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27 results for “Energy markets”
Case study result data set for Energy Economics article "Demystifying market clearing and price setting effects in low-carbon energy systems"
<p>The data set contains country-specific power generation and consumption time series data for the European energy system, including both traditional and new market participants due to cross-sectoral integration.</p> <p>Country codes: ALPHA-3<br> Unit: Megawatt (electric) (interval average values, i.e. MWh/h)</p> <p><strong>Generation technology types</strong></p> <ul> <li>batteryStorage (Li-Ion)</li> <li>conventionalHydro (aggregated for different equivalent hydropower systems)</li> <li>natural_gas_CC_COND (Combined Cycle Gas Turbine)</li> <li>natural_gas_CC_EXCOND (Combined Cycle Gas Turbine as extraction condensing CHP plant for district heating)</li> <li>natural_gas_GT_COND (Open-Cycle Gas Turbine)</li> <li>natural_gas_GT_EXCOND (Open-Cycle Gas Turbine as extraction condensing CHP plant for industry)</li> <li>offshoreWind (aggregated for different LCOE and IEC wind turbine classes)</li> <li>offshoreWindExplicit (offshore wind generation considered for offshore grid investments in the North Seas area, aggregated for different LCOE classes)</li> <li>onshoreWind (solar PV, aggregated for different LCOE classes)</li> <li>other (geothermal, waste)</li> <li>pumpedHydro (aggregated for different equivalent hydropower systems)</li> <li>solar (solar PV, aggregated for different LCOE classes)</li> <li>uran_ST_COND (steam turbine condensing power plant)</li> </ul> <p><strong>Consumption technology types</strong></p> <ul> <li>BEV (Battery Electric Vehicles, aggregated for different market segments)</li> <li>PHEV (Battery Electric Vehicles, aggregated for different market segments)</li> <li>airConditioning</li> <li>batteryStorage (Li-Ion)</li> <li>conventionalLoad</li> <li>heatPump (aggregated for different combinations of building, e.g. residential and non-residential, and technology, e.g. air-source, ground-source, types)</li> <li>hybridHeatPump (aggregated for different combinations of building, e.g. residential and non-residential, and technology, e.g. air-source, ground-source, types)</li> <li>hybridTruck (Hybrid Overhead-Line truck)</li> <li>largeScaleDirectResistiveHeating (Centralised CHP systems)</li> <li>natural_gas_CC_EXCOND_electrodeHeater</li> <li>natural_gas_CC_EXCOND_heatpumpHeater</li> <li>natural_gas_GT_EXCOND_electrodeHeater</li> <li>natural_gas_GT_EXCOND_heatpumpHeater</li> <li>powerToGas</li> <li>pumpedHydro (aggregated for different equivalent hydropower systems)</li> </ul>
Historical Annual Revenue of Energy Storage on European Electricity Markets
<p>This dataset provides the optimized annual revenue for 96 generic storage technologies (12 efficiency x 8 discharge duration). It covers 17 European electricity markets for up to 16 years (Austria, AT; Belgium, BE; Switzerland, CH; Czech Republic, CZ; German, DE; Spain, ES; France, FR; Italy, IT; Ireland, IR; Netherlands, NL; Nordpool which includes Denmark, Estonia, Finland, Latvia, Lithuania, Norway, Sweden, NP; Poland, PL; Portugal, PT; Romania, RO; Slovakia, SK; United Kingdom, UK). The optimization is an adaptation of the model presented in Gaudard et al. [2013]. It assumes perfect foresight assumption and a stochastic algorithm. Therefore, the results are an approximation of the maximum rather than the absolute optimum. Further information is provided in "Gaudard L. and Madani K., Energy storage race: Has the monopoly of pumped-storage in Europe come to an end?, forthcoming". </p> <p>The following information is provided:</p> <p>Country: Code of the specific market (also the filename)</p> <p>Currency: The currency in which the results are expressed</p> <p>Discharge duration [hours]: The time required to empty at full nominal power a device that is fully charged. </p> <p>Efficiency: Ratio between the amount of discharged and charged energy during a full cycle.</p> <p>Year: From January 1st to December 31st.</p> <p>The given numbers are in euros or GBP per year and normalized to 1kWh of energy storage. This means that for a specific device, the given figures must be multiplied by the volume of energy storage (in terms of kWh). As an example, for an energy device with the efficiency of 0.95, discharge duration of 6h and volume of energy storage of 2000kWh, the revenues in 2003 in Austria would be 9.26 x 2000=18520 euros. </p> <p>For any questions or further requirements, please feel free to get in touch with the authors.</p>
Dataset for "An alternative to market-oriented energy models: nexus patterns across hierarchical levels"
<p>Dataset used for the publication "Di Felice, Louisa Jane, Maddalena Ripa, and Mario Giampietro. "An alternative to market-oriented energy models: Nexus patterns across hierarchical levels." <em>Energy Policy</em> 126 (2019): 431-443.". The dataset follows the distinction across hierarchical levels as specified in the publication.</p> <p>The same dataset was also used for a case study developed for the MAGIC project, available <a href="http://magic-nexus.eu/case_study/electric-grid-catalonia-illustrations-musiasem">here</a>. </p>
