scholarly journals A data processing approach with built-in spatial resolution reduction methods to construct energy system models

2021 ◽  
Vol 1 ◽  
pp. 36
Author(s):  
Christian Etienne Fleischer
Keyword(s):  
Data Processing ◽  
Open Source ◽  
Spatial Resolution ◽  
Input Data ◽  
Energy System ◽  
System Modelling ◽  
System Models ◽  
Processing Approach ◽  
Open Source Data ◽  
Reduction Methods

Introduction: Data processing is a crucial step in energy system modelling which prepares input data from various sources into a format needed to formulate a model. Multiple open-source web-hosted databases offer pre-processed input data within the European context. However, the number of documented open-source data processing workflows that allow for the construction of energy system models with specified spatial resolution reduction methods is still limited. Methods: This paper presents a novel data processing approach to construct sector-coupled energy system models for European countries while maximising the use of existing web-hosted pre-processed data. Three power and heat optimisation models of Germany were constructed using different spatial resolution reduction methods. Results: Significant variation in generation, transmission and storage capacity of electricity were observed between the optimisation results of the energy system models. The results of the model that used administrative state boundaries to define regions were found to be sensitive to the omission of solar rooftop photovoltaic availability. Conclusions: This paper uses the proposed data processing approach to demonstrate the importance of spatial context when building and analysing power and heat optimisation models.

scholarly journals The Potential Of Simulating Energy Systems: The Multi Energy Systems Simulator Model

2021 ◽  
Author(s):  
Luigi Bottecchia ◽  
Pietro Lubello ◽  
Pietro Zambelli ◽  
Carlo Carcasci ◽  
Lukas Kranzl
Keyword(s):  
Open Source ◽  
Spatial Scales ◽  
Optimal Solution ◽  
Energy Systems ◽  
Energy Transition ◽  
Simulation Models ◽  
Energy System ◽  
System Modelling ◽  
Energy System Model ◽  
Urban Level

Energy system modelling is an essential practice to assist a set of heterogeneous stakeholders in the process of defining an effective and efficient energy transition. From the analysis of a set of open source energy system models, it has emerged that most models employ an approach directed at finding the optimal solution for a given set of constraints. On the contrary, a simulation model is a representation of a system that is used to reproduce and understand its behaviour under given conditions, without seeking an optimal solution. Given the lack of simulation models that are also fully open source, in this paper a new open source energy system model is presented. The developed tool, called Multi Energy Systems Simulator (MESS), is a modular, multi-node model that allows to investigate non optimal solutions by simulating the energy system. The model has been built having in mind urban level analyses. However, each node can represent larger regions allowing wider spatial scales to be be represented as well. MESS is capable of performing analysis on systems composed by multiple energy carriers (e.g. electricity, heat, fuels). In this work, the tool’s features will be presented by a comparison between MESS itself and an optimization model, in order to analyze and highlight the differences between the two approaches, the potentialities of a simulation tool and possible areas for further development.

scholarly journals The Potential of Simulating Energy Systems: The Multi Energy Systems Simulator Model

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5724
Author(s):  
Luigi Bottecchia ◽  
Pietro Lubello ◽  
Pietro Zambelli ◽  
Carlo Carcasci ◽  
Lukas Kranzl
Keyword(s):  
Open Source ◽  
Spatial Scales ◽  
Optimal Solution ◽  
Energy Systems ◽  
Energy Transition ◽  
Energy System ◽  
System Modelling ◽  
Energy System Model ◽  
Urban Energy ◽  
Realistic Description

Energy system modelling is an essential practice to assist a set of heterogeneous stakeholders in the process of defining an effective and efficient energy transition. From the analysis of a set of open-source energy system models, it emerged that most models employ an approach directed at finding the optimal solution for a given set of constraints. On the contrary, a simulation model is a representation of a system used to reproduce and understand its behaviour under given conditions without seeking an optimal solution. In this paper, a new open-source energy system model is presented. Multi Energy Systems Simulator (MESS) is a modular, multi-energy carrier, multi-node model that allows the investigation of non optimal solutions by simulating an energy system. The model was built for urban level analyses. However, each node can represent larger regions allowing wider spatial scales to be represented as well. In this work, the tool’s features are presented through a comparison between MESS and Calliope, a state of the art optimization model, to analyse and highlight the differences between the two approaches, the potentialities of a simulation tool and possible areas for further development. The two models produced coherent results, showing differences that were tracked down to the different approaches. Based on the comparison conducted, general conclusions were drawn on the potential of simulating energy systems in terms of a more realistic description of smaller energy systems, lower computational times and increased opportunity for participatory processes in planning urban energy systems.

