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27 results for “Electricity Markets”
Geospatial Modelling of Australia's National Electricity Market - Dataset
<p>This dataset contains information relating to the topology of Australia's largest electricity transmission network, along with details pertaining to the technical and economic characteristics of generators operating within this grid. Information has been compiled from publicly available datasets released by the Australian Energy Market Operator (AEMO) [1, 2] and Geoscience Australia (GA) [3, 4, 5]. Potential applications include the development of economic dispatch, power-flow, and unit commitment models.</p> <p>The network is comprised of 912 nodes, 1406 AC edges, and three HVDC links. Information regarding forward and reverse power-flow limits for two AC interconnectors is also provided. Latitude and longitude coordinates are given for each node, with the network based off of geospatial datasets obtained from GA [3, 4, 5]. Signals for electricity demand at each node were derived using regional load profiles in combination with population data obtained from the Australian Bureau of Statistics (ABS) [6]. Allocation methods outlined in [7, 8] were used to disaggregate regional load profiles according to the geospatial distribution of Australia's population. Construction of the generator dataset involved compiling information obtained from AEMO's Market Management System Data Model (MMSDM) [1] and National Transmission Network Development Plan (NTNDP) [2] datasets. Historic generator dispatch signals were also obtained from AEMO [1], allowing the output of market models to be compared with realised outcomes.</p> <p>For further information regarding the contents of each csv file please refer to <code>dataset_summary.pdf</code>. Jupyter Notebooks at [9] contain the Python code necessary to reproduce these datasets.</p> <p><strong>Version history:</strong></p> <p><strong>v1.3 - Documentation update:</strong></p> <ul> <li>The document summarising datasets, <code>dataset_summary.pdf</code>, has been updated.</li> </ul> <p><strong>v1.2 - Startup cost correction:</strong></p> <ul> <li>Startup cost column labels in <code>generators.csv</code> were mistakenly switched (specifically, SU_COST_WARM and SU_COST_HOT). This has now been corrected.</li> </ul> <p><strong>v1.1 - Transmission line parameters and demand allocation update</strong></p> <ul> <li><strong>Transmission lines: </strong>Line resistance and shunt susceptance values have been updated. Transmission line lengths and voltages have also been added to <code>network_edges.csv</code>. AC interconnector information is split over two files to better capture aggregate flow limits defined over the New South Wales - Victoria interconnector. Connection points for these interconnectors are described in <code>network_ac_interconnector_links.csv</code>, while <code>network_ac_interconnector_flow_limits.csv</code> contains aggregate forward and reverse power flow limits.</li> <li><strong>Demand allocation: </strong>The algorithm used to approximate demand at different nodes has been updated. The new method constructs a Voronoi tessellation based on network nodes. These cells are then overlapped with geospatial ABS population data, which are used to estimate the number of people served by each node.</li> </ul> <p><strong>v1.0 - First release</strong></p>
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>
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>
Vehicle-to-grid Response on 13 February 2024 in Australian National Electricity Market
<p>This data captures the response of 16 Nissan LEAF electric vehicles to a frequency contingency in the Australian National Electricity Market on the 13th of February 2024, which led to widespread blackouts in Melbourne. The data comes from the bidirectional Wallbox Quasar chargers, as well as six high speed power meters located at the grid connection of each of the properties in which the vehicles were charging.</p>
Modelling input data for the case studies of the paper "Strategic bidding in light-robust day-ahead electricity markets".