Case study result data set for Energy Economics (submitted) article "On Wholesale Electricity Prices and Market Values in a Carbon-Neutral Energy System"
<p>The data set contains wholesale power price time series data for Germany and France focussing on price setting effects in a long term low carbon European energy system context (scenario year 2050) generated with the model SCOPE SD of Fraunhofer Institute for Energy Economics and Energy System Technology IEE. The single time series are focussing on the price setting effects of different flexible technologies including both traditional and new market participants due to cross-sectoral integration.</p> <p>Unit: Euro/Megawatthour</p> <p><strong>Abbreviations:</strong></p> <ul> <li>BEV - Battery Electric Vehicles</li> <li>GER - Germany</li> <li>FRA - France</li> <li>OCGT - Open Cycle Gas Turbine</li> <li>PHEV - Plug-In Hybrid Vehicles</li> <li>RES - Renewable energy sources (here: wind and solar power)</li> <li>th. - thermal</li> </ul>
Intelligent Energy Systems Ontology: Local flexibility market and power system co-simulation demonstration
<p>The Intelligent Energy Systems Ontology (IESO) provides semantic interoperability within a society of multi-agent systems (MAS) developed in the scope of power and energy systems (PES). It leverages the knowledge from existing and publicly available semantic models developed for specific PES subdomains to accomplish a shared vocabulary among the agents of the MAS community, overcoming heterogeneity among the reused ontologies. IESO provides agents with semantic reasoning, constraints validation, and data uniformization. The use of IESO is demonstrated through the simulation of the management of a rural distribution network, considering the validation of the grid’s technical constraints. This dataset publishes files demonstrating: i) a snapshot of the initial semantic knowledge base (KB); ii) queries to the KB to get services inputs; iii) conversions between syntactic and semantic models; <br> iv) constraints validations; v) automatic conversion of units of measure.</p>
Supplementary Data: A peer-to-peer market mechanism incorporating multi-energy coupling and cooperative behaviors
<p>This dataset is the supplementary dataset for the case study used in the journal article (<a href="https://doi-org.tudelft.idm.oclc.org/10.1016/j.apenergy.2022.118572">https://doi.org/10.1016/j.apenergy.2022.118572</a>):</p> <p>A peer-to-peer market mechanism incorporating multi-energy coupling and cooperative behaviors</p> <p> </p> <p>Before using the dataset, please</p> <p>1. refer to the article for the details of the data used in the case study,</p> <p>2. read README.txt for the structure of the dataset.</p> <p> </p> <p>Please also kindly cite the journal article when using the dataset.</p>
CROSSBOW HLU2-UC6-TC1 Economical benefit of mFRR market participation with 5% of energy saved for upward regulation
<p>The energy market participation algorithm of RES-CC allows for participating in DA/ID markets and balancing markets. The process uses the energy generation forecasting of the plants to generate the energy bids for the DA/ID market. With regards to the mFRR market, the whole amount of curtailable energy is offered for downward regulation. Additionally, the process is configurable so that it can save a given amount of the potential generation, do not offering this energy in the DA/ID market but offering it for upward regulation in the mFRR. This upward regulation can only be done with Renewables by means of not selling part of the forecasted energy. This is a risk, as the upward regulation energy might not be requested by the operator, and thus the profit for the generation will be lost.</p> <p>This dataset contains the results of the market participation algorithm running for a week, configured for offering 5% of the forecasted energy for upward regulation.</p> <p>Dataset contains the results for the CROSSBOW portfolios of Croatia, Bulgaria, Romania and Greece.</p> <p>For each country, hour by hour, the following information is provided:</p> <ul> <li>Energy sold in IDM</li> <li>Forecasted generation</li> <li>Energy sold mFRR down</li> <li>Energy sold mFRR up</li> <li>Energy price mFRR down</li> <li>Energy price mFRR up</li> <li>Energy price in IDM</li> <li>Energy looses (saved and not sold in mFRR up)</li> </ul>
CROSSBOW HLU2-UC6-TC1 Economical benefit of mFRR market participation with 20% of energy saved for upward regulation
