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Input data management for advanced energy system models
Energy system models are useful tools to identify suitable energy transition pathways towards a sustainable energy future. They depend on input data, whose management becomes nontrivial when these models should be adaptable to systems in various contexts. A new municipal energy system model is being developed based on the RE3ASON model, which enables assessment of the local energy demand, renewable potential and existing infrastructure and determines an optimal energy transition pathway based on this information for German municipalities. The new model aims to support a wider range of technologies with increased technical detail and extend the model scope to Swiss municipalities. To ensure a coherent workflow for modelling with diverse data types from various sources, this development uses an open-source Python package, Spine Toolbox, for data management. In this context, the project focuses on integrated modularisation and automation of input data management workflow for energy system modelling.
Keywords: Data management, open-source tools, sustainable energy
The tasks of the project are:
• Design overarching structures for the input data management workflow and output database.
• Integrate individual input data components into cohesive workflows in a Spine Toolbox project.
• Implement data processing to streamline the preparation and integration of diverse data sources.
• Test and validate the workflow project using a toy model to ensure reproducibility and reliability.
• Document the workflow and provide recommendations for further improvements.
The student is expected to work closely with the new model development team. During the project, the
student will learn cutting-edge experiences and practices in open-source data management for advanced
energy system modelling from a passionate and welcoming open-source community.
The tasks of the project are: • Design overarching structures for the input data management workflow and output database. • Integrate individual input data components into cohesive workflows in a Spine Toolbox project. • Implement data processing to streamline the preparation and integration of diverse data sources. • Test and validate the workflow project using a toy model to ensure reproducibility and reliability. • Document the workflow and provide recommendations for further improvements. The student is expected to work closely with the new model development team. During the project, the student will learn cutting-edge experiences and practices in open-source data management for advanced energy system modelling from a passionate and welcoming open-source community.
The primary objective of this project is to develop integrated workflows to process different types of data inputs in a reproducible and modular fashion. The workflows consist of several components for data gathering, processing and storage, which will be implemented using the Spine Toolbox. The toolbox offers a variety of data management tools and allows for integrating customised scripts (in Python, Julia or GAMS)
for specific tasks. And the workflows are expected to generate a (group of) customised database(s) for the input of the new model. For example, input data on a municipal level is available for Switzerland for energy system relevant attributes such as installed generation capacities, renewable potentials, or the building stock, which in turn determines the energy demand. The automated workflows should allow one to select an arbitrary municipality and retrieve and process the necessary data to produce the resulting energy demand parameter for energy system model based on user specifications.
The primary objective of this project is to develop integrated workflows to process different types of data inputs in a reproducible and modular fashion. The workflows consist of several components for data gathering, processing and storage, which will be implemented using the Spine Toolbox. The toolbox offers a variety of data management tools and allows for integrating customised scripts (in Python, Julia or GAMS) for specific tasks. And the workflows are expected to generate a (group of) customised database(s) for the input of the new model. For example, input data on a municipal level is available for Switzerland for energy system relevant attributes such as installed generation capacities, renewable potentials, or the building stock, which in turn determines the energy demand. The automated workflows should allow one to select an arbitrary municipality and retrieve and process the necessary data to produce the resulting energy demand parameter for energy system model based on user specifications.
If you are interested in the project, please forward your CV and transcript by e-mail to Jiangyi Huang jiahuang@ethz.ch or Febin Kachirayil fkachirayil@ethz.ch.
This project could be carried out as a group project, together with the following projects: Advanced optimization model for municipal energy transitions in Switzerland and Data-driven decision support: Analyzing and visualizing energy model outputs
If you are interested in the project, please forward your CV and transcript by e-mail to Jiangyi Huang jiahuang@ethz.ch or Febin Kachirayil fkachirayil@ethz.ch. This project could be carried out as a group project, together with the following projects: Advanced optimization model for municipal energy transitions in Switzerland and Data-driven decision support: Analyzing and visualizing energy model outputs
Student project_input_workflow.pdf