<p><span>This data package includes the modelling input data to replicate the results of the case studies included in the paper "Strategic bidding in light-robust day-ahead electricity markets". </span></p> <p><span>This supplementary data package includes the following files:</span></p> <p><span><span>-<span> </span></span></span><span>Meta Data – Input data: Dataset containing the input data for the strategic bidding behavior problem. It includes bids from conventional, demand and stochastic players and the scenarios for system imbalance and real-time production.</span></p> <p><span><span>-<span> </span></span></span><span>Readme.txt: Includes a detailed description of the data packages</span></p> <p><span> </span></p> <p><span>Sources of data:</span></p> <p><span>* Ordoudis, C., Pinson, P., Morales, J. M., & Zugno, M. (2016). An updated version of the IEEE RTS 24-bus system for electricity market and power system operation studies. Technical University of Denmark.</span></p> <p><span>* Silva-Rodriguez, L., Sanjab, A., Fumagalli, E., Virag, A., & Gibescu, M. (2022). A light robust optimization approach for uncertainty-based day-ahead electricity markets. Electric Power Systems Research, 212, 108281. https://doi.org/10.1016/J.EPSR.2022.108281<span> </span></span></p> <p><span>* Derived (scaled down) from Elia. (2024). Open data. Retrieved from https://www.elia.be/en/grid-data/open-data<span> </span></span></p> <p><span>* Own data<span> </span></span></p> <p><span><span> </span></span></p>
Demonstration of Simulation Tools for Electricity Markets considering Power Flow Analysis
<p>Two novel publicly available web services for electricity market (EM) simulation and study and power flow evaluation and validation are proposed, namely the Electricity Market Service (EMS) and Power Flow Service (PFS). EMS enables the simulation of two auction-based algorithms and the execution of three wholesale EMs. PFS provides the creation and evaluation of electrical grids from the transmission to distribution grids. Combining both services one can simulate EMs from wholesale to local markets and test if the results are compatible with a specific electrical grid. This dataset publishes the input and output data of a case study simulation scenario.</p>
Spatial and temporal variation in the value of solar power across United States electricity markets
<p>This repository includes python scripts and input/output data associated with the following publication:</p> <p>[1] Brown, P.R.; O'Sullivan, F. "Spatial and temporal variation in the value of solar power across United States Electricity Markets". Renewable & Sustainable Energy Reviews 2019. <a href="https://doi.org/10.1016/j.rser.2019.109594">https://doi.org/10.1016/j.rser.2019.109594</a></p> <p>Please cite reference [1] for full documentation if the contents of this repository are used for subsequent work.</p> <p>Many of the scripts, data, and descriptive text in this repository are shared with the following publication:</p> <p>[2] Brown, P.R.; O'Sullivan, F. "Shaping photovoltaic array output to align with changing wholesale electricity price profiles". Applied Energy 2019, 256, 113734. <a href="https://doi.org/10.1016/j.apenergy.2019.113734">https://doi.org/10.1016/j.apenergy.2019.113734</a></p> <p>All code is in python 3 and relies on a number of dependencies that can be installed using pip or conda.</p> <p><strong>Contents</strong></p> <ul> <li>pvvm/*.py : Python module with functions for modeling PV generation and calculating PV energy revenue, capacity value, and emissions offset.</li> <li>notebooks/*.ipynb : Jupyter notebooks, including: <ul> <li>pvvm-vos-data.ipynb: Example scripts used to download and clean input LMP data, determine LMP node locations, assign nodes to capacity zones, download NSRDB input data, and reproduce some figures in [1]</li> <li>pvvm-example-generation.ipynb: Example scripts demonstrating the use of the PV generation model and a sensitivity analysis of PV generator assumptions</li> <li>pvvm-example-plots.ipynb: Example scripts demonstrating different plotting functions</li> <li>validate-pv-monthly-eia.ipynb: Scripts and plots for comparing modeled PV generation with monthly generation reported in EIA forms 860 and 923, as discussed in SI Note 3 of [1]</li> <li>validate-pv-hourly-pvdaq.ipynb: Scripts and plots for comparing modeled PV generation with hourly generation reported in