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8 results for “tariff”
2023 Portuguese Energy Tariffs: Retail and Feed-In Tariff Data
<p><strong>Introduction</strong></p> <p>This dataset contains historic data for 20 electric grid consumption and energy injection tariffs in Portugal, during 2023. The data includes both retail tariffs for grid consumption energy and feed-in income tariffs for energy injected back into the grid.</p> <p> </p> <p><strong>Data Overview</strong></p> <p>The dataset comprises the following key elements:</p> <p> 1. Grid Consumption (Retail Tariffs):</p> <ul> <li>Retail tariffs refer to the cost per unit of electricity consumed from the grid.</li> <li>Tariff values are given in euro (€) per kWh and vary across different retailers and hour schemes.</li> </ul> <p> 2. Energy Injection (Feed-in Tariffs):</p> <ul> <li>Feed-in tariffs represent the compensation per unit of electricity generated by solar systems and fed back into the grid.</li> <li>Tariff values are presented in € per kWh and vary across different retailers and conditions.</li> </ul> <p><br><strong>Assumptions</strong></p> <p><br>The retail tariffs in this dataset refer to the consumed energy only and do not include taxes, contracted power costs, and other fees.</p> <p>The dataset starts on Jan 1st, 2023, and ends on Dec 31st, 2023.</p> <p>The dataset uses a 15min time step for each line. The price per hour is applied to each of the four 15 minutes steps.</p> <p><br><strong>Reference data used to create this dataset:</strong></p> <ul> <li>ERSE Regulatory data simulator - https://simulador.precos.erse.pt/eletricidade/</li> <li>2023 Energy Cost Description - https://www.erse.pt/en/activities/market-regulation/tariffs-and-prices-electricity</li> <li>GALP - https://casa.galp.pt/planos-eletricidade-e-gas</li> <li>Eni Plenitude - https://eniplenitude.pt/eletricidade</li> <li>Repsol - https://www.repsol.pt/particulares/casa/eletricidade-gas/</li> <li>EDP - https://www.edp.pt/empresas/energia/tarifarios/</li> </ul> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications.</p>
Measuring the flexibility achieved by a change of tariff
<ul> <li><strong>Name</strong>: Measuring the flexibility achieved by a change of tariff.</li> <li><strong>Summary</strong>: This dataset contains the results of a survey carried out by the Spanish electricity retailer GoiEner to assess the impact that a change of from a "flat rate" tariff towards a "time of use" tariff have. Two files are provided: <ul> <li>The results of the survey merged with a summary of the energy consumption of the clients before and after the change of tariff.</li> <li>The questions of the survey.</li> </ul> </li> <li><strong>License</strong>: CC-BY-SA</li> <li><strong>Acknowledge</strong>: These data have been collected in the framework of the WHY project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 891943.</li> <li><strong>Disclaimer</strong>: The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the Executive Agency for Small and Medium-sized Enterprises (EASME) or the European Commission (EC). EASME or the EC are not responsible for any use that may be made of the information contained therein.</li> <li><strong>Collection Date</strong>:</li> <li><strong>Publication Date</strong>: December 1, 2022.</li> <li><strong>DOI</strong>: 10.5281/zenodo.7382924</li> <li><strong>Other repositories</strong>: None.</li> <li><strong>Author</strong>: GoiEner, University of Deusto.</li> <li><strong>Objective of collection</strong>: The objective of the data collected is to assess the impact that the change of tariff has on the clients of GoiEner. In particular, the following questions wanted to be answered: <ul> <li>How much energy conservation can trigger a change of tariff?</li> <li>How much time flexibility could be trigger with a Time of Use Tariff?</li> <li>What are the main barriers to change the behavoiur?</li> <li>Are there any differences on behaviour depending on the socio-cultural-psycological profile of the consumers?</li> </ul> </li> <li><strong>Description</strong>: The meaning of each column is described next: <ul> <li><strong>Qx_y</strong>: Answers to the survey. See the survey file attached (in Spanish) for details.</li> <li><strong>X.z</strong>: Answers to question "¿En qué rango de horas se realizan las siguientes acciones en el domicilio?". Sorted from left to right, top to bottom.