2024-03-29T12:56:39Z
https://zenodo.org/oai2d
oai:zenodo.org:8276294
2023-08-23T14:26:52Z
user-openentrance
user-eu
Backe, Stian
Zwickl-Bernhard, Sebastian
Schwabeneder, Daniel
Auer, Hans
Korpås, Magnus
Tomasgard, Asgeir
2022-07-06
<p>This paper investigates how the European electricity and heating system is impacted when medium-scale energy communities (ECs) are developed widely across Europe. We study the response on the capacity expansion of the cross-border transmission and national generation and storage within the European electricity and heating system with and without ECs in selected European countries. The representation of ECs has a special focus on flexibility, and we analyze the difference between flexibility responses by ECs towards local versus global cost minimization. Results show that EC development decreases total electricity and heating system costs on the transition towards a decarbonized European system in line with the 1.5 ◦C target, and less generation and storage capacity expansion is needed on a national scale to achieve climate targets. We also identify a conflict of interest between optimizing EC flexibility towards local cost minimization versus European cost minimization.</p>
https://doi.org/10.5281/zenodo.8276294
oai:zenodo.org:8276294
eng
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.8276293
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Applied Energy, 323, 119470, (2022-07-06)
Multi-energy-system modeling
Capacity expansion
Stochastic programming
Energy flexibility
Sector coupling
Energy community
Local renewable energy generation
Decarbonization
Impact of energy communities on the European electricity and heating system de
info:eu-repo/semantics/article
oai:zenodo.org:8288993
2023-08-28T14:26:52Z
user-openentrance
user-eu
Charousset, Sandrine
O'Reilly, Ryan
Ramos, Andres
Olmos, Luis
Alvarez, Erik
Frischmuth, Felix
Schmidt, Sarah
Pinel, Dimitri
Schledorn, Amos
Perger, Theresia
Pisciella, Paolo
Holtz, Franziska
Huppmann, Daniel
Graabak, Ingeborg
2023-06-30
<p>Open ENTRANCE has developed an open-source modelling platform that:</p>
<ul>
<li>Allows carrying out scientific calculations and assessments for different future options of a low-carbon Europe.</li>
<li>Makes models relevant for analysing different aspects of the energy transition open available, e.g. energy system models, macro-economic models, etc.</li>
<li>Makes data to be used in energy transition analyses open available, like energy system data, human behavioural data (e.g. energy consumption habits), macro-economic data, etc.</li>
<li>Soft links models such that output from one model can be used as input to another model and in that way provid consistent analyses across models.</li>
<li>Supports stakeholders to determine consequences of the energy transition and identify the best ways to transition to a ‘low-carbon’ economy.</li>
<li>Is openly available to use by any interested users, targeting mainly researchers and modellers.</li>
</ul>
<p>9 case studies have been performed during the project, as a real-size proof of concept of the project, applied to the main topics of the energy transition. These case studies made extensive use of the Open ENTRANCE platform.</p>
<p>The objective of this report is mainly to help future users of the platform to make the best use of it, as well as to give them some advice coming from the experience of the case studies.</p>
<p>Conducting case studies via the Open ENTRANCE platform allows to:</p>
<ul>
<li>Elaborate deeper analysis of the Open ENTRANCE scenarios thanks to the possibility of running specialised models fed with the Open ENTRANCE scenario results, including power system models, local models….</li>
<li>Benchmark results of different models (eg. 2 different power system models, 2 different macro economic models, 2 different energy system models) and easily compare them. This also increases confidence in the results;</li>
<li>Increase confidence of all results thanks to the full transparency of data, assumptions, algorithms and results;</li>
<li>Showcase analyses by taking advantadge of the postoprocessing and visualisation functions available in the Open ENTRANCE platform;</li>
<li>Make all results easily re-useable.</li>
</ul>
<p>The first part of this report includes a tutorial designed to help users to conduct a case study within the Open ENTRANCE environment, in particular how to define a case study workflow, how to describe the data involved in this workflow using the Open ENTRANCE nomenclature, how to download data from the platform, how to create linkage scripts for soft-connection of models to the platform and how to create datasets and upload them to the platform. The second part is devoted to advice from experience within the 9 case studies.</p>
https://doi.org/10.5281/zenodo.8288993
oai:zenodo.org:8288993
eng
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.8288992
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Best practice for performing case studies for the European energy system in transition
info:eu-repo/semantics/report
oai:zenodo.org:8289168
2023-08-28T14:26:53Z
user-openentrance
user-eu
Graabak, Ingeborg
Belsnes, Michael
Crespo del Granado, Pedro
2023-08-28
<p>This report (Open ENTRANCE deliverable 2.5) describes the science-policy interface in the H2020 project Open ENTRANCE and gives the project’s recommendations to how future energy system projects in Europe may strengthen their interaction with policy and decision makers.</p>
<p>The science-policy interface has been defined as “social processes which encompass relations between scientists and other actors in the policy process, and which allow for exchanges, co-evolution, and joint construction of knowledge with the aim of enriching decision-making”.</p>
<p>Policy and decision makers should in this report be understood in a broader context as politicians at both EU, national and local level together with the policy forming and policy implementing entities but also decision makers related to the energy system like Transmission System Operators (TSOs), ENTSO-E, ETSOG, ACER and national regulators, energy producers, distribution system operators (DSOs) etc.</p>
<p>Open ENTRANCE’s interactions with policy- and decision makers have mainly taken place in six workshops in the beginning and in the final stages of the project work with themes covering:</p>
<ul>
<li>development of scenarios for decarbonisation of the energy system</li>
<li>macro-economic analyses of the energy transition</li>
<li>case studies of specific challenges of the energy transition.</li>
</ul>
<p>Furthermore, Open ENTRANCE was the main responsible project for the EMP-E 2020 conference, which was a meeting place for around 500 energy system modellers and policy and decision makers in Europe. Finally, the 10 newsletter and active dissemination via Twitter and LinkedIn have also been an important part of Open ENTRANCE’s policy science interface.</p>
<p>The main recommendations for the future are: i) a strategy plan for the science-policy interface should be developed early in the project ii) energy system modelling projects should work with policy relevant cases and questions iii) ECEMP should be kept and further developed as a meeting place for policy and decision makers and energy system modellers.</p>
https://doi.org/10.5281/zenodo.8289168
oai:zenodo.org:8289168
eng
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.8289167
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Guidelines for policy-science interface
info:eu-repo/semantics/report
oai:zenodo.org:8289136
2023-08-28T14:26:53Z
user-openentrance
user-eu
Graabak, Ingeborg
Charousset, Sandrine
Huppmann, Daniel
Belsnes, Michael
2023-04-30
<p>Open ENTRANCE was a 4-year H2020 project (2019 -2023). Open ENTRANCE developed, applied and disseminated an open, transparent and integrated modelling platform designed to assess low-carbon transition pathways in Europe. The platform is populated with a suite of open state-of-the-art models that process data derived from multiple dimensions of the energy transition. Data is also made open available via the platform according to the FAIR principles.</p>
<p>This report (Open ENTRANCE deliverable 8.2) describes the Exploitation and IPR (Intellectual Property Rights) management plan for Open ENTRANCE. There are different results from Open ENTRANCE: an open modelling platform, open models, linked models, a common format for linking of models, data, tools for processing of the data, results from analyses, reports, presentations, communications results and scientific papers. Most of the results are open and freely available to the public.</p>
<p>The management strategies for IPR focus on:</p>
<ul>
<li>Identifying project results</li>
<li>Identifying the property of the results</li>
<li>Making as much as possible of the project results free of use</li>
<li>Identifying any licensing, copyrights etc</li>
</ul>
<p>The exploitation strategies focus on:</p>
<ul>
<li>Identifying target groups for the results</li>
<li>Exploitation strategies per partner</li>
</ul>
<p>The Open ENTRANCE results are already used in at least 15 new research proposals and projects. In several of these activities some of the Open ENTRANCE partners collaborate. Examples are the HEU projects ECEMF (IIASA and Comillas), iDesignRES (NTNU, IIASA, TU Wien, TU Berlin, EDF and SINTEF), OpenMod4Africa (SINTEF, Comillas, TU Berlin, EDF and IIASA) and the Clean Energy Transition partnership proposal Man0EUvRE (SINTEF, TU Berlin/EUF, EDF, Khas, NTNU). So far, most of the projects or proposals use elements of The Open Platform like the scenarios, the nomenclature (ECEMF) or some of the models and the Scenario explorer (OpenMod4Africa and iDesignRES).</p>
https://doi.org/10.5281/zenodo.8289136
oai:zenodo.org:8289136
eng
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.8289135
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Exploitation and IPR management strategy – final version
info:eu-repo/semantics/report
oai:zenodo.org:5521179
2021-09-28T09:08:27Z
user-openentrance
user-eu
Crespo del Granado, Pedro
Olmos, Luis
Quispe, Erik Alvarez
Pisciella, Paolo
Cohen, Jed
Perger, Theresia
Schmidt, Sarah
Härtel, Philipp
Dominokovic, Dominik
Oudjane, Nadia
Frischmuth, Felix
Charousset-Brignol, Sandrine
Kirkil, Gokhan
Hainsch, Karlo
Graabak, Ingeborg
2020-07-05
<p>This report takes us a step closer to recognising our project’s ambition to develop, use and disseminate an open, transparent, and integrated modelling platform for assessing low-carbon transition pathways of the European energy system. Here, our team describes the interaction needed between models being used on the openENTRANCE project, to create an integrated modelling platform. These interactions, are achieved through formalised descriptions of openENTRANCE partners actions will perform with their modelling tools on the database to implement our case studies. </p>
<p>This is a very significant step in making our models and tools open and transparent. By enabling data sharing between models in a consistent and standardised manner, we will see mutual benefits to each research area.</p>
<p>Our project categorises the suite of energy modelling tools by which element of energy systems they are analysing and then links this suit of energy modelling tools together. </p>
<ol>
<li>Energy System Models </li>
<li>Electricity Sector </li>
<li>Pan-European and National Models vs Local Models </li>
<li>Macro-economic models. </li>
</ol>
<p>openENTRANCE is creating links between these suits of models, so that the modelling tools can exchange information among them. This report also sets out the analyses to take place within the Case Studies as well as information exchange between the models that have been used in these Case Studies.</p>
https://doi.org/10.5281/zenodo.5521179
oai:zenodo.org:5521179
eng
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.5521178
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Definition of the interface between the models in the suite and the Common Database
info:eu-repo/semantics/report
oai:zenodo.org:5521144
2021-09-28T09:10:55Z
user-openentrance
user-eu
Charousset-Brignol, Sandrine
Cohen, Jed
Auer, Hans
Lumbreras, Sara
Härtel, Philipp
Oudjane, Nadia
Schmidt, Sarah
Vardanyan, Yelena
Kirkil, Gokhan
Crespo del Granado, Pedro
Huppman, Daniel
Graabak, Ingeborg
2020-04-30
<p>Our Case Study definition and requirements are a key part of our projects ambition to build an open, transparent and integrated modelling platform for assessing low-carbon transition pathways for Europe.</p>
<p>The case studies will also serve to test and demonstrate the functioning of the modelling platform and the linkages between models. To ensure consistency, supplemental data which may be needed for specific elements may be added to the platform while performing the case studies. This means it will be possible to re-run the case studies or challenge them by using other models and/or data.</p>
<p>This report details the nine ‘Case Studies’, which cover the main topics of the of proposed case studies:</p>
<ul>
<li>Show the adequacy and relevance of the Open ENTRANCE platform.</li>
<li>Show the ability of the proposed approach to answer specific questions related to the evolution of the energy system.</li>
<li>Provide complementary inputs (data) to Work Package 3 “Scenario Building Exercises”.</li>
<li>Provide knowledge regarding barriers and determinants of investments within WP7 “Transition Pathways” (Note: The last case study belongs to WP7).</li>
</ul>
<p>To use the case studies, simulations will be run using the linked models, which have been developed in our Suite of Modelling Tools, and the data set developed in our scenario building exercises. To ensure consistency, supplemental data which may be needed for specific elements may be added to the platform while performing the case studies. This means it will be possible to re-run the case studies or challenge them by using other models and/or data.</p>
<p>The case studies in this report will also act as a real-life proof of the concept of the project. Through the case studies the model connections will be validated and the modelling assumptions and results will be compared.</p>
<p>Within the following nine chapters, each of the case studies is deeply described, including:</p>
<ul>
<li>Explanation of the overall objective of the case study: which questions does it aim to answer?</li>
<li>Baseline of the case study: main assumptions, perimeter of the study, target user</li>
<li>Challenge(s) in the case study (beyond-state-of-the-art)</li>
<li>Modelling approach</li>
<li>Expected results</li>
<li>Limitations of the study</li>
<li>Data requirements including data coming from WP3 and complemental data and data sources</li>
<li>Models requirements: models to be used, required linkages, needed model enhancements</li>
<li>Methodology of the case study (modus operandi).</li>
</ul>
https://doi.org/10.5281/zenodo.5521144
oai:zenodo.org:5521144
eng
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.5521143
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Definition of and requirements for case studies of the European energy transition
info:eu-repo/semantics/article
oai:zenodo.org:8074141
2023-06-25T14:27:08Z
openaire_data
user-iiasa-ece
user-openentrance
user-eu
charousset
2023-06-23
<p>This dataset contains scenarios results for Case Study 4 of the openENTRANCE project.</p>
<p>Visit the openENTRANCE Scenario Explorer at <a href="https://data.ene.iiasa.ac.at/openentrance">https://data.ece.iiasa.ac.at/openentrance</a> for more information.</p>
https://doi.org/10.5281/zenodo.8074141
oai:zenodo.org:8074141
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/iiasa-ece
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.8074140
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Results for plan4EU runs in Case STudy 4 of the Open ENTRANCE project
info:eu-repo/semantics/other
oai:zenodo.org:5521194
2021-09-28T09:07:39Z
user-openentrance
user-eu
Pisciella, Paolo
Olmos Camacho, Luis
Crespo del Granado, Pedro
Perger, Theresia
Quispe, Erik Francisco Álvarez
Sancho, Sara Lumbreras
Galán, Andrés Ramos
Zwickl-Bernhard, Sebastian
Hainsch, Karlo
Löffler, Konstantin
Boonman, Hettie J.