NREL PVDAQ database, as discussed in SI Note 3 of [1]</li> <li>pvvm-energyvalue.ipynb: Scripts for calculating the wholesale energy market revenues of PV and reproducing some figures in [1]</li> <li>pvvm-capacityvalue.ipynb: Scripts for calculating the capacity credit and capacity revenues of PV and reproducing some figures in [1]</li> <li>pvvm-emissionsvalue.ipynb: Scripts for calculating the emissions offset of PV and reproducing some figures in [1]</li> <li>pvvm-breakeven.ipynb: Scripts for calculating the breakeven upfront cost and carbon price for PV and reproducing some figures in [1]</li> </ul> </li> <li>html/*.html : Static images of the above Jupyter notebooks for viewing without a python kernel</li> <li>data/lmp/*.gz : Day-ahead nodal locational marginal prices (LMPs) and marginal costs of energy (MCE), congestion (MCC), and losses (MCL) for CAISO, ERCOT, MISO, NYISO, and ISONE. <ul> <li>At the time of publication of this repository, permission had not been received from PJM to republish their LMP data. If permission is received in the future, a new version of this repository will be linked here with the complete dataset.</li> </ul> </li> <li>results/*.csv.gz : Simulation results associated with [1], including modeled energy revenue, capacity credit and revenue, emissions offsets, and breakeven costs for PV systems at all LMP nodes</li> </ul> <p><strong>Data notes</strong></p> <ul> <li>ISO LMP data are used with permission from the different ISOs. Adapting the MIT License (<a href="https://opensource.org/licenses/MIT">https://opensource.org/licenses/MIT</a>), "The data are provided 'as is', without warranty of any kind, express or implied, including but not limited to the warranties of merchantibility, fitness for a particular purpose and noninfringement. In no event shall the authors or sources be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the data or other dealings with the data." Copyright and usage permissions for the LMP data are available on the ISO websites, linked below.</li> <li>ISO-specific notes on LMP data: <ul> <li>CAISO data from <a href="http://oasis.caiso.com/mrioasis/logon.do">http://oasis.caiso.com/mrioasis/logon.do</a> are used pursuant to the terms at <a href="http://www.caiso.com/Pages/PrivacyPolicy.aspx#TermsOfUse">http://www.caiso.com/Pages/PrivacyPolicy.aspx#TermsOfUse</a>.</li> <li>ERCOT data are from <a href="http://www.ercot.com/mktinfo/prices">http://www.ercot.com/mktinfo/prices</a>.</li> <li>MISO data are from <a href="https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/">https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/</a> and <a href="https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/market-report-archives/">https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/market-report-archives/</a>.</li> <li>PJM data were originally downloaded from <a href="https://www.pjm.com/markets-and-operations/energy/day-ahead/lmpda.aspx">https://www.pjm.com/markets-and-operations/energy/day-ahead/lmpda.aspx</a> and <a href="https://www.pjm.com/markets-and-operations/energy/real-time/lmp.aspx">https://www.pjm.com/markets-and-operations/energy/real-time/lmp.aspx</a>. At the time of this writing these data are currently hosted at <a href="https://dataminer2.pjm.com/feed/da_hrl_lmps">https://dataminer2.pjm.com/feed/da_hrl_lmps</a> and <a href="https://dataminer2.pjm.com/feed/rt_hrl_lmps">https://dataminer2.pjm.com/feed/rt_hrl_lmps</a>.</li> <li>NYISO data from <a href="http://mis.nyiso.com/public/">http://mis.nyiso.com/public/</a> are used subject to the disclaimer at <a href="https://www.nyiso.com/legal-notice">https://www.nyiso.com/legal-notice</a>.</li> <li>ISONE data are from <a href="https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-da-hourly">https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-da-hourly</a> and <a href="https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-rt-hourly-final">https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-rt-hourly-final</a>. The Material is provided on an "as is" basis. ISO New England Inc., to the fullest extent permitted by law, disclaims all warranties, either express or implied, statutory or otherwise, including but not limited to the implied warranties of merchantability, non-infringement of third parties' rights, and fitness for particular purpose. Without limiting the foregoing, ISO New England Inc. makes no representations or warranties about the accuracy, reliability, completeness, date, or timeliness of the Material. ISO New England Inc. shall have no liability to you, your employer or any other third party based on your use of or reliance on the Material.