<p>The energy market participation algorithm of RES-CC allows for participating in DA/ID markets and balancing markets. The process uses the energy generation forecasting of the plants to generate the energy bids for the DA/ID market. With regards to the mFRR market, the whole amount of curtailable energy is offered for downward regulation. Additionally, the process is configurable so that it can save a given amount of the potential generation, do not offering this energy in the DA/ID market but offering it for upward regulation in the mFRR. This upward regulation can only be done with Renewables by means of not selling part of the forecasted energy. This is a risk, as the upward regulation energy might not be requested by the operator, and thus the profit for the generation will be lost.</p> <p>This dataset contains the results of the market participation algorithm running for a week, configured for offering 20% of the forecasted energy for upward regulation.</p> <p>Dataset contains the results for the CROSSBOW portfolios of Croatia, Bulgaria, Romania and Greece.</p> <p>For each country, hour by hour, the following information is provided:</p> <ul> <li>Energy sold in IDM</li> <li>Forecasted generation</li> <li>Energy sold mFRR down</li> <li>Energy sold mFRR up</li> <li>Energy price mFRR down</li> <li>Energy price mFRR up</li> <li>Energy price in IDM</li> <li>Energy looses (saved and not sold in mFRR up)</li> </ul>
Modelling assumptions and input dataset for the case study of the paper "Societal Effects of Large-Scale Energy Storage in the Current and Future Day-Ahead Market: A Belgian Case Study"
<p>This data package includes the modelling assumptions and input data to replicate the results of the case study included in the paper "Societal Effects of Large-Scale Energy Storage in the Current and Future Day-Ahead Market: A Belgian Case Study". This paper is part of the 18th International Conference on the European Energy Market (EEM22).</p> <p>The case study models the Belgian day-ahead electricity market, in which the existing storage is considered, in addition to large-scale battery energy storage systems of different sizes for varying renewable energy shares. A detailed description of the case study is provided in the readme file. </p> <p>This supplementary data package includes the following files: </p> <p> --Belgium Model Input Data.xlsx: Dataset used as input in the case study of the mentioned paper<br> --Modelling Assumptions.pdf: Modelling assumptions considered in the case study<br> --readme.txt (this file): Includes a detailed description of the data package</p> <p> </p> <p>The data included in this dataset was collected from public open sources [1]-[2]. Please notice that this dataset does not replace the original open access information. For accessing the data, please visit the following websites:</p> <p>[1] “ENTSO-E Transparency Platform.” [Online]. Available: https://transparency.entsoe.eu/dashboard/show. [Accessed: 06-Jul-2022].<br> [2] “Grid data.” [Online]. Available: https://www.elia.be/en/grid-data. [Accessed: 06-Jul-2022].</p> <p><br> </p> <p> </p>
Safeguarding the energy transition against political backlash to carbon markets - figure raw data
<p>Figure raw data for the article "Safeguarding the energy transition against political backlash to carbon markets" (published in Nature Energy).</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>
Energy Market Transformation Survey
<p>This report presents a survey performed with stakeholders and end-users under the scope of PARITY project. The survey explores the overall patterns that will form the energy market transformation and the consumer's perspective. The survey aims to evaluate participants’ opinions on the concepts of LEM and LFM, to identify factors and barriers to the progress of local renewable energy development based on the participants’ knowledge and what would motivate people to participate in such markets. Additionally, the goal of this survey is to analyse the current market needs, as well as investigate what is driving the energy markets transformation and where it is leading.</p>
Exploring the Effects of Local Energy Markets on Electricity Retailers and Customers
<p>PSCC - Data Source</p> <p>Paper title: Exploring the Effects of Local Energy Markets on Electricity Retailers and Customers</p> <p>1. Wholesale price,</p> <p>2. Linear and quadratic benefit coefficients of flexible consumers, </p> <p>3. Linear and quadratic cost coefficients of micro-generators,</p> <p>4. Power capacity, minimum & maximum energy limits, initial energy level, charging & discharging efficiency of energy storages.</p>
Supplementary Information of Multistage Provincial Power Expansion Planning and Market Design toward Energy Transition: A Case Study of Xinjiang in China
<p>Supplementary Information of Multistage Provincial Power Expansion Planning and Market Design toward Energy Transition: A Case Study of Xinjiang in China</p>
Replication package for the paper "Modeling Europe's role in the global LNG market 2040: balancing decarbonization goals, energy security, and geopolitical tensions"