Charousset-Brignol, Sandrine
Ahang, Mohammadreza
Huppmann, Daniel
Graabak, Ingeborg
2021-02-26
<p>This report focuses on the concept of openness of the suite of models populating the openENTRANCE platform. In this respect, the document provides an explanation of the necessary steps needed to define an open-source model, followed by a report of the experiences of the openENTRANCE modelling teams in opening the models they have developed and are maintaining. The core of the report is represented by a set of tables, one for each modelling, team containing information about the steps taken to revise, restructure and simplify the source code of their models as well as providing them with a user guide and a suitable opensource license.</p>
<p>The deliverable can serve as a basis both for project stakeholders and for third parties, such as modelling teams interested in engaging in collaboration with the openENTRANCE platform also beyond the lifespan of the project, to understand the steps that have been taken to make the models suitable for a transparent interaction around a common platform. This will give researchers and modelers the possibility to use the models that have been included in the initial suite of tools operating around the openENTRANCE platform and facilitate future interactions between the created platform and third parties interested in exploiting its potentialities using their own models.</p>
https://doi.org/10.5281/zenodo.5521194
oai:zenodo.org:5521194
eng
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.5521193
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Definition and Implementation of Upgrades of openENTRANCE Models to Make Them Open-Source
info:eu-repo/semantics/report
oai:zenodo.org:8090925
2023-06-28T14:26:45Z
openaire_data
user-openentrance
user-eu
Schledorn, Amos
Charousset-Brignol, Sandrine
Dominković, Dominik Franjo
2023-06-28
<p>In case study 7 of the openENTRANCE project, Plan4EU, an electricity dispatch model for Europe, is soft-linked to the Flexibility Function, an indirect demand response model, via Frigg, a novel framework for integrating realistic demand response in energy system analysis. This modelling setup is applied to analyse the role of power-to-heat demand flexibility (end-consumer demand response and heat storage) in the Danish electricity system of 2050. This dataset contains results as presented in the related project report.</p>
https://doi.org/10.5281/zenodo.8090925
oai:zenodo.org:8090925
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.8090924
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Demand response
Power-to-heat
Plan4EU
Frigg
Flexibility Function
openENTRANCE
openENTRANCE - Case Study 7 - Power-to-Heat Demand Flexibility: Results
info:eu-repo/semantics/other
oai:zenodo.org:8278529
2023-08-25T14:26:59Z
user-openentrance
user-eu
boonman, Hettie
Pisciella, Paolo
Reynes, Frédéric
2023-07-14
<p>This paper compares the macroeconomic impacts of different decarbonization storylines until 2050 using two Computable General Equilibrium models. The modeling shocks are harmonized across models to simulate four decarbonization scenarios through three main drivers: technical development, societal attitude, and policies. The study explores the contribution of each of these drivers to the European decarbonization. The results show that the decarbonization scenarios have moderate effects on GDP; decarbonization scenarios rather result in sectoral shifts. The impact of temperature on labor productivity minimally alters expected growth levels, since larger differences in temperature between the scenarios are only expected to occur in 2100, not yet in 2050. Electricity demand is increasing in all scenarios, particularly with stronger political guidance leading to additional tax and subsidy measures to encourage the use of electricity and discourage fossil-based fuels. When society is a driving factor, it is found that circular business models result in an increase in the service industry but might have a slightly negative impact on overall growth due to stronger spillover effects linked to the manufacturing industry. Technology appears to be the only driver to facilitate decarbonization while maintaining steady economic growth.</p>
https://doi.org/10.2139/ssrn.4509806
oai:zenodo.org:8278529
eng
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
SSRN, (2023-07-14)
ECEMP 2023, European Climate and Energy Modelling Platform 2023, online, 5-6 October 2023
Computable General Equilibrium Models
Energy Transition
Scenario analysis
The Macroeconomic Impact of Policy Measures, Technological Progress and Societal Attitude in Energy Transition Scenarios
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:8289178
2023-08-28T14:26:53Z
user-openentrance
user-eu
Pisciella, Paolo
Crespo del Granado, Pedro
Backe, Stian
Barani, Mostafa
Morch, Andrei
Schmidt, Sarah
Burandt, Thorstein
Zwickl-Bernhard, Sebastian
Löffler, Konstantin
Graabak, Ingeborg
2023-01-30
<p>Deliverable D7.3 is concerned with identifying, discussing and analysing the main barriers and incentives to investments in low-carbon solutions as well as the efficiency of possible policies to address the aforementioned barriers.</p>
<p>This document focuses on the description of the directions that could be considered to address the aforementioned barriers and facilitate the transition to a low-carbon society. The analysis is performed starting from an individual viewpoint where the potential barriers related to the behavior of the individuals are identified and discussed. Then the focus moves towards the analysis of the business dimension, where it concentrates on the potential barriers that could limit the proliferation of new business models related to flexibility and peer-to-peer solutions to the local management of energy consumption and production. Next, the attention shifts to the study of bigger aggregates considering both the technical aspects and the welfare-related aspects, where the focus is placed on understanding what is the impact of the widely used global least-cost solution towards different Countries and sectors involved in the transition process. The analysis is carried out considering how the least-cost solution of a long-term energy system model suggests operating to decarbonize the European energy system and benchmark this suggestion with the actual possibility that the considered countries have to carry out the plans suggested by the optimization tool.</p>
<p>Finally, an array of macroeconomic analyses of the effects of different types of barriers, from political to technological, is performed, as well as using the combination of a macroeconomic general equilibrium model and a long-term energy system model to understand which type of impact a decarbonization effort might have on the sectoral activities and on the economic growth of the European countries. The analysis is complemented with a survey investigating on stakeholders’ opinions about the impact of economic and regulatory aspects on the willingness to invest in low-carbon solutions. The main results shed light on the fact that technological development is key to the transition towards low carbon solutions and that regulatory aspects need to support the uptake and diffusion of new technology and promote cooperation, rather than penalize players that have not year caught up with the transition; this is further highlighted by the fact that calling off the decarbonization agreements by countries rich in fossil resources might bring economic benefits to those countries. This underlines how the decarbonization effort needs to be defined as a collective effort based on mutual help rather than on penalty mechanisms.</p>
https://doi.org/10.5281/zenodo.8289178
oai:zenodo.org:8289178
eng
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.8289177
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Policy Measures that Address Barriers and Market Failures in the Low-carbon Transition
info:eu-repo/semantics/report
oai:zenodo.org:8289149
2023-08-28T14:26:53Z
user-openentrance
user-eu
Huppmann, Daniel
Charousset, Sandrine
Belsnes, Michael
Graabak, Ingeborg
2023-04-28
<p>This document (Open ENTRANCE deliverable 4.5) suggests innovation recommendations for the continued development of the Open Platform, developed as part of the Open ENTRANCE project.</p>
<p>Section 1 provides an overview of new developments implemented in the modeling platform over the course of the project. Section 2 collects ideas and feature requests that arose over the course of the Open ENTRANCE project but which could not be implemented within this project. This document can therefore serve as input to other, ongoing Horizon projects like the European Climate and Energy Modeling Forum (<a href="http://www.ecemf.eu">ECEMF</a>) or <a href="http://www.prisma-horizon.eu">PRISMA</a>.</p>
<p>The ideas and feature requests presented in this document were collected throughout the project. In addition, a dedicated workshop session was held at the final consortium meeting, which took place in Trondheim on March 23, 2023.</p>
https://doi.org/10.5281/zenodo.8289149
oai:zenodo.org:8289149
eng
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.8289148
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Platform Innovation Recommendation
info:eu-repo/semantics/report
oai:zenodo.org:8065983
2023-06-25T14:27:05Z
openaire_data
user-openentrance
user-eu
Erik Alvarez
2023-06-21
<p>This dataset partially comprises the main results of case study 3 of the openENTRANCE project. Additionally, input data related to the power generation system and renewables profiles are included in the dataset.</p>
https://doi.org/10.5281/zenodo.8065983
oai:zenodo.org:8065983
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.8065982
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
openENTRANCE - Case Study 3 - Need for flexibility - Storage - version 21062023
info:eu-repo/semantics/other
oai:zenodo.org:5524025
2021-10-14T01:48:33Z
user-openentrance
user-eu
Auer, Hans
Crespo del Granado, Pedro
Oei, Pao-Yu
Hainsch, Karlo
Löffler, Konstantin
Burandt, Thorsten
Huppmann, Daniel
Graabak, Ingeborg
2020-10-14
<p>The ambition of the openENTRANCE project is to develop and establish an open, transparent and integrated modelling platform for assessing low-carbon transition pathways of the European energy system. In this context, the open source energy system model GENeSYS-MOD is one of the core models having been developed enabling quantitative scenario pathway studies of the future European energy system. The four quantitative studies presented in the openENTRANCE project and in this paper build upon the four storylines developed at the beginning of the openENTRANCE project. A storyline is a narrative describing possible future trajectories (pathways) of the energy transition. Storylines should be understood as possible future developments of the European energy system, which could occur equally without having a preference for one of them. Three of the storylines, and subsequently quantified scenario pathway studies in openENTRANCE comply with the (European fraction of the) 1.5 <sup>∘</sup>C global temperature increase limit. The fourth one approaches the 2.0 <sup>∘</sup>C limit. The quantified scenario pathway results not only show the needs of the fully open energy system model GENeSYS-MOD to find feasible solutions of the underlying analytical optimization problem, but more importantly highlight <em>what needs to be done in the future European energy system if we seriously intend to limit global warming!</em></p>
https://doi.org/10.5281/zenodo.5524025
oai:zenodo.org:5524025
eng
Zenodo
https://doi.org/10.1007/s00502-020-00832-7
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.5524024
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
e & i Elektrotechnik und Informationstechnik, 137, 346-358, (2020-10-14)
Development and modelling of different decarbonization scenarios of the European energy system until 2050 as a contribution to achieving the ambitious 1.5 ◦C climate target—establishment of open source/data modelling in the European H2020 project openENTRANCE
info:eu-repo/semantics/article
oai:zenodo.org:7997196
2023-06-19T07:45:16Z
openaire_data
user-iiasa-ece
user-openentrance
user-eu
Pisciella, Paolo
2023-06-02
<p>This dataset contains scenario results from the REMES:EU model as part of the macro-economic analysis in the openENTRANCE project.</p>
<p>The data file follows the IAMC data format and the conventions established by the openENTRANCE project. See <a href="https://github.com/openENTRANCE/openentrance">https://github.com/openENTRANCE/openentrance</a> for details.</p>
<p>Visit the openENTRANCE Scenario Explorer at <a href="https://data.ene.iiasa.ac.at/openentrance">https://data.ece.iiasa.ac.at/openentrance</a> for more information about the openENTRANCE project and other datasets.</p>
https://doi.org/10.5281/zenodo.7997196
oai:zenodo.org:7997196
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/iiasa-ece
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.7997195
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Results of the Macro-Economic Analysis by the REMES model in the openENTRANCE project
info:eu-repo/semantics/other
oai:zenodo.org:7997103
2023-06-30T11:54:04Z
openaire_data
user-iiasa-ece
user-openentrance
user-eu
Charousset, Sandrine
2023-06-02
<p>This dataset contains scenarios results for Case Study 1 of the openENTRANCE project.</p>
<p>Visit the openENTRANCE Scenario Explorer at <a href="https://data.ene.iiasa.ac.at/openentrance">https://data.ece.iiasa.ac.at/openentrance</a> for more information.</p>
https://doi.org/10.5281/zenodo.7997103
oai:zenodo.org:7997103
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/iiasa-ece
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.7997102
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Results for Case Study 1 of the openENTRANCE project
info:eu-repo/semantics/other
oai:zenodo.org:5520993
2021-09-22T13:48:23Z
user-openentrance
user-eu
Löffler, Konstantin
2021-09-16
<p>Discounting plays a large role in cost-optimization models, but is nevertheless often only covered in little detail in energy system models. The aim of this paper is to highlight the effects of varying discount rates and social costs of carbon in energy system models with the example of the Global Energy System (GENeSYS-MOD), propagating open debate and transparency about chosen parameters for model applications. In doing so, this paper adds to the academic discourse on socio-economic factors in energy system models and gives an outline to modelers in the field by providing example results. The results show that close-to-zero discount rates that factor in intergenerational equality, total emissions could be reduced by up to 41% until 2050 compared to the baseline discount rate of 5%. This effect is even increased when a carbon price akin to the actual social costs of carbon is chosen. This underlines the importance of the topic, which is, up to now, seldom covered in cost-optimizing energy system models.</p>
https://doi.org/10.1088/1748-9326/ac228a
oai:zenodo.org:5520993
eng
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Environmental Research Letters, 16, 10, (2021-09-16)
energy system modeling
discounting
social costs of carbon
energy policy