</li> <li><strong>Idioma</strong>: Languaje used to answer the survey.</li> <li><strong>Desea.dar.su.CU</strong>: data used to de-anonymize the answers.</li> <li><strong>{P,F,V}{19,20,21}</strong>: total energy consumed during the peak, flat and valley period of the day between 6/19 to 5/20 (19), 6/20 to 5/21 (20) and 6/21 to 5/22 (21).</li> <li><strong>T{19,20,21}</strong>: total energy consumed between 6/19 to 5/20 (19), 6/20 to 5/21 (20) and 6/21 to 5/22 (21).</li> <li><strong>kpi2_abs</strong>: T20 - T21</li> <li><strong>kpi2_rel</strong>: kpi2_abs / T20</li> <li><strong>kpi1_{P,F,V}{19,20,21}: </strong>{P,F,V}{19,20,21} / T{19,20,21}</li> <li><strong>kpi1_{P,F,V}diff</strong>: kpi1_{P,F,V}19 - kpi1_{P,F,V}21</li> <li><strong>T20DHS{19,20,21}</strong>: Cost of the energy of during the different periods using the last tariff.</li> <li><strong>T20TD{19,20,21}</strong>: Cost of the energy of during the different periods using the new tariff.</li> <li><strong>POWER_TARGET</strong>: Energy powerty rist indicator (https://powerpoor.eu/sites/default/files/2022-09/POWERPOOR%20D2.2%20POWER%20TARGET%20v1.0.pdf)</li> <li><strong>TotalEnergyBudget</strong>: Self assessment of the energy budget of the house in Euros.</li> <li><strong>Invoices20{20,21,22}</strong>: Amount of all the invoices for the house during the different periods.</li> <li><strong>min30{in,pre,pst}</strong>: Cluster assigned depending on its electric behaviour <em>pre</em>-COVID, dur<em>in</em>g the COVID lockdowns and <em>post</em> COVID lockdowns. See 10.5281/zenodo.7382818 for details.</li> </ul> </li> <li><strong>5 star</strong>: ⭐⭐⭐</li> <li><strong>Preprocessing steps</strong>: Data integration (from different sources from GoiEner services); data transformation (anonymization, unit conversion, metadata generation).</li> <li><strong>Reuse:</strong> This dataset is related to datasets: <ul> <li>"A database of features extracted from different electricity load profiles datasets" (DOI 10.5281/zenodo.7382818), where time series feature extraction has been performed.</li> </ul> </li> <li><strong>Update policy:</strong> There might be a single update in mid-2023 with a repetition of the survey as there have been another change of tariff in Spain.</li> <li><strong>Ethics and legal aspects:</strong> The data provided by GoiEner contained values of the CUPS (Meter Point Administration Number), which are personal data. A pre-processing step has been carried out to replace the CUPS by random 64-character hashes.</li> <li><strong>Technical aspects:</strong> the survey is provided as a PDF file and the data as a CSV file compressed with zstandard.</li> <li><strong>Other:</strong> None.</li> </ul>
German Renewable Energy Tariff Register
<p>The TariffRegister compiles all tariff types paid to renewable energy units in Germany under the RES Act 2000 and its subsequent amendments. This dataset is based on the tariff files reported by the four German Transmission System Operators (TSOs) on their conjoint reporting <a href="https://www.netztransparenz.de/EEG/Verguetungs-und-Umlagekategorien">webpage</a>. It includes over 6500 different tariff types applicable from 2000 to 2023. The file consists of 19 descriptors, the tariff identification code (Tariff_ID), publication year, associated technology, commissioning year of applicable units, unit characterization criteria (Criteria1 and Criteria2), bonus details (Bonus_name and Bonus_start), fuel type (Fuel), tariff values (Tariff), management premium, "Ausfallvergütung" value, “Mieterstromzuschlag”, and date of tariff introduction or change. Additionally, two tariff categorization levels (Category1 and Category2) group tariffs into 29 and 17 respective categories, while the KWK descriptor indicates CHP-biomass plant subsidies and the "Sign" descriptor signifies positive or negative tariffs.</p> <p>Notes:</p> <p>The TariffRegister.csv can be matched with the <a href="https://zenodo.org/record/8010410">PaymentRegister</a> dataset to identify the payment types received by renewable energy units.</p> <p>The provided categorization process reflects the specific research needs of the Chair of Energy Economics at BTU and does not affect the original data, allowing users to choose whether to utilize the categorization descriptors.