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7 results for “renewable power”
The global renewable power support policy dataset
<p>The global renewable power support policy dataset was compiled by Sarah Hafner (Anglia Ruskin University, United Kingdom) and Johan Lilliestam (Institute for Advanced Sustainability Studies (IASS), Germany) in February-July 2017 and completed during 2017. The work was led by Johan Lilliestam but each author gathered half of the data. The data was formatted and checked for internal consistency by Tim Tröndle, IASS.</p> <p>All non-commercial users are allowed to use and manipulate our data, but are required to give appropriate attribution. Hence, <strong>please cite this data as</strong>:</p> <p>Hafner, S. & Lilliestam, J. (2019): <em>The global renewable power support dataset</em>. Institute for Advanced Sustainability Studies (IASS) & Anglia Ruskin University, Potsdam & Cambridge. Doi: https://doi.org/ 10.5281/zenodo.3371375.</p> <p><strong>If you are interested in contributing</strong> to and further developing the dataset: please contact Johan Lilliestam (IASS Potsdam).</p> <p>The search was done in publically available sources, including but not limited to the IEA renewables policy database, res-legal.eu, Worldbank data, as well as data from the responsible national ministries.</p> <p>Our data holds information on 10 specific policy instruments explicitly dedicated to the support for expansion of renewable electricity generation 1990-2016; some instruments, including taxation of non-renewables or emission trading, affect other sectors than renewable power, but are mentioned in their original policy description to also be dedicated to increasing renewable power. Our data concerns national policy measures, but ignores policies enacted on higher (e.g. EU-level in Europe) or lower (e.g. state-level policies in Canada, USA) political levels. For example, the “no support” entry for the United Arab Emirates indicates that there were no national-level policies: all policies were, in this case, emirate-specific.</p> <p>The data exists in two versions: one version readable for humans (RE_policies_fullglobal.xlsx) and for each instrument type as .csv. The information in the two versions is identical and differs only in the way it is displayed.</p> <p><strong>Please refer to the metadata file for a detailed description of the dataset and the data categories.</strong></p>
Supplementary material to the manuscript: Regionalised Heat Demand and Power-To-Heat Capacities in Germany - An Open Data Set for Assessing Renewable Energy Integration
<p>This is the supplementary material for the manuscript:</p> <p>"Regionalised Heat Demand and Power-To-Heat Capacities in Germany - an Open Data Set for Assessing Renewable Energy Integration"</p> <p>Article DOI: <a href="https://doi.org/10.1016/j.apenergy.2019.114161">https://doi.org/10.1016/j.apenergy.2019.114161</a></p> <p>Open access preprint: <a href="https://arxiv.org/abs/1912.03763">https://arxiv.org/abs/1912.03763</a></p> <p> </p> <p><strong>DESCRIPTION OF THE DATASET AND LICENSES:</strong></p> <p>The subdirectory "04_results" contains the regionalised heat demand an power-to-heat capacity data on administrative district level (NUTS-3) for Germany. The subdirectories "01_census_special_evaluation_data" and "02_other_input_data" contain the utilised input data. The subdirectory "03_code" contains the developed and applied source code.</p> <p>The data in this repository are provided under open source licenses. For license information and other general information on the supplementary material, refer to the LICENSE files and README files in the respective subdirectories.</p> <p>For a detailed description of the approach developed by the author, the input data used and the generated results, refer to the manuscript "Regionalised Heat Demand and Power-To-Heat Capacities in Germany - an Open Data Set for Assessing Renewable Energy Integration".</p> <p><strong>METADATA:</strong></p> <p>Sector: Residential Buildings – Space Heating and Domestic Hot Water</p> <p>Geographical scope: Germany</p> <p>Geographical resolution: Administrative districts (NUTS-3)</p> <p>Temporal scope: 2011, three scenarios for 2030</p> <p>Temporal resolution: 15min</p> <p> </p> <p><strong>UNITS:</strong></p> <p>In the final results folders (04_results/01_installed_heating_p2h_capacity; 04_results/02_daily_time_series; 04_results/03_yearly_time_series) the units of the data are indicated in the file names or the column names, e.g. by "in_MW". In case of unit indication in the file name, the unit refers to all columns in the file.</p> <p>In the intermediate results folder (04_results/00_sql_tables_exported_to_csv) all units referring to power are "kW" and all units referring to energy are "kWh".</p> <p><strong>NEWS AND CONTACT:</strong></p> <p>This dataset will be used as part of the <a href="https://wiki.openmod-initiative.org/wiki/Region4FLEX">region4FLEX model</a>. We are currently enhancing the data by temporally and spatially resolved COP time series and determining load shifting potentials. If you wish to receive news or have general questions please contact: wilko.heitkoetter@dlr.de. </p>