decarbonization pathways
GENeSYS-MOD
Social discounting, social costs of carbon, and their use in energy system models
info:eu-repo/semantics/article
oai:zenodo.org:8276341
2023-08-23T14:26:52Z
user-openentrance
user-eu
Zwickl-Bernhard, Sebastian
Auer, Hans
2022-07-22
<p>This paper investigates a possible future business case for green hydrogen production from hydropower. The main research question is to find the trade-offs for a run-of-river hydropower plant owner between the currently prevailing business model of wholesale electricity trading and, alternatively, production of green hydrogen. Hence, a bi-level optimization framework between a hydropower plant owner (H2 producer and price setter) and a transportation firm (H2 consumer) is developed. The empirical scaling of the numerical example describes Central Western European wholesale electricity market settings. Results indicate that the current market environment and price setup do not allow for profitable green hydrogen production as yet. However, an increasing CO2 price as the key determining parameter leads to improved competitiveness and expected profitability of the business case studied in this work. In the numerical example examined, a CO2 price above 245 EUR∕t triggers profitability, when green hydrogen production is competing with a future electricity contract price of 45 EUR∕MWh.</p>
https://doi.org/10.5281/zenodo.8276341
oai:zenodo.org:8276341
eng
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.8276340
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Energy Strategy Reviews, 43, 100912, (2022-07-22)
Green hydrogen
Hydropower
Non-cooperative game
Resource allocation
Profitability
CO2 price
Green hydrogen from hydropower: A non-cooperative modeling approach assessing the profitability gap and future business cases
info:eu-repo/semantics/article
oai:zenodo.org:5520441
2021-09-22T13:48:22Z
user-openentrance
user-eu
Zwickl-Bernhard, Sebastian
Auer, Hans
2021-01-08
<p>In this work, the main research question is how a high penetration of energy communities (ECs) affects the national electricity demand in the residential sector. Thus, the existing building stock of three European regions/countries, namely, the Iberian Peninsula, Norway, and Austria, is analyzed and represented by four different model energy communities based on characteristic settlement patterns. A tailor-made, open-source model optimizes the utilization of the local energy technology portfolio, especially small-scale batteries and photovoltaic systems within the ECs. Finally, the results on the national level are achieved by upscaling from the neighborhood level. The findings of different 2030 scenarios (building upon narrative storylines), which consider various socio-economic and techno-economic determinants of possible future energy system development, identify a variety of modification potentials of the electricity demand as a result of EC penetration. The insights achieved in this work highlight the important contributions of ECs to low-carbon energy systems. Future work may focus on the provision of future local energy services, such as increasing cooling demand and/or high shares of electric vehicles, further enhancement of the upscaling to the national level (i.e., considering the distribution network capacities), and further diversification of EC composition beyond the residential sector.</p>
https://doi.org/10.3390/en14020305
oai:zenodo.org:5520441
eng
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Energies, 14(2), 305, (2021-01-08)
energy communities
low-carbon energy systems
local energy technology portfolio
small-scale batteries
local self-consumption
upscaling
Citizen Participation in Low-Carbon Energy Systems: Energy Communities and Its Impact on the Electricity Demand on Neighborhood and National Level
info:eu-repo/semantics/article
oai:zenodo.org:8289189
2023-08-28T14:26:53Z
user-openentrance
user-eu
Boonman, Hettie
Crespo del Granado, Pedro
Pisciella, Paolo
Reynès, Frédéric
O'Reilly, Ryan
Garzon, Giulia
Graabak, Ingeborg
2022-10-31
<ul>
<li>We present a comparative analysis of macroeconomic impacts of the four openENTRANCE decarbonization scenarios until the year 2050 using the outcomes of two Computable General Equilibrium models.</li>
<li>The decarbonization scenarios have only moderate effects on GDP, they rather result in large sectoral shifts.</li>
<li>The ETS-price (Emission Trading System) is expected to increase extremely fast between 2040-2050. The last 20% CO2 reductions seem to be the costliest to avoid emitting.</li>
<li>Results are mainly robust across the two models.</li>
</ul>
<p> </p>
<p>The mitigation of the increasingly visible events and consequences of global warming are one of the biggest challenges of humankind. At the same time, national governments feel the need to keep the economy healthy, thriving and just for its population. Global warming – as the word suggest – is a global problem and cannot singlehandedly be solved by individual countries. The Paris Agreements, following on the 2015 Climate Change conference, was a big step towards a low-carbon future. Countries could submit their national independent contribution (INDC), expressing in which way they aim at reducing greenhouse gas emissions and reduce the risk and impact of climate change (Zhang et al (2021)). </p>
<p>Still, the dialogue between policy makers, researchers and industry can be further improved. This deliverable aims at contributing to this challenge by giving insight in the different ways to get to a low carbon-emissions energy system in Europe. <strong>We present a comparative analysis of environmental and socio-economic impacts of recently developed decarbonization scenarios for the region until the year 2050 using two Computable General Equilibrium models, EXIOMOD and REMES-EU.</strong></p>
<p>In the OpenENTRANCE project, four scenarios for decarbonization of the energy system have been developed (Hainsch et al (2022)). Three of the scenarios comply with the (European fraction of the) 1.5°C global temperature increase limit. The fourth one approaches the 2.0°C target. The scenarios focus on combinations of a strong technological development, fierce climate policies or a motivated society as driving factors behind the decarbonisation. The scenarios have been translated in quantified model input for the macro-economic models. This input was partly provided by an energy system model. This model provided for example the technology mix (e.g. wind, solar, hydro, etc) of the electricity sector, and the energy demand of non-energy producing sectors.</p>
<p>Both EXIOMOD and REMES-EU show <strong>strong declines in CO<sub>2</sub> emissions</strong>, forced by the cap-and-trade system implemented in the EU. Furthermore, both models <strong>predict an exponential growth in ETS-price between 2040-2050</strong>. The last CO<sub>2</sub> emissions are the costliest to prevent from emitting. In order to keep emissions below the cap on carbon, both models predict a steep<strong> increase in demand for electricity and a decline in demand for fossil-based energy sources</strong>. The <strong>effect of the decarbonisation scenarios on GDP is limited</strong>. Feedback effects from climate on economy are included via decreasing labour productivity due to higher temperatures. This results in a lower expected GDP when the economy does not decarbonize fast enough. While <strong>most of the results are robust for the model choice</strong>, the models differ regarding the price mechanisms. EXIOMOD shows increasing prices of fossil fuels, which are driven by the existence and increase of the carbon budget. These high fossil fuel prices in turn result in decreasing demand for fossil fuels. REMES-EU shows on the other hand CO<sub>2</sub> allowances supposed to be purchased alongside fossil fuels in proportion to the amount of emission that a particular fuel produces in a given sector. The increase in CO<sub>2</sub> price due to the lower carbon budget over time, lead to a decrease in demand of fossil fuels which, in turn, lead to a decrease in price of the aforementioned fuels.</p>
https://doi.org/10.5281/zenodo.8289189
oai:zenodo.org:8289189
eng
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.8289188
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Macro-economic impacts of low- carbon transition
info:eu-repo/semantics/report
oai:zenodo.org:8086794
2023-06-28T02:26:47Z
openaire_data
user-iiasa-ece
user-openentrance
user-eu
Mostafa Barani
Pedro Crespo del Granado
2023-06-27
<p>This dataset contains scenario results from the EMPIRE model as part of Case Study 1 in the openENTRANCE project.</p>
<p>The data file follows the IAMC data format and the conventions established by the openENTRANCE project. See <a href="https://github.com/openENTRANCE/openentrance">https://github.com/openENTRANCE/openentrance</a> for details.</p>
<p>Visit the openENTRANCE Scenario Explorer at <a href="https://data.ene.iiasa.ac.at/openentrance">https://data.ece.iiasa.ac.at/openentrance</a> for more information about the openENTRANCE project and other datasets.</p>
https://doi.org/10.5281/zenodo.8086794
oai:zenodo.org:8086794
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/iiasa-ece
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.8086793
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Results from the EMPIRE model in the openENTRANCE project
info:eu-repo/semantics/other
oai:zenodo.org:5521033
2021-09-22T13:48:23Z
user-openentrance
user-eu
Zwickl-Bernhard, Sebastian
Auer, Hans
2021-08-18
<p>In this paper, deep decarbonization in an urban neighborhood in Vienna, Austria is proposed focusing on decommissioning of the gas distribution grid for heat supply rather than trying to feed in “green” gas in the future. The core objective is to demonstrate that alternative network infrastructures and energy technologies ensure not only an adequate but also an even superior provision of local heat energy services. Two different deep decarbonization pathways are studied, namely, electrification of almost all energy services and expansion of the district heating network. In addition, future district cooling service supply is considered. The method applied couples and extends two open-source models offering a complete analysis toolkit covering a high spatial and temporal resolution. The results show that deep decarbonization of local multiple-energy carrier systems is possible, without being dependent on the existing distribution grid of natural gas. Possible stranded assets (also at the gas end-user level) must not play a decisive role, especially since the trade-off analyses in this work show that alternative scenarios of lower/zero-emission energy service provision are even more economical in the longer term since the CO2 price is expected to increase in the next decades. Future work may focus, among others, on the energy generation technology mix feeding into the district heating grid, the local mobility service needs, and a higher granularity to improve the assessment of the on-site (building-integrated) renewable generation potential associated with the emergence of energy community concepts.</p>
https://doi.org/10.1016/j.energy.2021.121805
oai:zenodo.org:5521033
eng
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Energy, 238, 121805, (2021-08-18)
Natural gas
Distribution grid decommissioning
Decarbonization
District heating/cooling
Open-source modeling
Urban neighborhoods
Demystifying natural gas distribution grid decommissioning: An open-source approach to local deep decarbonization of urban neighborhoods
info:eu-repo/semantics/article
oai:zenodo.org:7870715
2023-04-27T14:26:41Z
user-openentrance
user-eu
Holz, Franziska
Olmos, Luis
Charousset, Sandrine
Huppmann, Daniel
Graabak, Ingeborg
2023-04-27
<p>Task 5.3 is part of WP5 (“A suite of modeling tools”) and contributed to ensuring the functional characterization of models and model linkages, as well as to showing and providing proof of the coordinated use of the models. To this end, reduced case studies, so-called “case examples” were developed and tested. Model linking is carried out by exchange of input and output data of models. The data exchange is facilitated by the use of a common data format initially developed by the IAMC and a template (i.e., list of variables and regions) adopted from previous model comparison projects. In this report, we highlight the main features of the case examples and of the case example building process, with a particular focus on the model linking and the openENTRANCE variable template. We also report on the definitions of regions and variables in the IAMC/openENTRANCE template.</p>
https://doi.org/10.5281/zenodo.7870715
oai:zenodo.org:7870715
eng
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.7870714
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Open ENTRANCE deliverable 5.4 – Illustrative case examples for the coordinated use of models
info:eu-repo/semantics/report
oai:zenodo.org:8276366
2023-08-23T14:26:52Z
user-openentrance
user-eu
Zwinckl-Bernhard, Sebastian
Golab, Antonia
Perger, Theresia
Auer, Hans
2023-07-25
<p>The primary goal of this paper is to investigate the most cost-effective decommissioning and <a href="https://www.sciencedirect.com/topics/engineering/refurbishment">refurbishment</a> investment decision for existing gas networks. An optimization model is developed and tested on a real test bed in an Austrian federal state. The analysis is performed from the network operator’s perspective and depicts different network decommissioning or refurbishment options under the decision of supplying or not supplying available gas demands. Whether or not there is ensured supply, we find that smaller gas networks (in terms of pipeline capacity and network length) are needed in the future. Analyzed shadow prices indicate that a balance/trade-off between the cost-optimal gas network design with and without ensured supply could lead to a robust and economically competitive future for downsized gas networks. The results demonstrate that it is necessary to socialize network operators’ costs among the remaining consumers connected to the network in the future. This adds a cost component to consumers, which needs to be considered when determining the profitability of sustainable alternatives to natural gas.</p>
<ul>
</ul>
https://doi.org/10.1016/j.esr.2023.101138
oai:zenodo.org:8276366
eng
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Energy Strategy Reviews, 49, 101138, (2023-07-25)
Gas Network
Decommissionin
Decarbonization
Designing a model for the cost-optimal decommissioning and refurbishment investment decision of gas networks: application on a real test bed in Austria until 2050
info:eu-repo/semantics/article
oai:zenodo.org:5521160
2021-09-28T09:09:08Z
user-openentrance
user-eu
Auer, Hans
Crespo del Granado, Pedro
Huppmann, Daniel
Oei, Pao-yu
Hainsch, Karlo
Löffler, Konstantin
Burandt, Thorsten
Härtel, Philipp
Frischmuth, Felix
Graabak, Ingeborg
2020-05-30