</p> <p>Users can update the dataset by following the outlined steps:</p> <ol> <li>Update the “Original files” by downloading the latest published data (<a href="http://www.netztransparenz.de/EEG/Verguetungs-und-Umlagekategorien">here</a>)</li> <li>Clean the files from any row that is not including a Tariff_ID</li> <li>Include the categorization descriptors by matching the categories based on the Tariff_ID and update the categories for new tariffs.</li> <li>Paste this information into a CSV file and store it under “Cleaned files” and check that the column names and data types match the information from previous files.</li> <li>Run the provided R code after modifying the top-level directory.</li> </ol> <p>This publication is part of a series of datasets on German Renewable Energy Sources Data, which also includes a <a href="https://zenodo.org/record/7945029">UnitRegister</a>, a <a href="https://zenodo.org/record/8010410">PaymentRegister</a>, and the combination of this information to analyze individual bids in renewable auctions. For a comprehensive understanding of the datasets and their relationships, we recommend referring to two related publications:</p> <ul> <li>Batz Liñeiro, T. B., Müsgens, F. (2023). "Evaluating the German onshore wind auction programme: An analysis based on individual bids." Energy Policy, 172, 113317. <a href="https://doi.org/10.1016/j.enpol.2022.113317">https://doi.org/10.1016/j.enpol.2022.113317</a> | <a href="https://ssrn.com/abstract=4130232">Preprint</a></li> <li>Batz Liñeiro, T., Müsgens, F. (2021). "Evaluating the German PV auction program: The secrets of individual bids revealed." Energy Policy, 159, 112618. <a href="https://doi.org/10.1016/j.enpol.2021.112618">https://doi.org/10.1016/j.enpol.2021.112618</a> | <a href="http://arxiv.org/abs/2104.07536">Preprint</a></li> </ul>
H2020 Platone Greek Demo Variable DUoS Tariff Calculation
<p>Variable DUoS tariff calculation for dynamic nodes of the Greek Demo</p>
Flexibilization or biomethane upgrading? Investment preference of German biogas plant operators for the follow-up of guaranteed feed-in tariffs
<p><span>This dataset reports the results of a discrete choice experiment with 183 German biogas plant operators designed to elicit the respondents' plans for biogas utilization pathways after the end of guaranteed feed-in tariffs. Participants could choose between 'flexibilization' for demand-based electricity generation and conversion to biomethane upgrading for direct feed-in into the natural gas grid. A binomial logit model revealed a 37% probability of switching to biomethane upgrading. These plants are characterized by higher capacities, several involved shareholders, secured succession, costly digestate disposal and belonging to the upper performance quartile. Mixed logit estimations conducted separately for the two investment concepts revealed a very high overall willingness to invest: 71% for flexibilization and 82% for biomethane upgrading. The respondents demand a return on investment of 19% for flexibilization and 26% for biomethane upgrading. Within the flexibilization, twofold overbuilding (installed capacity equals 2 times the rated power) is clearly preferred to fivefold overbuilding. For the biomethane upgrading, private ownership of the upgrading plant is preferred to a joint investment in a central upgrading facility. Limiting the use of energy crops reduces the propensity to invest in both models, while a longer utilization period enhances it. The respondents consider lack of planning reliability as the biggest obstacle to invest, followed by long approval procedures and high investment costs due to restrictive legal requirements.</span></p>
Flexibilization or biomethane upgrading? Investment preference of German biogas plant operators for the follow-up of guaranteed feed-in tariffs
Open the record for dataset details and reuse information.
Data from: Hospital costs of out-of-hospital cardiac arrest patients treated in intensive care; a single centre evaluation using the national tariff-based system
Open the record for dataset details and reuse information.
Supplementary material for the article entitled "Socioeconomic Analysis of the Proposed Opening of the Brazilian energy Market Through the Combination of Forecast Method with the Optimized Tariff Model"
<p>This supplementary material includes the input data used for the simulations conducted in the article.</p>
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