scholarly journals The Open Energy Modelling Framework (oemof) - A novel approach in energy system modelling

2017 ◽  
Author(s):  
Simon Hilpert ◽  
Cord Kaldemeyer ◽  
Uwe Krien ◽  
Stephan Günther ◽  
Clemens Wingenbach ◽  
...  
Keyword(s):  
Energy Systems ◽  
Maximum Level ◽  
Energy System ◽  
System Modelling ◽  
System Models ◽  
Modelling Framework ◽  
Novel Approach ◽  
Model Complex ◽  
Energy Modelling ◽  
Development Approach

Energy system models have become indispensable to shape future energy systems by providing insights into different trajectories. However, sustainable systems with high shares of renewable energy are characterised by growing crosssectoral interdependencies and decentralised structures. To capture important properties of increasingly complex energy systems, sophisticated and flexible modelling environments are needed. This paper presents the Open Energy Modelling Framework (oemof) as a novel approach in energy system modelling, representation and analysis. The framework forms a structured set of tools and sub-frameworks to construct comprehensive energy system models and has been published open source under a free licence. Using a collaborative development approach and extensive documentation on different levels, the framework seeks for a maximum level of transparency. Based on a generic graph based description of energy systems it is well suited to flexibly model complex crosssectoral systems ranging from a distributed or urban to a transnational scale. This makes the framework a multi-purpose modelling environment for strategic planning of future energy systems.

pyam - an open-source Python package for energy system scenario analysis & visualization

2021 ◽  
Author(s):  
Daniel Huppmann ◽  
Matthew Gidden
Keyword(s):  
Open Source ◽  
Scenario Analysis ◽  
Input Data ◽  
Best Practice ◽  
Ad Hoc ◽  
Source Code ◽  
Energy System ◽  
Quality Of Data ◽  
Scientific Software ◽  
Python Package

<p><strong>Background: Transparency, reproducibility and reusability of scientific analysis</strong></p><p>Researchers are now widely expected to share the data and source code of their work to foster transparency, reproducibility and reusability.<br>Alas, the quality of data documentation and scientific software scripts can vary substantially. In many instances, metadata and information on the provenance of data are missing or incomplete, and source code often does not include a clear list of dependencies (including version information) or systems requirements. Finally, the source code does not include sufficient inline documentation to be easily understood. As a consequence, even though the data and related scripts may be released under an open-source license, analysis too often cannot be reproduced or adapted with reasonable effort by other researchers.</p><p><strong>The pyam package</strong></p><p>This talk presents the open-source Python package <strong>pyam</strong> for energy system scenario analysis and visualization. The aim of pyam is not to provide any ground-breaking new methods or analysis routines. Instead, it provides a reliable, well-tested interface<br>similar in feel & style to the widely used <strong>pandas</strong> package, but geared for data formats and applications often used in energy systems analysis<br>and integrated assessment modelling.</p><p>By using pyam for their scenario input data processing and analysis workflows, researchers can reduce standard tasks like unit conversion and data validation from a 5-minute effort to 30 seconds - and have the knowledge that their scripts won't break if pandas or another dependency change their APIs, because the pyam community will work to ensure forward-compatibility and continuity. As another side benefit, the pyam package will raise meaningful errors when input data doesn't make sense, whereas own ad-hoc scripts may fail silently or - even worse - return non-sensical values.</p><p><strong>Spatial, temporal and sectoral aggregation & downscaling features</strong></p><p>To highlight the applicability of the pyam package for the EGU community and energy & climate modellers at large, this talk will focus on the features for spatial, temporal and sectoral aggregation and downscaling. The package include several often-used methods like weighting by proxy variables or deriving indicators based on minimum or maximum values of timeseries data.</p><p><strong>Building a community</strong></p><p>The pyam package follows best-practice of version control, continuous-integration and scientific-software documentation. This facilitates building on the package by other researchers. The community uses several tools for communication and discussion, including a Slack channel, an email list and a Github repository for issues & pull requests. And of course, we appreciate contributions by colleagues to extend the scope of features and methods based on their own use cases and requirements!</p><p><strong>More information</strong></p><p>ReadTheDocs: https://pyam-iamc.readthedocs.io/<br>GitHub repo: https://github.com/iamconsortium/pyam</p>

scholarly journals A Review on Time Series Aggregation Methods for Energy System Models

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 641 ◽  
Author(s):  
Maximilian Hoffmann ◽  
Leander Kotzur ◽  
Detlef Stolten ◽  
Martin Robinius
Keyword(s):  
Time Series ◽  
Input Data ◽  
Renewable Energy Sources ◽  
Energy Systems ◽  
Energy System ◽  
Machine Learning Techniques ◽  
Future Research ◽  
System Models ◽  
Number Of Publications ◽  
The Impact