<p>The four quantitative scenarios for low-carbon futures of the pan-European energy system presented in the following report build upon the four storylines developed at the beginning of the openENTRANCE project. A storyline is a narrative describing ideas for future trajectories (pathways) of the energy transition. It is important to stress that a storyline should not be understood in the sense of a prediction about a most likely future development. Each of the four possible energy futures described in the openENTRANCE project could occur equally without stating a preference or bias for a single outcome. Instead, they should be understood as four possible energy futures, which could occur equally without having a preference for one of them. Consequently, the corresponding quantified scenarios, determined by means of the open source energy system model GENESYS-MOD, represent the more concrete empirical frames of the four possible future developments of the pan-European energy system.</p>
<p>Since the ambition of the openENTRANCE project is to study low-carbon futures of the European energy system, three of the quantified scenarios comply with the (European fraction of the) 1.5°C global temperature increase limit. The fourth one (called ‘Gradual Development‘) approaches the 2.0°C target. This means that the remaining CO<sub>2</sub> budget available for Europe (derived from the Integrated Assessment Model MESSAGEix-GLOBIOM) fixes one of the challenging external constraints in the openENTRANCE scenario modelling exercises. In addition, available technology portfolios/breakthroughs (notably this also includes the availability/non-availability of carbon dioxide removal technologies) in the different storylines/scenarios as well as maximum feasible technology exchange rates (triggered by corresponding CO2 price needs) are determining parameters for achieving carbon neutrality in Europe in the years 2040 or 2050. In this context, it is important to note that the gradients of modelling results in terms of technology progression exchange rates in individual cases/sub-sectors presented in the scenario results might be difficult to imagine what is feasible in the real, “physical” world. Nonetheless, the quantified scenario results not only show the needs of the energy system model GENESYS-MOD to find feasible solutions of the underlying analytical optimisation problem, but more importantly highlight: <strong>what needs to be done in the future European energy system if we seriously intend to reach the ambitious global </strong><strong>heating targets.</strong></p>
https://doi.org/10.5281/zenodo.5521160
oai:zenodo.org:5521160
eng
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.5521159
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Quantitative Scenarios for Low Carbon Futures of the pan-European Energy System
info:eu-repo/semantics/report
oai:zenodo.org:5520431
2021-09-22T13:48:22Z
user-openentrance
user-eu
Perger, Theresia
Wachter, Lucas
Fleischhacker, Andreas
Auer, Hans
2020-12-11
<p>Distributed on-site PV generation enables traditional consumers to become active participants in a decentralized energy system. In this work, a linear program optimizing peer-to-peer trading between prosumers of a local energy community with PV systems and battery energy storage systems (BESSs) is developed. The community members are characterized by their individual willingness-to-pay for purchasing PV electricity generated by the community, which reflects their ambitions to reduce marginal emissions from the grid. By adding the willingness-to-pay, prosumers do not prefer their own PV generation over the community’s generation. The objective function of the optimization model maximizes the social welfare, which means maximizing the self- consumption of the entire community and optimally allocating generation between prosumers. The method is applied to community set-ups including households and small businesses. Results of an arbitrary case-study show improvements in the overall profitability of the PV systems and BESSs. BESSs further decrease imports from the grid by 15% due to flexibilities. The willingness-to-pay is a promising tool to save marginal emissions from the grid, and the case-study shows annual savings of up to 38%. The results show that the community set-up is able to sustain without any subsidies and it can compete in the electricity market. </p>
https://doi.org/10.1016/j.scs.2020.102634
oai:zenodo.org:5520431
eng
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Sustainable Cities and Society, 66, 102634, (2020-12-11)
Energy communities
Low-carbon society
Peer-to-peer trading
Optimization model
PV sharing
Willingness-to-pay
PV sharing in local communities: Peer-to-peer trading under consideration of the prosumers' willingness-to-pay
info:eu-repo/semantics/article
oai:zenodo.org:7997172
2023-06-30T11:54:03Z
openaire_data
user-iiasa-ece
user-openentrance
user-eu
Charousset, Sandrine
2023-06-02
<p>This dataset contains scenarios results for Case Study 5 of the openENTRANCE project.</p>
<p>Visit the openENTRANCE Scenario Explorer at <a href="https://data.ene.iiasa.ac.at/openentrance">https://data.ece.iiasa.ac.at/openentrance</a> for more information.</p>
https://doi.org/10.5281/zenodo.7997172
oai:zenodo.org:7997172
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/iiasa-ece
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.7997171
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Results for Case Study 5 of the openENTRANCE project
info:eu-repo/semantics/other
oai:zenodo.org:7997298
2023-06-19T07:45:13Z
openaire_data
user-iiasa-ece
user-openentrance
user-eu
Loeffler, Konstantin
Hanisch, Karlo
Brandt, Thorsten
2023-06-02
<p>This dataset contains quantitative pathways developed with the GENeSYS-MOD 3.1 model in the openENTRANCE project.</p>
<p>You can find additional information, including all model results and native input files, at the Supplementary Material for Deliverable D3.2 of the openENTRANCE project at <a href="https://doi.org/10.5281/zenodo.7760651">https://doi.org/10.5281/zenodo.7760651</a>.</p>
<p>The data file follows the IAMC data format and the conventions established by the openENTRANCE project. See <a href="https://github.com/openENTRANCE/openentrance">https://github.com/openENTRANCE/openentrance</a> for details.</p>
<p>Visit the openENTRANCE Scenario Explorer at <a href="https://data.ene.iiasa.ac.at/openentrance">https://data.ece.iiasa.ac.at/openentrance</a> for more information about the openENTRANCE project and other datasets.</p>
https://doi.org/10.5281/zenodo.7997298
oai:zenodo.org:7997298
Zenodo
https://doi.org/10.5281/zenodo.7760651
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/iiasa-ece
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.7997297
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
energy systems
GENeSYS-MOD
Europe
energy transition
openENTRANCE
GENeSYS-MOD pathways developed by the openENTRANCE project
info:eu-repo/semantics/other
oai:zenodo.org:7182594
2023-04-27T13:15:20Z
openaire_data
user-openentrance
user-eu
O'Reilly, Ryan
Cohen, Jed
Reichl, Johannes
2022-10-10
<p>Three data files are provided for Case Study 1 in the openENTRANCE project: Full_potential.V9.csv, metaData.Full_Potential.csv, and acheivable_NUTS2_summary.csv. The data covers 10 residential devices on the NUTS2 level for the EU27 + UK +TR + NO + CH from 2020-2050. The devices included are storage heater, water heater with storage capabilitites, air conditiong, heat circulation pump, air-to-air heat pump, refreigeration (includes refrigerators and freezers), dish washer, washing machine, and tumble drier.</p>
<p>Full_potential.V9.csv shows the NUTS2 level unadjusted loads for residential storage heater, water heater, air conditiong, circulation pump, air-to-air heat pump, refreigeration (includes refrigerators and freezers), dish washer, washing machine, and tumble drier using representative hours from 2020-2050. The loads provided here have not been adjusted with the direct load participation rates (see paper for more details). More details on the dataset can be found in the metaData.Full_Potential.csv file.</p>
<p>The acheivable_NUTS2_summary.csv shows the NUTS2 level acheivable direct load control potentials for the average hour in the respective year (years - 2020, 2022,2030,2040, 2050). </p>
https://doi.org/10.5281/zenodo.7182594
oai:zenodo.org:7182594
eng
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.7182593
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
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direct load control
demand response
electric vehicles
Residential electrical loads
Europe
heat pumps
energy transition
openENTRANCE - Case Study 1 - Residential Demand Response - Final Data
info:eu-repo/semantics/other
oai:zenodo.org:5521426
2021-09-23T13:48:33Z
user-openentrance
user-eu
Perger, Theresia
Auer, Hans
2020-10-13
<p>A method optimizing dynamic participation of prosumers in local energy communities is proposed. First, an optimization model maximizing the social welfare is applied to a community including PV systems and battery storages. The PV generation is peer-to-peer traded within the community considering each prosumer's individual willingness-to-pay for local PV generation. Dynamic participation is studied by adding a new prosumer and varying installed PV capacity and willingness-topay. The proposed method finds a new optimum of the extended community considering constraints limiting the deviation of each prosumer's annual profits and GHG emissions after adding the new prosumer compared to the original community.</p>
https://doi.org/10.5281/zenodo.5521426
oai:zenodo.org:5521426
eng
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.5521425
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
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2020 17th International Conference on the European Energy Market (EEM), Stockholm, Sweden, 16-18 September 2020
air pollution
battery storage plants
optimisation
photovoltaic power systems
profitability
energy communities
Peer-to-peer trading
Optimization model
PV Sharing
Willingness-to-pay
Fair Energy Sharing in Local Communities: Dynamic Participation of Prosumers
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:6576797
2022-05-24T13:50:44Z
user-openentrance
user-eu
Amos Scledorn
Rune Grønborg Junker
Daniela Guericke
Henrik Madsen
Dominik Franjo Dominkovic
2022-04-30
<p>Flexible and responsive demand is key to the decarbonising of energy systems. In this paper, an economic dispatch model of a district heating system, modelled as a linear program, is soft-linked to a so-called flexibility function of end-consumer responses to time-varying heat prices, modelled generically as a set of ordinary differential equations. This linkage allows us to determine the cost savings potential of demand response in order to quantify its role in smart energy systems. Our key contribution is an optimal soft-linking framework for energy system and demand response models, named Frigg. Frigg finds the aforementioned economic potential under consideration of end-consumer behaviour. The dynamics of this behaviour lead to a significant problem complexity and pose computational challenges. Hence, the proposed method decomposes the problem based on backward dynamic programming and solves it efficiently. The framework is to be understood as a generic blue-print for soft-linking demand response and energy system models: It can be applied to a variety of energy systems and sources of demand response as well as other objectives than production cost minimisation. We compute the cost savings in a case study of the district heating system of Ejby, Denmark, under different degrees of demand flexibility. The results are compared with the alternative of investing in heat storage systems. Our results suggest substantial cost savings through demand response. Nevertheless, cost savings from heat storage that is cost-optimal in size exceed those achieved through demand response in the system configurations analysed.</p>
https://doi.org/10.1016/j.apenergy.2022.119074
oai:zenodo.org:6576797
eng
Zenodo
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https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
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Applied Energy, 317, 119074, (2022-04-30)
Frigg
Demand response
Energy system optimisation
Solft-linking
District heating
Frigg: Soft-linking energy system and demand response models
info:eu-repo/semantics/article
oai:zenodo.org:4063317
2021-09-23T12:22:26Z
user-openentrance
Reichl, Johannes
2020-09-01
<p>Preprint of <a href="https://doi.org/10.1515/mcma-2020-2068">https://doi.org/10.1515/mcma-2020-2068</a> </p>
https://doi.org/10.1515/mcma-2020-2068
oai:zenodo.org:4063317
eng
Zenodo
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info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
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Estimating marginal likelihoods from the posterior draws through a geometric identity
info:eu-repo/semantics/article
oai:zenodo.org:5520652
2021-09-22T13:48:23Z
user-openentrance
user-eu
Löffler, Konstantin
Burandt, Thorsten
Hainsch, Karlo
Oei, Pao-Yu
Seehaus, Frederik
Wejda, Felix
2021-09-06
<p>With the energy sector being one of the largest sources of global greenhouse-gas emissions, a swift change in the ways of energy generation and consumption is needed for a fulfilment of climate goals. But while the existence of global warming and the resulting need for action are widely agreed upon, there isa lot of discussion around the concrete measures and their timeline. A major cause of this discussion is that of uncertainty, both with regard to possible outcomes, as well as to a multitude of factors such as future technology innovation (concerning both availability and costs), and final energy demands, but also socio-economic factors such as employment or sufficiency. This paper aims to give valuable insights into this uncertainty by applying the method of exploratory sensitivity analysis to an application of the Global Energy System Model (GENeSYS-MOD) for the German energy system. By computing over 1500 sensitivities across 11 core parameters, the key influential factors for the German Energiewende can be quantified, and possible chances, such as so-called no-regret options, as well as potentials barriers (if assumptions are not met) can be distilled. Results show that final energy demand developments,renewable potentials and costs, as well as carbon pricing are among the main drivers of the analyzed energy pathways. It would thus be highly beneficial for policy makers to focus on these key issues to ensure a timely transformation of the energy system and reach set climate targets.</p>
https://doi.org/10.1016/j.energy.2021.121901
oai:zenodo.org:5520652
eng
Zenodo
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https://zenodo.org/communities/eu
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Energy, 239, 121901, (2021-09-06)
Energy systems modeling
Decarbonization
Energy policy
Energy transition
Uncertainty
Long-term energy pathways
GENeSYS-MOD
Chances and barriers for Germany's low carbon transition -Quantifying uncertainties in key influential factors
info:eu-repo/semantics/article
oai:zenodo.org:8289103
2023-08-28T14:26:53Z
user-openentrance
user-eu
Charousset, Sandrine
Oudjane, Nadia
O'Reilly, Ryan
Crespo del Granado, Pedro
Barani, Mostafa
Perger, Theresia
Zwickl-Bernhard, Sebastien
Auer, Hans
Olmos, Luis
Ramos, Andrés
Graabak, Ingeborg
Alvarez, Erik F.
Härtel, Philipp
Frischmuth, Felix
Lepaul, Sebastien
Pinel, Dimitri
Wolfgang, Ove
Schledorn, Amos
Dominkovic, Dominik F.