Data for: "Market Power and Price Exposure: Learning from Changes in Renewable Energy Regulation"
<p>Given the key role of renewable energies in current and future electricity markets, it is important to understand how they affect firms' pricing incentives in these markets. In this paper, we study whether renewables depress electricity market prices, and how this effect depends on their degree of market price exposure. Our theoretical analysis shows that paying renewables with fixed prices, rather than with market-based prices, is relatively more effective at curbing market power when the dominant electricity firms own large shares of the renewable capacity, and <em>vice-versa</em>. To test this prediction, our empirical analysis leverages several short-lived changes to renewable energy pricing mechanisms in the Spanish electricity market. In this context, we find that the switch from full price exposure to fixed prices caused a 2-4% reduction in the average price-cost markup.</p>
Dataset: Harmonized and Open Energy Dataset for Modeling a Highly Renewable Brazilian Power System
<p>The dataset provided here is intended for publication - Harmonized and Open Energy Dataset for Modeling a Highly Renewable Brazilian Power System.</p> <p>Direct use of our provided datasets is available from Zenodo, and the source code to generate the datasets is published in <a href="https://gitlab.com/dlr-ve/esy/open-brazilian-energy-data">Gitlab</a>. We describe the data collection process in detail and open source the code for data processing and analysis in our publication.</p> <p><br> The assembled dataset includes the following subcategories, as detailed in the methods section of our publication: i) geospatial data for Brazil, ii) aggregated grid network topology, iii) vRES potentials --- profile and installable generation capacity, iv) geographically installable capacity of biomass thermal plants, v) hydropower plants inflow, vi) existing and planned power generators with their capacity, vii) electricity load profile, viii) scenarios of sectoral energy demand and ix) cross-border electricity exchanges. This dataset is resolved geographically by Brazilian federal states, and time series data are resolved by hours, spanning 2012-2020.</p> <p>The dataset can be used as input to popular open energy system models such as PyPSA and any other modelling framework.</p> <p>We encourage you to contribute to improving the datasets.</p>
Dataset on PowerWorld Software Power Flow Calculations on an Underground Distribution Feeder for Inserting Renewable Distribution Generation from Biogas, Photovoltaic and Small Wind Sources
<p>This Dataset brings all the information, details and source files used for power flow studies of the USP-105 underground feeder of the distribution medium voltage network in the University of São Paulo campus, which has received several embedded DG sources, namely a biogas plant, photovoltaic units and a small wind turbine.</p> <p>The power flow simulations were realized using the PowerWorldTM Simulator, v.23</p> <p>The files types on the Dataset are: </p> <p>.pwb, .pwd and tsb: Powerworld software input files for the simulations</p> <p>.csv: where a semicolon symbol (;) is used as a column separator, while a dot symbol (.) represents the decimal separator. The first row of each CSV file corresponds to the header row to help identify data.</p>
Importance of storage in renewable power systems
<p>This figure illustrates the renewable energy production from run-of-river hydro, wind, and photovoltaic (PV) sources, alongside the daily electricity demand curve. It highlights that during midday, the combined renewable energy production exceeds the demand, creating an opportunity to charge energy storage systems. Conversely, during the night, morning, and evening hours, renewable production falls short of meeting the demand. During these periods, the stored energy should be discharged to ensure a stable and reliable power supply. This emphasizes the critical role of energy storage in balancing supply and demand in renewable power systems.</p>
Dataset from Denmark for Modeling a Highly Renewable Power System
<p>This is a completed database of Danish electrical transmission system for modeling an electrical system with high penetration of renewable energy.</p>
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