Open energy system models | EPFL Graph Search

Open energy system models are energy system models that are open source. However, some of them may use third party proprietary software as part of their workflows to input, process, or output data. Preferably, these models use open data, which facilitates open science. Energy system models are used to explore future energy systems and are often applied to questions involving energy and climate policy. The models themselves vary widely in terms of their type, design, programming, application, scope, level of detail, sophistication, and shortcomings. For many models, some form of mathematical optimization is used to inform the solution process. Energy regulators and system operators in Europe and North America began adopting open energy system models for planning purposes in the early2020s. Open models and open data are increasingly being used by government agencies to guide the develop of netzero public policy as well (with examples indicated throughout this article). Companies and engineering consultancies are likewise adopting open models for analysis (again see below). Energy modeling and Open-source software development The open energy modeling projects listed here fall exclusively within the bottom-up paradigm, in which a model is a relatively literal representation of the underlying system. Several drivers favor the development of open models and open data. There is an increasing interest in making public policy energy models more transparent to improve their acceptance by policymakers and the public. There is also a desire to leverage the benefits that open data and open software development can bring, including reduced duplication of effort, better sharing of ideas and information, improved quality, and wider engagement and adoption. Model development is therefore usually a team effort and constituted as either an academic project, a commercial venture, or a genuinely inclusive community initiative. This article does not cover projects which simply make their source code or spreadsheets available for public download, but which omit a recognized free and open-source software license.

Reducing Greenhouse Gas emissions is not the only solution

Justine Brun

With the increasing strength and frequency of climate change events, the urgency to mitigate climate change impact is ever so important. Canada reported his Nationally Determined Contribution (NDC) and submitted its ambition to reduce his Greenhouse Gas (GHG) emissions by 40-45% by 2030 compared to 2005 and reach net-zero emissions by 2050. Interest for energy system modelling has been increasing for planning future energy systems, as a result of growing concern for sustainable development and transition towards renewable energies. Nevertheless, planning a decarbonization of energy system can potentially lead to environmental burden shifting, and can increase impacts on other environmental impacts such as ecosystem quality or human health. Thus, this project aims towards integrating Life Cycle Analysis (LCA) and more specifically Life Cycle Impact Assessment (LCIA) within Energy Systems Modelling, in order to minimize not only climate change impacts, but human health and ecosystem quality. In addition, a dual spatial resolution was integrated in the model in order to increase data precision and computational efficiency. The results show that a low-carbon energy system, based on the optimization of GHG emissions allows to greatly reduce all the environmental impacts under analysis, compared to an all economic- based energy system, but a multi-objective optimization allows to simultaneously reduce impacts on ecosystem quality and human health, while reducing GHG emissions and keeping good economical and technical performances. The results also show that it is theoretically possible for Canada to keep on track with their emission reduction ambition by 2030, by deploying more renewable energies. This project does not allow assessing Canada’s potential to reach net-zero emissions, as environmental impacts from carbon capture technologies are still to be characterized and integrated in the model.

2022

Energy modeling of the Solar Decathlon pavilion: development and fine-tuning of solar passive strategies

Mélanie Isabelle Gaillet-Tournier, Mathieu Desmarescaux

The objective of the project is to evaluate the possible improvements of the design of the Solar Decathlon pavilion. In particular, this project aims at determining the effectiveness of passive systems (e.g. insulation, thermal mass, shading devices) in providing visual and thermal comfort.

2017

Overview over the design (and planning) of Urban energy systems

Davide Migliorisi

This report presents some approaches to modelize urban energy systems. More precisely, as a first step, a clear definition of what is an urban energy system model is exposed before listing the main tech- niques of modelization. With the goal to run a case study, two of these techniques are developed, the technology design and the system design. The latter is characterized by the use of optimization tools, which are briefly explained. Softwares like OSMOSE are run thanks to optimization methods, that is why the decomposition of an opti- mization sequence and then the software itself are presented. Finally, the theoretical part of this report is closed by the introduction of a central notion when modelizing and optimizing urban energy systems, the pinch analysis. The second part of the present report illustrates the use of the tools and concepts seen by then through a case study. Playing with two conflictive objectives, OSMOSE is run to find the best trade off for the example of the Meyrin Site, exploited by the CERN.

2013