Kirkil, Gokhan
Celebi, Emre
Yucekaya, Ahmet
Holz, Franziska
Belsnes, Michael
2023-06-30
<p>This report (Open ENTRANCE deliverable 6.2) presents results of the 8 case studies performed during the project, covering the main topics of the energy transition. The main objectives of the case studies were:</p>
<ul>
<li>Show the adequacy and relevance of the Open ENTRANCE platform. For this purpose, the<br>
case studies have been using scenarios, assumptions and data developed within Work Package<br>
3 “Scenario Building Exercises”, and the suite of modelling tools supplied by Work Package 5<br>
“Suite of Modelling tools”.</li>
<li>Show the ability of the proposed approach to answer specific questions related to the evolution<br>
of the energy system. This has been done with a specific focus on the effects of decentralisation,<br>
variability, the need for flexibility, real market functioning, integration of energy sectors,<br>
behaviour of individuals and communities of actors.</li>
<li>Provide complementary inputs (data) to Work Package 3 “Scenario Building Exercises”. This<br>
has taken place during the process of running the case studies, which conducted to a new version<br>
of the Scenarios in WP3.</li>
</ul>
<p>In order to perform the case studies, simulations were run using the linked models developed within WP5 “Suite of Modelling tools” and the scenario dataset developed within WP3 “Scenario Building Exercises”. As for the dataset, in order to ensure consistency among studies, supplemental data, which were needed on specific items, were added while performing the case studies. Therefore, it will be possible to easily re-run case studies or derive variants or challenge them by using other models/data.</p>
<p>Regarding the linkage of the data and models, it relied on the common Open ENTRANCE data format as well as on the nomenclature which is defining the variables and regions names, as well as establishing rules for using them. The case studies are the following:</p>
<ul>
<li>Case study 1 is dedicated to Demand-Response from household consumers. It evaluates the<br>
flexibility potential when using load-control with household consumers and study its impact on<br>
the integrated European electricity system cost, operation and investments needs.</li>
<li>Case study 2 is dedicated to behaviour of communities of actors. It studies shared energy<br>
management in different local energy community concepts, taking into account the individual<br>
preferences of the actors involved. Based on comprehensive modelling, the quantitative results<br>
have been up-scaled on country and European level.</li>
<li>Case study 3 is dedicated to flexibilities and storage. It analyses how the uses of flexible hydropower<br>
and more generally of different kinds of storages (pumped-hydro, batteries, gas. . . .)<br>
can tackle some of the main challenges of the energy transition.</li>
<li>Case study 4 is dedicated to cross-sector integration, with a specific focus on the flexibilities<br>
provided by electric vehicle owners to the electricity system. It also evaluates the impact of<br>
import hydrogen prices on the integrated system.</li>
<li>Case study 5 compares different levels of geographic coordination for investment decisions, both<br>
at regional and European level, focusing on the topic of decentralisation. In particular regional<br>
decisions with local objectives were compared with European coordinated decisions with global<br>
targets.</li>
<li>Case study 6 analyses the use of innovative technology in terms of underground rocks for seasonal<br>
storage of heat from summer to winter in a district in Oslo, Norway. The analyses show the<br>
impact on the energy system in the district.</li>
<li>Case study 7 evaluates how the use of flexibilities from the heating sector at different time scales<br>
(short-time planning with hourly to 5 minutes resolution) may have an impact on the system<br>
operation costs and network expansion needs in Denmark.</li>
<li>Case study 8 investigates the role of natural gas storage in current and future energy systems in<br>
Turkey.</li>
</ul>
https://doi.org/10.5281/zenodo.8289103
oai:zenodo.org:8289103
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.8289102
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
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Case study results
info:eu-repo/semantics/report
oai:zenodo.org:5520506
2021-09-22T13:48:22Z
user-openentrance
user-eu
Backe, Stian
Ahang, Mohammadreza
Tomasgard, Asgeir
2021-08-18
<p>This paper examines the importance of including operational scenarios representing short-term stochasticity in the long-term capacity expansion models with high shares of variable renewables. As scenario generation routines often are probabilistic, for example based on sampling, it is crucial that they ensure stable results in the capacity expansion model, so that it is the underlying uncertainty that decides the optimal solution, and not the approximation of that uncertainty in the model. However, it is unclear which operational scenario properties that are important to ensure good results and stability in stochastic models. This paper evaluates three sampling-based scenario generation routines in a multi-horizon stochastic capacity expansion problem representing the European electricity system. We compare the use of stochastic versus deterministic modelling with high shares of variable renewables. Further, we perform in-sample and out-of-sample stability tests on 90 scenario trees for each routine, and we compare the routines’ ability to produce stable system costs and capacity investments when approximating the optimal value from the real distribution. Results show that stochastic modelling with more than 80% share of variable renewables leads to more investments in both dispatchable and variable renewable capacity compared to deterministic modelling, which means that stochastic modelling should be used with very high shares of variable renewables. The scenario generation routine based on stratified sampling increases stability with the same number of operational scenarios compared to its alternatives, and scenario generation routines using stratified sampling should be further explored.</p>
https://doi.org/10.1016/j.apenergy.2021.117538
oai:zenodo.org:5520506
eng
Zenodo
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https://zenodo.org/communities/eu
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Applied Energy, 302, 117538, (2021-08-18)
Power system modelling
Stochastic programming
Multi-horizon representation
Scenario generation
Stability testing
Moment matching
Stable stochastic capacity expansion with variable renewables: Comparing moment matching and stratified scenario generation sampling
info:eu-repo/semantics/article
oai:zenodo.org:5520596
2021-09-22T13:48:23Z
user-openentrance
user-eu
Xiong, Bobby
Predel, Johannes
Crespo del Granado, Pedro
Egging-Bratseth, Ruud
2020-11-26
<p>The energy transition faces the challenge of increasing levels of decentralised renewable energy injection into an infrastructure originally laid out for centralised, dispatchable power generation. Due to limited transmission capacity and flexibility, large amounts of renewable electricity are curtailed. In this paper, we assess how Power-to-Gas facilities can provide spatial and temporal flexibility by shifting pressure from the electricity grid to the gas infrastructure. For this purpose, we propose a two-stage model incorporating the day-head spot market and subsequent redispatch. We introduce Power-to-Gas as a redispatch option and apply the model to the German electricity system. Instead of curtailing renewable electricity, synthetic natural gas can be produced and injected into the gas grid for later usage. Results show a reduction on curtailment of renewables by 12% through installing Power-to-Gas at a small set of nodes frequently facing curtailment. With the benefits of decentralised synthetic natural gas injection and usage, we exploit the advantages of coupling the two energy systems. The introduction of Power-to-Gas provides flexibility to the electricity system, while contributing to a higher effective utilisation of renewable energy sources as well as the natural gas grid.</p>
https://doi.org/10.1016/j.apenergy.2020.116201
oai:zenodo.org:5520596
eng
Zenodo
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Creative Commons Attribution 4.0 International
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Applied Energy, 283, 116201, (2020-11-26)
Power-to-Gas
Flexibility
Redispatch
Congestion management
Renewable energy
Sector coupling
Spatial flexibility in redispatch: Supporting low carbon energy systems with Power-to-Gas
info:eu-repo/semantics/article
oai:zenodo.org:8289228
2023-08-28T14:26:53Z
user-openentrance
user-eu
Lumbreras, Sara
Olmos, Luis
Quispe, Erik
Ramos, Andrés
Zwickl-Bernhard, Sebastian
Graabak, Ingeborg
2023-04-30
<p>The development and use of models as decision tools has increased steadily in the past few years. Given its complexity, the energy sector is one of the contexts where this trend can be seen more intensely. We can find different models that include sectoral models, macro-economic models, investment models, operation models or integrated assessment ones. These models have different scopes and granularities and have been developed at different institutions within different platforms. If the aim is to generate a perspective on the entire implications of the energy transition, it is necessary to use several of these models concurrently and in a consistent manner, that is, integrating them. This document presents the framework for the connection of models that has been developed in the context of openENTRANCE.</p>
https://doi.org/10.5281/zenodo.8289228
oai:zenodo.org:8289228
eng
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.8289227
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
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Analysis framework, functional specification of models, and conceptual assessment of the linkages among them defined in the Case Studies and Pathways
info:eu-repo/semantics/report
oai:zenodo.org:8278374
2023-08-25T14:26:52Z
user-openentrance
user-eu
Pinel, Dimitri
Belsnes, Michael Martin
Löffler, Konstantin
Charousset, Sandrine
Boonman, Hettie
Olmos, Luis
Huppmann, Daniel
Crespo del Granado, Pedro
Støa, Petter
Auer, Hans
Graabak, Ingeborg
2023-08-24
<p>The H2020 Open ENTRANCE project (open ENergy TRansition ANalyses for a low-Carbon Economy), running between 2019 and 2023, aimed to develop, use, and disseminate an open, transparent, and integrated modelling platform for assessing low-carbon transition scenarios in Europe. The project was successful in reaching these objectives.</p>
<p>The Open Platform helped identify the most cost-efficient alternatives for development of the energy system to reach pre-defined climate goals, to determine the macro-economic consequences and the distributional effects of the alternatives, and to explore impacts and options on different geographical levels. The Platform is open and can be used by any interested actor.</p>
<p>The Platform hosts a description and input and output data of the scenarios developed during the project. These give a common framework to conduct studies with consistent and open assumptions, contributing to their impact through the replicability of the results and the clear assumptions in the scenarios. The four scenarios are defined on three characteristics (policy exertion, smart society, and technological novelty) and share ambitious climate goals of limiting global warming to either 1.5 or 2 degrees.</p>
<p>The analysis of the development of the energy system under these scenarios shows that the present European policy is not sufficient to provide the region’s contribution required to limit the temperature increase to 1.5 or 2 degrees. To reach such a target, massive investment is necessary in the power sector as well as for the decarbonisation of other sectors before 2035. Efficiency gains from electrification of the transport and the heating sector will have a major role, while hydrogen will also be important for the decarbonisation of specific applications (e.g. air transport, steel production).</p>
<p>The transition can be achieved with little impact on the GDP. This is due to the fact that the impact of increased energy efficiency is able to counter the effect of the cap on emissions and of the feedback effects from climate change.</p>
<p>Eight case studies are demonstrating the potential of the Platform for detailed local analyses, delivering results on different topics or various geographic scopes, and allowing to validate the robustness of the results.</p>
<p>The results from the project, The Open Platform and the analyses results demonstrate their value by being used by 16 other projects on the end of the project. Maintenance will be necessary to preserve continued positive impact of the Platform.</p>
<p>Based on the project results, we recommend an increase in the speed of the energy transition to be back on track to meet the objectives of limiting temperature increase. This means a more rapid electrification of the transport sector, the installation of heat pumps in buildings, and investments in large amounts of renewable energy. The cost of inaction is larger than the cost of the transition.</p>
https://doi.org/10.5281/zenodo.8278374
oai:zenodo.org:8278374
eng
Zenodo
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https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.8278373
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
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Open ENTRANCE Deliverable 7.4 – Open ENTRANCE Synthesis and recommendations
info:eu-repo/semantics/report
oai:zenodo.org:8282598
2023-08-28T05:49:53Z
user-openentrance
user-eu
Morch, Andrei
Schmidt, Sarah
Crespo del Granado, Pedro
2022-10-20
<p>Reaching of the Pan-European decarbonisation targets requires radical steps (e.g., phase out of gas and coal) and the development of innovative business models to support the uptake of Renewable Energy Sources. Implementing new business models will be central to provide incentives for the advancement of and investments in new technologies. This study employs the e3value business modelling methodology for exploring hydrogen production from curtailed renewable electricity and identifies potential barriers, which may prevent investment into the Power-to-Gas infrastructure for utilising curtailed electricity from RES. Following the e3value methodology the study identifies the main involved actors, activities, and value exchanges between them. Based on the modelling the study identifies the critical barriers and suggests the next steps for resolving these.</p>
https://doi.org/10.1109/EEM54602.2022.9921163
oai:zenodo.org:8282598
IEEE
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EEM, 2022 18th International Conference on the European Energy Market (EEM), Ljubljana, 3-15 September 2022
Hydrogen energy
Power-to-Gas
Business modelling
Regulatory and policy barriers
Identification of barriers and investment determinants for hydrogen infrastructure: Development of new business models
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:8054693
2023-06-20T02:27:17Z
user-openentrance
user-eu
Auer, Hans
Löffler, Konstantin
Hainsch, Karlo
Burandt, Thorsten
Graabak, Ingeborg
Schmidt, Sarah
Yucekaya, Ahmet
Celebi, Emre
Gokhan, Kirkil
Zwickl-Bernhard, Sebastian
2022-06-01
<p>In this deliverable, we present updates of pan-European scenario results, together with selected highlights of European country-specific and region-specific scenario results, as well as consistent and coherent results on local community, neighbourhood and individual building level.</p>
<p>The scope of this deliverable includes two main aspects: First, the final openENTRANCE scenario model runs were performed both at the pan-European level and at the level of individual European countries. This was done after making selective improvements to the input data and refinements to the energy system model GENeSYS MOD, based on the findings from the results of the “draft” energy system decarbonization scenario results at the pan-European level (Deliverable D3.1).</p>
<p>Second, the development of consistent and coherent methods and algorithms to break down country-specific GENeSYS MOD modeling results across the different geographic aggregation levels to the smallest administrative unit level such as municipalities, districts, neighbourhoods, buildings, and finally to the individual end-user. To our knowledge, there has not yet been the ambition to conduct concrete energy system analyses to achieve specific climate goals across all levels of aggregation in a consistent and very concrete manner down to the individual end user. These kinds of “last mile” to end-user analyses, presented in Deliverable D3.2, are becoming increasingly important to get a sense of how to respond in very concrete ways to future climate challenges in practice.</p>
https://doi.org/10.5281/zenodo.8054693
oai:zenodo.org:8054693
Zenodo
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https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.8054692
info:eu-repo/semantics/openAccess
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Open ENTRANCE deliverable 3.2 – Quantitative Scenarios for Low Carbon Futures of the European Energy System on Country, Region and Local Level
info:eu-repo/semantics/report
oai:zenodo.org:8278641
2023-08-25T14:26:55Z
user-openentrance
user-eu
Ramos, Andrés
Alvarez, Erik F.
