations.
The use of wind power is rapidly expanding worldwide. It is important to examine the impact of wind turbines on the environment to see if they provide a net benefit and to identify potential for improving. Therefore life cycle assessments (LCA) of different wind turbine types are compared in this short review. The results are then shown side by side in tables for comparison. Overall the LCAs show that wind turbines compensate the required energy and emitted pollutants after approx. 6-16 months. The energy payback period (EPP) for 2 MW onshore wind turbines remained roughly the same since 2009 with approximately 7 months. Onshore wind turbines have a higher impact due to emissions but a shorter EPP than offshore wind turbines. The estimated service life of 20 years should be maximized to ensure a high energy yield ratio. The biggest impact on the environment results from the processes to provide the building material e.g. steel and cement. That impact could be reduced by 20 % if recycled steel would be used. It is shown that wind power is one of the cleanest energy sources. But further investigations in material processing and recycling are important to improve the eco-balance of wind turbines.