Due to the high degree of intermittency of renewable energy sources (RES) and the growing interdependences amongst formerly separated energy pathways, the modeling of adequate energy systems is crucial to evaluate existing energy systems and to forecast viable future ones. However, this corresponds to the rising complexity of energy system models (ESMs) and often results in computationally intractable programs. To overcome this problem, time series aggregation (TSA) is frequently used to reduce ESM complexity. As these methods aim at the reduction of input data and preserving the main information about the time series, but are not based on mathematically equivalent transformations, the performance of each method depends on the justifiability of its assumptions. This review systematically categorizes the TSA methods applied in 130 different publications to highlight the underlying assumptions and to evaluate the impact of these on the respective case studies. Moreover, the review analyzes current trends in TSA and formulates subjects for future research. This analysis reveals that the future of TSA is clearly feature-based including clustering and other machine learning techniques which are capable of dealing with the growing amount of input data for ESMs. Further, a growing number of publications focus on bounding the TSA induced error of the ESM optimization result. Thus, this study can be used as both an introduction to the topic and for revealing remaining research gaps.

scholarly journals Integrating Behavioural Aspects in Energy System Modelling—A Review

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4579
Author(s):  
David Huckebrink ◽  
Valentin Bertsch
Keyword(s):  
Technology Acceptance ◽  
Renewable Energy Sources ◽  
Energy Systems ◽  
Energy System ◽  
System Modelling ◽  
Economic Dimension ◽  
System Models ◽  
The Many ◽  
Sustainable Energy System ◽  
Behavioural Aspects

Many countries worldwide have adopted policies to support the expansion of renewable energy sources aimed at reducing greenhouse gas emissions, combating climate change, and, more generally, establishing a globally sustainable energy system. As a result, energy systems around the world are undergoing a process of fundamental change and transformation that goes far beyond the technological dimension. While energy system models have been developed and used for several decades to support decision makers in governments and companies, these models usually focus on the techno-economic dimension, whereas they fall short in addressing and considering behavioural and societal aspects of decisions related to technology acceptance, adoption, and use. In fact, it is often the societal dimension that comes with the greatest challenges and barriers when it comes to making such a socio-technical transformation happen in reality. This paper therefore provides an overview of state-of-the-art energy system models on the one hand and research studying behavioural aspects in the energy sector on the other hand. We find that these are two well-developed fields of research but that they have not yet been integrated sufficiently well to provide answers to the many questions arising in the context of complex socio-technical transformation processes of energy systems. While some promising approaches integrating these two fields can be identified, the total number is very limited. Based on our findings, research gaps and potentials for improvement of both energy system models and behavioural studies are derived. We conclude that a stronger collaboration across disciplines is required.

Open Source Software (OSS) for Big Data

2021 ◽  
pp. 858-875
Author(s):  
Richard S. Segall
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Open Source ◽  
Open Source Software ◽  
Statistical Software ◽  
Big Data Processing ◽  
Open Source Data ◽  
Source Data ◽  
And Storage ◽  
Aggregation Analysis

This chapter discusses Open Source Software and associated technologies for the processing of Big Data. This includes discussions of Hadoop-related projects, the current top open source data tools and frameworks such as SMACK that is acronym for open source technologies Spark, Mesos, Akka, Cassandra, and Kafka that together compose the ingestion, aggregation, analysis, and storage layers for Big Data processing. Tabular summaries and categories for 38 Open Source Statistical Software (OSSS) are provided that include for each listing of features and URLs for free downloads. The current challenges of Big Data and Open Source Software are also discussed.

Open Source Software (OSS) for Big Data

2020 ◽  
pp. 50-72
Author(s):  
Richard S. Segall
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Open Source ◽  
Open Source Software ◽  
Statistical Software ◽  
Big Data Processing ◽  
Open Source Data ◽  
Source Data ◽  
And Storage ◽  
Aggregation Analysis

This chapter discusses Open Source Software and associated technologies for the processing of Big Data. This includes discussions of Hadoop-related projects, the current top open source data tools and frameworks such as SMACK that is acronym for open source technologies Spark, Mesos, Akka, Cassandra, and Kafka that together compose the ingestion, aggregation, analysis, and storage layers for Big Data processing. Tabular summaries and categories for 38 Open Source Statistical Software (OSSS) are provided that include for each listing of features and URLs for free downloads. The current challenges of Big Data and Open Source Software are also discussed.

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