Lumbreras, Sara
2022-05-11
<p>The expansion of the transmission network will be a key enabler of the energy transition. However, the high level of technical detail involved in network studies, where a DCPF and the consideration of discrete investment are necessary, meant that it was accessible only to very specialized researchers. OpenTEPES changes the picture by providing an open-access tool with full functionality. OpenTEPES determines the investment plans for new power facilities (generating units and lines) necessary to supply future demand at minimum cost. OpenTEPES represents hierarchically the different time scopes involved in the planning decisions, from the medium to the very long term. It includes the uncertainty related to system operation, such as the availability of renewable energy sources and electricity demand, and multiple criteria such as investment cost or carbon emissions. OpenTEPES is a flexible tool that has been applied to planning projects in a European context. It has been developed as part of the H2020 project OpenENTRANCE and is now available open-source for the energy planning community.</p>
https://doi.org/10.1016/j.softx.2022.101070
oai:zenodo.org:8278641
eng
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SoftwareX, 18(June), 101070, (2022-05-11)
Transmission Expansion Planning
Generation Expansion Planning
Stochastic optimization
OpenTEPES: Open-source Transmis
info:eu-repo/semantics/article
oai:zenodo.org:5521135
2021-09-28T09:09:43Z
user-openentrance
user-eu
Auer, Hans
Crespo del Granado, Pedro
Backe, Stian
Pisciella, Paolo
Hainsch, Karlo
Holz, Franziska
Graabak, Ingeborg
2019-11-30
<p>The mitigation of the increasingly visible events and consequences of global warming and climate change are one of the biggest challenges of humankind. Ultimately, a global effort is necessary to implement corresponding corrective actions and strategies. A key aspect in this context is to limit GHG (greenhouse gas) emissions in the future. One of the most prominent representatives in terms of emissions is CO2. Moreover, limiting the remaining global CO2 budget in the energy and transport systems is one of the key necessities to comply with a maximum global temperature increase.</p>
<p>The European Commission is fully committed to several climate-related challenges and takes responsibility for immediate decisive policy actions necessary. Recently, in November 2018, the long term strategic vision to reduce GHG emissions was presented, indicating how Europe can lead the way to climate neutrality (an economy with net-zero GHG emissions) in 2050 and beyond. Strategies and options have been explored on how this can be achieved by looking at all the key economic sectors. Anticipating climate neutrality in 2050 in a global context (i.e. 100% GHG emission reduction compared to 1990 level) would limit global temperature increase to 1.5 °C, and avoid some of the worst climate impacts and reduce the likelihood of extreme weather events and others.</p>
<p>Logically, Europe cannot decide on policies and strategies on global level. However, Europe at least can act on the leading edge of implementing climate mitigation policies in Europe and thus act as a global role model. In the context, one of the main challenges is to anticipate the future development of the energy system. This, however, is not easy. From today’s point-of-view, it is virtually impossible to anticipate how a future energy system will look like in detail. Too many uncertainties in terms of possible future developments exist. Therefore, it is important to consider at least future narratives in terms of possible developments, cornerstones and features of a future energy system.</p>
<p>This is exactly what this document, Deliverable D7.1, is about. The overall motivation of this document is to develop narratives of a few realistic and possible future energy worlds. It is important to note here, that the storyteller does not have any preference about one (or a most probable) future development. On the contrary, several of the possible narratives presented in this document can<br>
happen in the future likewise.</p>
https://doi.org/10.5281/zenodo.5521135
oai:zenodo.org:5521135
eng
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.5521134
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Storylines for Low Carbon Futures of the European Energy System exchange format and template
info:eu-repo/semantics/report
oai:zenodo.org:8283028
2023-08-28T05:49:49Z
user-openentrance
user-eu
Sævareid, Erik
Durakovic, Goran
Knudsen, Brage Rugstad
Straus, Julian
Tomasgard, Asgeir
2022-10-20
<p>To enable offshore wind in the North Sea to take a key, rapid role in the European energy transition, massive grid expansions and investments are required. We consider the impact of such grid expansion in the North Sea on the future European power prices. To this end, we compare, using the power-system model EMPIRE, a scenario in which wind farms can only connect to their own countries, and a scenario in which the wind farms can connect both to other countries and all other wind farms. Uncertainty is implemented in the hourly power load as well as the renewable power production in each system node. The nodes in this work represent the countries in the European power system as well as the major wind farm projects in the North Sea.Our results indicate that allowing the offshore wind farms to connect to other countries and wind farms can have a significant impact on the power prices in the different prize zones. These observed effects depend on the level of investments in the North Sea wind farms. Furthermore, allowing for interconnections between countries through the North Sea wind farms significantly increases the investments in the North Sea by means of much larger wind farm capacities and comprehensive grid development. Our results also highlight how certain offshore wind farm nodes may be mainly used for electric power export, rather than covering the domestic power demand. For such wind farm</p>
https://doi.org/10.1109/EEM54602.2022.9921174
oai:zenodo.org:8283028
eng
IEEE
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
EEM, 2022 18th International Conference on the European Energy Market (EEM), Ljubljana, 13-15 September 2022
North Sea
Offshore wind
Capacity expansion
Energy transition , Optimization
Hybrid Versus Radial Offshore Wind Connections: Power Grid Investments in the North Sea
info:eu-repo/semantics/conferencePaper
oai:zenodo.org:7760652
2023-03-23T12:22:45Z
openaire_data
user-genesys-mod
user-openentrance
user-eu
Löffler, Konstantin
Hainsch, Karlo
Burandt, Thorsten
2022-05-01
<p>This repository contains the supplementary material for Open ENTRANCE Deliverable D3.2 (https://openentrance.eu/2022/07/06/quantitative-scenarios-for-low-carbon-futures-of-the-european-energy-system-oncountry-region-and-local-level/)</p>
<p>It contains all input and output files, including GAMS model files of the used GENeSYS-MOD version (model version v3.1-oE).</p>
https://doi.org/10.5281/zenodo.7760652
oai:zenodo.org:7760652
Zenodo
https://zenodo.org/communities/genesys-mod
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.7760651
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Open ENTRANCE Deliverable D3.2 - Supplementary Material
info:eu-repo/semantics/other
oai:zenodo.org:5521120
2021-09-28T09:10:15Z
user-openentrance
user-eu
Huppmann, Daniel
Charousset-Brignol, Sandrine
Graabak, Ingeborg
2019-10-30
<p>The project ‘Open ENergy TRansition ANalyses for a low-carbon Economy’ – openENTRANCE develops an open, transparent and integrated modelling platform for assessing low-carbon transition pathways. The platform will gather a suite of state-of-the-art modelling tools and data for covering the multiple dimensions of a green and clean energy transition.</p>
<p>openENTRANCE is a project funded by the European Commission under the Horizon 2020 framework and implemented by a consortium of 14 leading European research institutions, Universities and civil society organisations.</p>
<p>The openENTRANCE project will produce a number of datasets concerning historic development and scenarios of future energy transitions in Europe. We are dedicated to the goal of the European Commission and the Horizon 2020 programme to make all academic work FAIR. All historical and scenario data sets will be made available via the “open platform” (developed in WP4)1, with<br>
references to supporting literature and hyperlinks (where possible) to the open-source modelling frameworks used to generate the scenarios. All academic publications and policy briefs resulting from this project will be made available either following the green or gold open access standard and will be published on a dedicated project website. The data will be useful for policymakers and researchers to assess quantified scenarios of the energy transition and related policy measures. The open platform will facilitate the reuse of results from openENTRANCE as reference in scientific work by other modelling teams and for science communication activities by NGOs and other organizations working on related topics.</p>
https://doi.org/10.5281/zenodo.5521120
oai:zenodo.org:5521120
eng
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.5521119
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Data Management Plan Version 1.0, Oct 2019
info:eu-repo/semantics/report
oai:zenodo.org:5521098
2021-09-28T09:11:32Z
user-openentrance
user-eu
Krey, Volker
Huppmann, Daniel
Charousset-Brignol, Sandrine
Camacho, Luis Olmos
Cohen, Jed
Galán, Andrés Ramos
Pisciella, Paolo
Boonman, Hettie
Perger, Theresia
Haertel, Philipp
Graabak, Ingeborg
Crespo del Granado, Pedro
2019-10-31
<p>Providing results from energy-economic modelling to policymakers and the public at large in a transparent and accessible format requires intuitive, web-based tools for visualization and analysis. In addition, making methods, structural information and detailed parametric model assumptions transparently available to the wider scientific community is important to live up to scientific standards and establish trust in the results of model-based studies.</p>
<p>In order to facilitate the linking of different energy models in an efficient way, on the one hand efficient processes and workflows are needed and on the other hand standardized ways of exchanging information among the models. This requires the development of both an improved modelling platform infrastructure as well as the standards and “handshake” definitions to exchange data between models within the consortium and disseminate results underpinning policy insights to the broader scientific community and all stakeholders of this project.</p>
<p>The openENTRANCE consortium has thus embarked on the creation of common data exchange formats to share model input data and quantitative results. Ideally such standards build on existing standards to enable faster uptake across the energy modelling community. IIASA has been involved in the process of developing of such standards in the context of the Integrated Assessment Modelling Consortium (IAMC), and drawing on the experiences and lessons learned there to ensure the development of standards that are compatible with norms used in other disciplines.</p>
https://doi.org/10.5281/zenodo.5521098
oai:zenodo.org:5521098
eng
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.5521097
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
Data exchange format and template
info:eu-repo/semantics/report
oai:zenodo.org:7871106
2023-04-27T14:26:43Z
openaire_data
user-openentrance
user-eu
O'Reilly, Ryan
Cohen, Jed
Reichl, Johannes
2022-10-10
<p>Data files and Python and R scripts are provided for Case Study 1 of the openENTRANCE project. The data covers 10 residential devices on the NUTS2 level for the EU27 + UK +TR + NO + CH from 2020-2050. The devices included are full battery electric vehicles (EV), storage heater (SH), water heater with storage capabilitites (WH), air conditiong (AC), heat circulation pump (CP), air-to-air heat pump (HP), refrigeration (includes refrigerators (RF) and freezers (FR)), dish washer (DW), washing machine (WM), and tumble drier (TD). The data for the study uses represenative hours to describe load expectations and constraints for each residential device - hourly granularity from 2020 to 2050 for a representative day for each month (i.e. 24 hours for an average day in each month).</p>
<p>The aggregated final results are in Full_potential.V9.csv and acheivable_NUTS2_summary.csv. The file metaData.Full_Potential.csv is provided to guide users on the nomenclature in Full_potential.V9.csv and the disaggregated data sets.The disaggregated loads can be found in d_ACV8.csv, d_CPV6.csv, d_DWV6.csv, d_EVV7.csv, d_FRV5.csv, d_HPV4.csv, d_RFV5.csv, d_SHV7.csv, d_TDV6.csv, d_WHV7.csv, d_WMV6.csv while the disaggregated maximum capacities p_ACV8.csv, p_CPV6.csv, p_DWV6.csv, p_EVV7.csv, p_FRV5.csv, p_HPV4.csv, p_RFV5.csv, p_SHV7.csv, p_TDV6.csv, p_WHV7.csv, p_WMV6.csv. </p>
<p>Full_potential.V9.csv shows the NUTS2 level unadjusted loads for the residential devices using representative hours from 2020-2050. The loads provided here have not been adjusted with the direct load participation rates (see paper for more details). More details on the dataset can be found in the metaData.Full_Potential.csv file.</p>
<p>The acheivable_NUTS2_summary.csv shows the NUTS2 level acheivable direct load control potentials for the average hour in the respective year (years - 2020, 2022,2030,2040, 2050). These summaries have allready adjusted the disaggregated loads with direct load participation rates from participation_rates_country.csv.</p>
<p>A detailed overview of the data files are provided below. Where possible, a brief description, input data, and script use to generate the data is provided. If questions arise, first refer to the publication. If something still needs clarification, send an email to ryano18@vt.edu.</p>
<p><strong>Description of data provided</strong></p>
<ol>
<li>Achievable_NUTS2_summary.csv
<ol>
<li>Description
<ol>
<li>Average hourly achievable direct load potentials for each NUTS2 region and device for 2020, 2022, 2030,2040, 2050</li>
</ol>
</li>
<li>Data input
<ol>
<li>Full_potential.V9.csv</li>
<li>participation_rates_country.csv</li>
<li>P_inc_SH.csv</li>
<li>P_inc_WH.csv</li>
<li>P_inc_HP.csv</li>
<li>P_inc_DW.csv</li>
<li>P_inc_WM.csv</li>
<li>P_inc_TD.csv</li>
</ol>
</li>
<li>Script
<ol>
<li>NUTS2_acheivable.R</li>
</ol>
</li>
</ol>
</li>
<li>COP_.1deg_11-21_V1.csv
<ol>
<li>Description
<ol>
<li>NUTS2 average coefficient of performance estimates from 2011-2021 daily temperature</li>
</ol>
</li>
<li>Data
<ol>
<li>tg_ens_mean_0.1deg_reg_2011-2021_v24.0e.nc</li>
<li>NUTS_RG_01M_2021_3857.shp</li>
<li>nhhV2.csv</li>
</ol>
</li>
<li>Script
<ol>
<li>COP_from_E-OBS.R</li>
</ol>
</li>
</ol>
</li>
<li>Country dd projections.csv
<ol>
<li>Description
<ol>
<li>Assumptions for annual change in CDD and HDD</li>
<li>Spinoni, J., Vogt, J. V., Barbosa, P., Dosio, A., McCormick, N., Bigano, A., & Füssel, H. M. (2018). Changes of heating and cooling degree‐days in Europe from 1981 to 2100. International Journal of Climatology, 38, e191-e208.</li>
<li>Expectations for future HDD and CDD used the long-run averages and country level expected changes in the rcp45 scenario</li>
</ol>
</li>
</ol>
</li>
<li>EV NUTS projectionsV5.csv
<ol>
<li>Description
<ol>
<li>NUTS2 level EV projections 2018-2050</li>
</ol>
</li>
<li>Data input
<ol>
<li>EV projectionsV5_ave.csv
<ol>
<li>Country level EV projections</li>
</ol>
</li>
<li>NUTS 2 regional share of national vehicle fleet
<ol>
<li>Eurostat - Vehicle Nuts.xlsx</li>
</ol>
</li>
</ol>
</li>
<li>Script
<ol>
<li>EVprojections_NUTS_V5.py</li>
</ol>
</li>
</ol>
</li>
<li>EV_NVF_EV_path.xlsx
<ol>
<li>Description
<ol>
<li>Country level – EV share of new passenger vehicle fleet</li>
<li>From: Mathieu, L., & Poliscanova, J. (2020). Mission (almost) accomplished. <em>Carmakers’ Race to Meet the</em>, <em>21</em>.</li>
</ol>
</li>
</ol>
</li>
<li>EV_parameters.xlsx
<ol>
<li>Description
<ol>
<li>Parameters used to calculate future loads from EVs</li>
<li>Wunit_EV – represents annual kWh per EV</li>
<li>evLIFE_150kkm
<ol>
<li>number of years</li>
<li>represents usable life if EV only lasted 150 thousand km. Hence, 150,000/average km traveled per year with respect to country (this variable is dropped and not used for estimation).</li>
</ol>
</li>
<li>Average age/#years assuming 150k life – represents
<ol>
<li>Number of years</li>
<li>Average between evLIFE_150kkm and average age of vehicle with respect to the country</li>
</ol>
</li>
</ol>
</li>
</ol>
</li>
<li>full_potentialV9.csv
<ol>
<li>Description
<ol>
<li>Final data that shows hourly demand (Maximum Reduction) and (Maximum Dispatch for each device, region, and year.