<p>This package contains folders and files with code and data used in the study described in the paper. Further information can be found on <a href="https://github.com/sebastianzwickl/lng-trade-europe" target="_blank" rel="noopener">GitHub</a>.</p>
IEEE-30 energy system data of multi-period market with intertemporal constraints
<p>This is the dataset that is used for the original article: "Locational marginal pricing in multi-period AC OPF environment"</p> <p>The dataset consists of the following files</p> <ul> <li>Case1.zip</li> <li>Case2.zip</li> <li>Case3.zip</li> <li>case_modifications.py</li> <li>data_spec.py</li> <li>OPF_formulation.pdf</li> </ul> <p>Multiperiod AC OPF is given in OPF_formulation.pdf. Modifications of traditional IEEE 30-node case are given in case_modifications.py</p> <p>The case files incorporate input and output multiperiod AC OPF and LMP decomposition data in csv and pickle formats. Data structure of case files is given in data_spec.py.</p> <p>For python users pickle files are given. Nevertheless, python environment is not required. Specification can be read as a text file. All necessary data are repeated in csv format.</p> <p>Step 1 are to define LMPs of limited energy resources or storage resources that are formed by actual marginal resources from all time periods (first LMP definition in fig. 6 in the paper).</p> <p>Step 2 are to define all other LMPs at price-taking nodes (second LMP definition in fig. 6 in the paper).</p> <p>The following interrelation between Lagrange multipliers, LMP components, and price-bonding factors holds true:</p> <pre>assert np.max(np.abs(output_ramp.sensitivities.dot(output_ramp.offer_gen_data.price).tolist() - output_ramp.ramping_gen_data.price)) < 1e-2 if output_pt_step1.components.shape[0]: step1_pf_filter = (~output_pf.is_limited_energy) & (~output_pf.is_storage) assert np.max(np.abs(output_pt_step1.components.node_price - (output_pt_step1.components.f + output_pt_step1.components.tc_sum + output_pt_step1.components.vc_sum))) < 1e-2 assert (output_pt_step1.components.f - output_pt_step1.w_f.dot(output_pf.node_price[step1_pf_filter])).abs().max() < 1e-2 assert (output_pt_step1.components.tc_sum - pd.concat( (w.dot(output_pf.offer_price[step1_pf_filter]) for w in output_pt_step1.w_tc_list), axis=1 ).sum(axis=1)).abs().max() < 1e-2 assert np.max(np.abs(output_pt_step2.components.node_price - (output_pt_step2.components.f + output_pt_step2.components.tc_sum + output_pt_step2.components.vc_sum))) < 1e-2 assert (output_pt_step2.components.f - output_pt_step2.w_f.dot(output_pf.node_price)).abs().max() < 1e-2 assert (output_pt_step2.components.tc_sum - pd.concat( (w.dot(output_pf.offer_price) for w in output_pt_step2.w_tc_list), axis=1 ).sum(axis=1) ).abs().max() < 1e-2</pre> <p> </p>
Data for National carbon footprint estimations of an electrified internet of energy with circular economy under future EV market share prediction in China
<p>This dataset is created for National carbon footprint estimations of an electrified internet of energy with circular economy under future EV market share prediction in China.</p> <p>The dataset includes the energy demand for buildings of each province in China, the Centralized and Distributed PV-battery system design of each province in China, The EV and ICEV carbon emission comparison of each province in China, the electricity price in China, The Carbon footprint and NPV calculation of current and future building-transportation system in China.</p>
Dataset for "Energy markets and allocative efficiency in an energy transition context" paper
<p>This upload contains the dataset used on the paper "Energy markets and allocative efficiency in an energy transition context" analysis.</p>
Electronic Companion -- Optimal Management of Distribution-Connected Assets Operating under Carbon and Energy Day-Ahead Markets
<p>This release is associated with a paper entitled "Optimal Management of Distribution-Connected Assets Operating under Carbon and Energy Day-Ahead Markets".</p> <p>In this document, we provide information regarding the transmission and distribution power systems adopted to present the results shown in the paper. These systems are modified versions of the IEEE <a href="https://icseg.iti.illinois.edu/ieee-14-bus-system/">14-bus</a> and the IEEE <a href="https://cmte.ieee.org/pes-testfeeders/resources/">34-bus</a>, respectively. Five dispatchable generators were added to the distribution system and 13 load shapes were considered. The data regarding the costs and physical parameters of each of the power system's elements are provided in this document.</p>
Supplementary files to the paper "Optimal Offering of Energy Storage in Electricity Markets with Loop Blocks"
<p>These files include the retrieved optimal offering decisions, corresponding to the case studies of Section IV.C of the paper Optimal Offering of Energy Storage in Electricity Markets with Loop Blocks. A README.txt file is included, describing the format of all other files.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.