<ol>
<li>This data has not been adjusted with participation_rates_country.csv</li>
<li>Maximum dispatch is equal to max capacity – hourly demand with respect to the device, region, year, and hour.</li>
</ol>
</li>
</ol>
</li>
<li>Script
<ol>
<li>Full_potentialV9.py</li>
</ol>
</li>
</ol>
</li>
<li>gils projection assumptions.xlsx
<ol>
<li>Description
<ol>
<li>Data from: Gils, H. C. (2015). Balancing of intermittent renewable power generation by demand response and thermal energy storage.</li>
<li>A linear extrapolation was used to determine values for every year and country 2020-2050. AC – Air Conditioning, SH – Storage Heater, WH – Water heater with storage capability, CP – heat circulation pump, TD – Tumble Drier, WM – Washing Machine, DW -Dish Washer, FR – Freezer, RF – Refrigerator. The results are in the files shown below.
<ol>
<li>nflh – full load hours
<ol>
<li>nflh_ac.csv</li>
<li>nflh_cp.csv</li>
</ol>
</li>
<li>wunit – annual energy consumption
<ol>
<li>Wunit_rf_fr.csv</li>
</ol>
</li>
<li>Pcycle – power demand per cycle
<ol>
<li>Pcycle_wm.csv</li>
<li>Pcycle_dw.csv</li>
<li>Pcycle_td.csv</li>
</ol>
</li>
<li>Punit – power damand for device
<ol>
<li>Punit_ac.csv</li>
<li>Punit_cp.csv</li>
</ol>
</li>
<li>r – country level household ownership rates of residential device
<ol>
<li>rfr.csv</li>
<li>rrf.csv</li>
<li>rwm.csv</li>
<li>rtd.csv</li>
<li>rdw.csv</li>
<li>rac.csv</li>
<li>rwh.csv</li>
<li>rcp.csv</li>
<li>rsh.csv</li>
</ol>
</li>
<li>Script
<ol>
<li>openENTRANCE projections.py</li>
</ol>
</li>
</ol>
</li>
</ol>
</li>
</ol>
</li>
<li>heat_pump_hourly_share.csv
<ol>
<li>Description
<ol>
<li>Hours share of daily energy demand</li>
<li>From ENTROS TYNDP – Charts and Figures
<ol>
<li><a href="https://2020.entsos-tyndp-scenarios.eu/download-data/#download">https://2020.entsos-tyndp-scenarios.eu/download-data/#download</a></li>
</ol>
</li>
</ol>
</li>
</ol>
</li>
<li>hourlyEVshares.csv
<ol>
<li>Description
<ol>
<li>Hours share of daily energy demand</li>
<li>From My Electric Avenue Study
<ol>
<li><a href="https://eatechnology.com/consultancy-insights/my-electric-avenue/">https://eatechnology.com/consultancy-insights/my-electric-avenue/</a></li>
</ol>
</li>
</ol>
</li>
</ol>
</li>
<li>HP_transitionV2.csv
<ol>
<li>Description
<ol>
<li>Used to create Qhp_thermal_MWh_projectedV2.csv</li>
<li>Final_energy_15-19
<ol>
<li>Average final energy demand for the residential heating sector between 2015-2019</li>
</ol>
</li>
<li>Final_energy_15-19_nonEE
<ol>
<li>Average final energy demand for the residential heating sector for energy sources that are not energy efficient between 2015-2019 (see paper for sources)</li>
</ol>
</li>
<li>Final_energy_15-19_nonEE_share
<ol>
<li>share of inefficient heating sources</li>
</ol>
</li>
<li>HP_thermal_2018
<ol>
<li>Thermal energy provided by residential heat pumps in 2018</li>
</ol>
</li>
<li>HP_thermal_2019
<ol>
<li>Thermal energy provided by residential heat pumps in 2019</li>
</ol>
</li>
<li>See publication for data sources</li>
</ol>
</li>
</ol>
</li>
<li>Nflh_ac.csv, nflh_cp.csv
<ol>
<li>See gils projection assumptions.xlsx</li>
</ol>
</li>
<li>nhhV2
<ol>
<li>Description
<ol>
<li>Expected number of households for NUTS2 regions for 2020-2050</li>
<li>See publication for data sources</li>
</ol>
</li>
<li>Script
<ol>
<li>EUROSTAT_POP2NUTSV2.R</li>
</ol>
</li>
</ol>
</li>
<li>NUTS0_thermal_heat_annum.csv
<ol>
<li>Description
<ol>
<li>Country level residential annual thermal heat requirements in kWh</li>
<li>Used to determine maximum dispatch in openENTRANCE final V14.py</li>
<li>Mantzos, L., Wiesenthal, T., Matei, N. A., Tchung-Ming, S., Rozsai, M., Russ, P., & Ramirez, A. S. (2017). <em>JRC-IDEES: Integrated Database of the European Energy Sector: Methodological Note</em> (No. JRC108244). Joint Research Centre (Seville site).</li>
</ol>
</li>
</ol>
</li>
<li>p_ACV8.csv, p_CPV6.csv, p_DWV6.csv, p_EVV7.csv, p_FRV5.csv, p_HPV4.csv, p_RFV5.csv, p_SHV7.csv, p_TDV6.csv, p_WHV7.csv, p_WMV6.csv
<ol>
<li>Description
<ol>
<li>Maximum capacity – load for a device can never exceed maximum capacity</li>
</ol>
</li>
<li>Data
<ol>
<li>gils projection assumptions.xlsx</li>
</ol>
</li>
<li>Script
<ol>
<li>openENTRANCE final V14.py</li>
</ol>
</li>
</ol>
</li>
<li>P_inc_DW.csv, P_inc_HP.csv, P_inc_SH.csv, P_inc_TD.csv, P_inc_WH.csv, P_inc_WM.csv, SAMPLE_PINC.csv
<ol>
<li>Description
<ol>
<li>Unadjusted average hourly potential for increase by NUTS2 region for 2018-2050</li>
</ol>
</li>
<li>Data
<ol>
<li>d_ACV8.csv, d_CPV6.csv, d_DWV6.csv, d_EVV7.csv, d_FRV5.csv, d_HPV4.csv, d_RFV5.csv, d_SHV7.csv, d_TDV6.csv, d_WHV7.csv, d_WMV6.csv
<ol>
<li>Theoretical maximum reduction / load of the respective device</li>
</ol>
</li>
<li>p_ACV8.csv, p_CPV6.csv, p_DWV6.csv, p_EVV7.csv, p_FRV5.csv, p_HPV4.csv, p_RFV5.csv, p_SHV7.csv, p_TDV6.csv, p_WHV7.csv, p_WMV6.csv
<ol>
<li>Maximum capacity</li>
</ol>
</li>
</ol>
</li>
<li>Script
<ol>
<li>P_increaseV2.py</li>
</ol>
</li>
</ol>
</li>
<li>Pcycle_dw.csv, Pcycle_td.csv, Pcycle_wm.csv
<ol>
<li>Description
<ol>
<li>power demand per cycle kWh</li>
<li>See gils projection assumptions.xlsx</li>
</ol>
</li>
</ol>
</li>
<li>Punit_ac.csv, Punit_cp.csv
<ol>
<li>Description
<ol>
<li>Unit capacities kWh</li>
<li>See gils projection assumptions.xlsx</li>
</ol>
</li>
</ol>
</li>
<li>Qhp_thermal_MWh_projectedV2.csv
<ol>
<li>Description
<ol>
<li>NUTS2 expectations for thermal energy demand met by heat pumps for 2022-2050</li>
<li>Assumes a linear decomposition of non-renewable and non-energy efficient heating sources until 2050</li>
</ol>
</li>
<li>Data
<ol>
<li>HP_transitionV2.csv</li>
<li>nhhV2.csv</li>
</ol>
</li>
<li>Script
<ol>
<li>HP_projection_nuts.py</li>
</ol>
</li>
</ol>
</li>
<li>rac.csv, rcp.csv, rdw.csv, rfr.csv, rrf.csv, rsh.csv, rtd.csv, rwh.csv, rwm.csv
<ol>
<li>Description
<ol>
<li>Household ownership rates</li>
<li>See gils projection assumptions.xlsx</li>
</ol>
</li>
</ol>
</li>
<li>s_hdd nutsV3.csv, s_cdd nutsV3.csv, yr_hdd nutsV3.csv, yr_cdd nutsV3.csv
<ol>
<li>Description
<ol>
<li>s_hdd nutsV3.csv and s_cdd nutsV3.csv – months share of total heating and cooling degree days (yr_hdd and yr_cdd respectively)</li>
<li>yr_hdd nutsV3.csv and yr_cdd nutsV3.csv – annual heating and cooling degree days respectively</li>
<li>long run (2011-2021) average NUTS 2 level hdd and cdd</li>
</ol>
</li>
</ol>
</li>
<li>s_wash nuts_V2.csv
<ol>
<li>Description
<ol>
<li>Hours share of daily energy demand for washing machine, tumble drier, and dishwasher</li>
</ol>
</li>
<li>Data
<ol>
<li>stamminger_V2.xlsx</li>
</ol>
</li>
<li>Script
<ol>
<li>S_wash_nuts_V2.py</li>
</ol>
</li>
</ol>
</li>
<li>Stamminger_2009.csv
<ol>
<li>Description
<ol>
<li>Hours share of daily energy demand for water heater – WH, storage heater – SH, air conditioner AC, heat circulation pump – CP</li>
<li>From Stamminger, R. (2009). Synergy potential of smart domestic appliances in renewable energy systems.</li>
</ol>
</li>
</ol>
</li>
<li>Time_index.csv
<ol>
<li>Used to create the appropriate timestamp for representative hours</li>
</ol>
</li>
<li>Wunit_rf_fr.csv
<ol>
<li>Annual energy consumption for refrigeration and freezers</li>
<li>See gils projection assumptions.xlsx</li>
</ol>
</li>
</ol>
https://doi.org/10.5281/zenodo.7871106
oai:zenodo.org:7871106
eng
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.7182593
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
direct load control
demand response
electric vehicles
Residential electrical loads
Europe
heat pumps
energy transition
openENTRANCE - Case Study 1 - Residential Demand Response - Data and Scripts
info:eu-repo/semantics/other
oai:zenodo.org:7186521
2023-04-27T13:15:20Z
openaire_data
user-openentrance
user-eu
O'Reilly, Ryan
Cohen, Jed
Reichl, Johannes
2022-10-10
<p>Data files and Python and R scripts are provided for Case Study 1 of the openENTRANCE project. The data covers 10 residential devices on the NUTS2 level for the EU27 + UK +TR + NO + CH from 2020-2050. The devices included are full battery electric vehicles (EV), storage heater (SH), water heater with storage capabilitites (WH), air conditiong (AC), heat circulation pump (CP), air-to-air heat pump (HP), refrigeration (includes refrigerators (RF) and freezers (FR)), dish washer (DW), washing machine (WM), and tumble drier (TD). The data for the study uses represenative hours to describe load expectations and constraints for each residential device - hourly granularity from 2020 to 2050 for a representative day for each month (i.e. 24 hours for an average day in each month).</p>
<p>The aggregated final results are in Full_potential.V9.csv and acheivable_NUTS2_summary.csv. The file metaData.Full_Potential.csv is provided to guide users on the nomenclature in Full_potential.V9.csv and the disaggregated data sets.The disaggregated loads can be found in d_ACV8.csv, d_CPV6.csv, d_DWV6.csv, d_EVV7.csv, d_FRV5.csv, d_HPV4.csv, d_RFV5.csv, d_SHV7.csv, d_TDV6.csv, d_WHV7.csv, d_WMV6.csv while the disaggregated maximum capacities p_ACV8.csv, p_CPV6.csv, p_DWV6.csv, p_EVV7.csv, p_FRV5.csv, p_HPV4.csv, p_RFV5.csv, p_SHV7.csv, p_TDV6.csv, p_WHV7.csv, p_WMV6.csv. </p>
<p>Full_potential.V9.csv shows the NUTS2 level unadjusted loads for the residential devices using representative hours from 2020-2050. The loads provided here have not been adjusted with the direct load participation rates (see paper for more details). More details on the dataset can be found in the metaData.Full_Potential.csv file.</p>
<p>The acheivable_NUTS2_summary.csv shows the NUTS2 level acheivable direct load control potentials for the average hour in the respective year (years - 2020, 2022,2030,2040, 2050). These summaries have allready adjusted the disaggregated loads with direct load participation rates from participation_rates_country.csv.</p>
<p>A detailed overview of the data files are provided below. Where possible, a brief description, input data, and script use to generate the data is provided. If questions arise, first refer to the publication. If something still needs clarification, send an email to ryano18@vt.edu.</p>
<p><strong>Description of data provided</strong></p>
<ol>
<li>Achievable_NUTS2_summary.csv
<ol>
<li>Description
<ol>
<li>Average hourly achievable direct load potentials for each NUTS2 region and device for 2020, 2022, 2030,2040, 2050</li>
</ol>
</li>
<li>Data input
<ol>
<li>Full_potential.V9.csv</li>
<li>participation_rates_country.csv</li>
<li>P_inc_SH.csv</li>
<li>P_inc_WH.csv</li>
<li>P_inc_HP.csv</li>
<li>P_inc_DW.csv</li>
<li>P_inc_WM.csv</li>
<li>P_inc_TD.csv</li>
</ol>
</li>
<li>Script
<ol>
<li>NUTS2_acheivable.R</li>
</ol>
</li>
</ol>
</li>
<li>COP_.1deg_11-21_V1.csv
<ol>
<li>Description
<ol>
<li>NUTS2 average coefficient of performance estimates from 2011-2021 daily temperature</li>
</ol>
</li>
<li>Data
<ol>
<li>tg_ens_mean_0.1deg_reg_2011-2021_v24.0e.nc</li>
<li>NUTS_RG_01M_2021_3857.shp</li>
<li>nhhV2.csv</li>
</ol>
</li>
<li>Script
<ol>
<li>COP_from_E-OBS.R</li>
</ol>
</li>
</ol>
</li>
<li>Country dd projections.csv
<ol>
<li>Description
<ol>
<li>Assumptions for annual change in CDD and HDD</li>
<li>Spinoni, J., Vogt, J. V., Barbosa, P., Dosio, A., McCormick, N., Bigano, A., & Füssel, H. M. (2018). Changes of heating and cooling degree‐days in Europe from 1981 to 2100. International Journal of Climatology, 38, e191-e208.</li>
<li>Expectations for future HDD and CDD used the long-run averages and country level expected changes in the rcp45 scenario</li>
</ol>
</li>
</ol>
</li>
<li>EV NUTS projectionsV5.csv
<ol>
<li>Description
<ol>
<li>NUTS2 level EV projections 2018-2050</li>
</ol>
</li>
<li>Data input
<ol>
<li>EV projectionsV5_ave.csv
<ol>
<li>Country level EV projections</li>
</ol>
</li>
<li>NUTS 2 regional share of national vehicle fleet
<ol>
<li>Eurostat - Vehicle Nuts.xlsx</li>
</ol>
</li>
</ol>
</li>
<li>Script
<ol>
<li>EVprojections_NUTS_V5.py</li>
</ol>
</li>
</ol>
</li>
<li>EV_NVF_EV_path.xlsx
<ol>
<li>Description
<ol>
<li>Country level – EV share of new passenger vehicle fleet</li>
<li>From: Mathieu, L., & Poliscanova, J. (2020). Mission (almost) accomplished. <em>Carmakers’ Race to Meet the</em>, <em>21</em>.</li>
</ol>
</li>
</ol>
</li>
<li>EV_parameters.xlsx
<ol>
<li>Description
<ol>
<li>Parameters used to calculate future loads from EVs</li>
<li>Wunit_EV – represents annual kWh per EV</li>
<li>evLIFE_150kkm
<ol>
<li>number of years</li>
<li>represents usable life if EV only lasted 150 thousand km. Hence, 150,000/average km traveled per year with respect to country (this variable is dropped and not used for estimation).</li>
</ol>
</li>
<li>Average age/#years assuming 150k life – represents
<ol>
<li>Number of years</li>
<li>Average between evLIFE_150kkm and average age of vehicle with respect to the country</li>
</ol>
</li>
</ol>
</li>
</ol>
</li>
<li>full_potentialV9.csv
<ol>
<li>Description
<ol>
<li>Final data that shows hourly demand (Maximum Reduction) and (Maximum Dispatch for each device, region, and year.
<ol>
<li>This data has not been adjusted with participation_rates_country.csv</li>
<li>Maximum dispatch is equal to max capacity – hourly demand with respect to the device, region, year, and hour.</li>
</ol>
</li>
</ol>
</li>
<li>Script
<ol>
<li>Full_potentialV9.py</li>
</ol>
</li>
</ol>
</li>
<li>gils projection assumptions.xlsx
<ol>
<li>Description
<ol>
<li>Data from: Gils, H. C. (2015). Balancing of intermittent renewable power generation by demand response and thermal energy storage.</li>
<li>A linear extrapolation was used to determine values for every year and country 2020-2050. AC – Air Conditioning, SH – Storage Heater, WH – Water heater with storage capability, CP – heat circulation pump, TD – Tumble Drier, WM – Washing Machine, DW -Dish Washer, FR – Freezer, RF – Refrigerator. The results are in the files shown below.
<ol>
<li>nflh – full load hours
<ol>
<li>nflh_ac.csv</li>
<li>nflh_cp.csv</li>
</ol>
</li>
<li>wunit – annual energy consumption
<ol>
<li>Wunit_rf_fr.csv</li>
</ol>
</li>
<li>Pcycle – power demand per cycle
<ol>
<li>Pcycle_wm.csv</li>
<li>Pcycle_dw.csv</li>
<li>Pcycle_td.csv</li>
</ol>
</li>
<li>Punit – power damand for device
<ol>
<li>Punit_ac.csv</li>
<li>Punit_cp.csv</li>
</ol>
</li>
<li>r – country level household ownership rates of residential device
<ol>
<li>rfr.csv</li>
<li>rrf.csv</li>
<li>rwm.csv</li>
<li>rtd.csv</li>
<li>rdw.csv</li>
<li>rac.csv</li>
<li>rwh.csv</li>
<li>rcp.csv</li>
<li>rsh.csv</li>
</ol>
</li>
<li>Script
<ol>
<li>openENTRANCE projections.py</li>
</ol>
</li>
</ol>
</li>
</ol>
</li>
</ol>
</li>
<li>heat_pump_hourly_share.csv
<ol>
<li>Description
<ol>
<li>Hours share of daily energy demand</li>
<li>From ENTROS TYNDP – Charts and Figures
<ol>
<li><a href="https://2020.entsos-tyndp-scenarios.eu/download-data/#download">https://2020.entsos-tyndp-scenarios.eu/download-data/#download</a></li>
</ol>
</li>
</ol>
</li>
</ol>
</li>
<li>hourlyEVshares.csv
<ol>
<li>Description
<ol>
<li>Hours share of daily energy demand</li>
<li>From My Electric Avenue Study
<ol>
<li><a href="https://eatechnology.com/consultancy-insights/my-electric-avenue/">https://eatechnology.com/consultancy-insights/my-electric-avenue/</a></li>
</ol>
</li>
</ol>
</li>
</ol>
</li>
<li>HP_transitionV2.csv
<ol>
<li>Description
<ol>
<li>Used to create Qhp_thermal_MWh_projectedV2.csv</li>
<li>Final_energy_15-19
<ol>
<li>Average final energy demand for the residential heating sector between 2015-2019</li>
</ol>
</li>
<li>Final_energy_15-19_nonEE
<ol>
<li>Average final energy demand for the residential heating sector for energy sources that are not energy efficient between 2015-2019 (see paper for sources)</li>
</ol>
</li>
<li>Final_energy_15-19_nonEE_share
<ol>
<li>share of inefficient heating sources</li>
</ol>
</li>
<li>HP_thermal_2018
<ol>
<li>Thermal energy provided by residential heat pumps in 2018</li>
</ol>
</li>
<li>HP_thermal_2019
<ol>
<li>Thermal energy provided by residential heat pumps in 2019</li>
</ol>
</li>
<li>See publication for data sources</li>
</ol>
</li>
</ol>
</li>
<li>Nflh_ac.csv, nflh_cp.csv
<ol>
<li>See gils projection assumptions.xlsx</li>
</ol>
</li>
<li>nhhV2
<ol>
<li>Description
<ol>
<li>Expected number of households for NUTS2 regions for 2020-2050</li>
<li>See publication for data sources</li>
</ol>
</li>
<li>Script
<ol>
<li>EUROSTAT_POP2NUTSV2.R</li>
</ol>
</li>
</ol>
</li>
<li>NUTS0_thermal_heat_annum.csv
<ol>
<li>Description
<ol>
<li>Country level residential annual thermal heat requirements in kWh</li>
<li>Used to determine maximum dispatch in openENTRANCE final V14.py</li>
<li>Mantzos, L., Wiesenthal, T., Matei, N. A., Tchung-Ming, S., Rozsai, M., Russ, P., & Ramirez, A. S. (2017). <em>JRC-IDEES: Integrated Database of the European Energy Sector: Methodological Note</em> (No. JRC108244). Joint Research Centre (Seville site).</li>
</ol>
</li>
</ol>
</li>
<li>p_ACV8.csv, p_CPV6.csv, p_DWV6.csv, p_EVV7.csv, p_FRV5.csv, p_HPV4.csv, p_RFV5.csv, p_SHV7.csv, p_TDV6.csv, p_WHV7.csv, p_WMV6.csv
<ol>
<li>Description
<ol>
<li>Maximum capacity – load for a device can never exceed maximum capacity</li>
</ol>
</li>
<li>Data
<ol>
<li>gils projection assumptions.xlsx</li>
</ol>
</li>
<li>Script
<ol>
<li>openENTRANCE final V14.py</li>
</ol>
</li>
</ol>
</li>
<li>P_inc_DW.csv, P_inc_HP.csv, P_inc_SH.csv, P_inc_TD.csv, P_inc_WH.csv, P_inc_WM.csv, SAMPLE_PINC.csv
<ol>
<li>Description
<ol>
<li>Unadjusted average hourly potential for increase by NUTS2 region for 2018-2050</li>
</ol>
</li>
<li>Data
<ol>
<li>d_ACV8.csv, d_CPV6.csv, d_DWV6.csv, d_EVV7.csv, d_FRV5.csv, d_HPV4.csv, d_RFV5.csv, d_SHV7.csv, d_TDV6.csv, d_WHV7.csv, d_WMV6.csv
<ol>
<li>Theoretical maximum reduction / load of the respective device</li>
</ol>
</li>
<li>p_ACV8.csv, p_CPV6.csv, p_DWV6.csv, p_EVV7.csv, p_FRV5.csv, p_HPV4.csv, p_RFV5.csv, p_SHV7.csv, p_TDV6.csv, p_WHV7.csv, p_WMV6.csv
<ol>
<li>Maximum capacity</li>
</ol>
</li>
</ol>
</li>
<li>Script
<ol>
<li>P_increaseV2.py</li>
</ol>
</li>
</ol>
</li>
<li>Pcycle_dw.csv, Pcycle_td.csv, Pcycle_wm.csv
<ol>
<li>Description
<ol>
<li>power demand per cycle kWh</li>
<li>See gils projection assumptions.xlsx</li>
</ol>
</li>
</ol>
</li>
<li>Punit_ac.csv, Punit_cp.csv
<ol>
<li>Description
<ol>
<li>Unit capacities kWh</li>
<li>See gils projection assumptions.xlsx</li>
</ol>
</li>
</ol>
</li>
<li>Qhp_thermal_MWh_projectedV2.csv
<ol>
<li>Description
<ol>
<li>NUTS2 expectations for thermal energy demand met by heat pumps for 2022-2050</li>
<li>Assumes a linear decomposition of non-renewable and non-energy efficient heating sources until 2050</li>
</ol>
</li>
<li>Data
<ol>
<li>HP_transitionV2.csv</li>
<li>nhhV2.csv</li>
</ol>
</li>
<li>Script
<ol>
<li>HP_projection_nuts.py</li>
</ol>
</li>
</ol>
</li>
<li>rac.csv, rcp.csv, rdw.csv, rfr.csv, rrf.csv, rsh.csv, rtd.csv, rwh.csv, rwm.csv
<ol>
<li>Description
<ol>
<li>Household ownership rates</li>
<li>See gils projection assumptions.xlsx</li>
</ol>
</li>
</ol>
</li>
<li>s_hdd nutsV3.csv, s_cdd nutsV3.csv, yr_hdd nutsV3.csv, yr_cdd nutsV3.csv
<ol>
<li>Description
<ol>
<li>s_hdd nutsV3.csv and s_cdd nutsV3.csv – months share of total heating and cooling degree days (yr_hdd and yr_cdd respectively)</li>
<li>yr_hdd nutsV3.csv and yr_cdd nutsV3.csv – annual heating and cooling degree days respectively</li>
<li>long run (2011-2021) average NUTS 2 level hdd and cdd</li>
</ol>
</li>
</ol>
</li>
<li>s_wash nuts_V2.csv
<ol>
<li>Description
<ol>
<li>Hours share of daily energy demand for washing machine, tumble drier, and dishwasher</li>
</ol>
</li>
<li>Data
<ol>
<li>stamminger_V2.xlsx</li>
</ol>
</li>
<li>Script
<ol>
<li>S_wash_nuts_V2.py</li>
</ol>
</li>
</ol>
</li>
<li>Stamminger_2009.csv
<ol>
<li>Description
<ol>
<li>Hours share of daily energy demand for water heater – WH, storage heater – SH, air conditioner AC, heat circulation pump – CP</li>
<li>From Stamminger, R. (2009). Synergy potential of smart domestic appliances in renewable energy systems.</li>
</ol>
</li>
</ol>
</li>
<li>Time_index.csv
<ol>
<li>Used to create the appropriate timestamp for representative hours</li>
</ol>
</li>
<li>Wunit_rf_fr.csv
<ol>
<li>Annual energy consumption for refrigeration and freezers</li>
<li>See gils projection assumptions.xlsx</li>
</ol>
</li>
</ol>
https://doi.org/10.5281/zenodo.7186521
oai:zenodo.org:7186521
eng
Zenodo
https://zenodo.org/communities/openentrance
https://zenodo.org/communities/eu
https://doi.org/10.5281/zenodo.7182593
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
direct load control
demand response
electric vehicles
Residential electrical loads
Europe
heat pumps
energy transition
openENTRANCE - Case Study 1 - Residential Demand Response - Data and Scripts
info:eu-repo/semantics/other