</li> </ul> </li> <li>Data workup: LMP data were downloaded directly from the ISOs using scripts similar to the pvvm.data.download_lmps() function (see below for caveats), then repackaged into single-node single-year files using the pvvm.data.nodalize() function. These single-node single-year files were then combined into the dataframes included in this repository, using the procedure shown in the pvvm-vos-data.ipynb notebook for MISO. We provide these yearly dataframes, rather than the long-form data, to minimize file size and number. These dataframes can be unpacked into the single-node files used in the analysis using the pvvm.data.copylmps() function.</li> </ul> <p><strong>Usage notes</strong></p> <ul> <li>Code is provided under the <a href="https://opensource.org/licenses/MIT">MIT License</a>, as specified in the pvvm/LICENSE file and at the top of each *.py file. </li> <li>Updates to the code, if any, will be posted in the non-static repository at <a href="https://github.com/patrickbrown4/pvvm_vos">https://github.com/patrickbrown4/pvvm_vos</a>. The code in the present repository has the following version-specific dependencies: <ul> <li>matplotlib: 3.0.3</li> <li>numpy: 1.16.2</li> <li>pandas: 0.24.2</li> <li>pvlib: 0.6.1</li> <li>scipy: 1.2.1</li> <li>tqdm: 4.31.1</li> </ul> </li> <li>To use the NSRDB download functions, you will need to modify the "settings.py" file to insert a valid NSRDB API key, which can be requested from <a href="https://developer.nrel.gov/signup/">https://developer.nrel.gov/signup/</a>. Locations can be specified by passing (latitude, longitude) floats to pvvm.data.downloadNSRDBfile(), or by passing a string googlemaps query to pvvm.io.queryNSRDBfile(). To use the googlemaps functionality, you will need to request a googlemaps API key (<a href="https://developers.google.com/maps/documentation/javascript/get-api-key">https://developers.google.com/maps/documentation/javascript/get-api-key</a>) and insert it in the "settings.py" file.</li> <li>Note that many of the ISO websites have changed in the time since the functions in the pvvm.data module were written and the LMP data used in the above papers were downloaded. As such, the pvvm.data.download_lmps() function no longer works for all ISOs and years. We provide this function to illustrate the general procedure used, and do not intend to maintain it or keep it up to date with the changing ISO websites. For up-to-date functions for accessing ISO data, the following repository (no connection to the present work) may be helpful: <a href="https://github.com/catalyst-cooperative/pudl">https://github.com/catalyst-cooperative/pudl</a>.</li> </ul> <p> </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>
How do changes in settlement periods affect wholesale market prices? Evidence from Australia's National Electricity Market
<p>This is an electricity market dataset, including demand, dispatch and regional prices, sourced from the Australian Energy Market Operator (AEMO, 2023). We assemble a five-minute frequency panel dataset of the National Electricity Market (NEM) between 1 July 2020 and 31 December 2021.</p>
Dataset for A probabilistic forecast methodology for volatile electricity prices in the Australian National Electricity Market
Open the record for dataset details and reuse information.
Electronic Companion - Distribution and Transmission Coordinated Dispatch Under Joint Electricity and Carbon Day-Ahead Markets
<p>This release is associated with a paper entitled "Distribution and Transmission Coordinated Dispatch Under Joint Electricity and<br>Carbon Day-Ahead Markets".</p> <p>In this document, we provide the mathematical manipulation employed to linearize an MPEC formulation for the strategic bidding of a DSO participating in the electricity and carbon allowance wholesale day-ahead markets.</p> <p>We also aggregate all the information regarding the transmission and distribution power systems, as well as the power sources connected to them, adopted to obtain the results shown in the paper. The power systems are modified versions of the IEEE <a href="https://icseg.iti.illinois.edu/ieee-14-bus-system/">14-bus</a> representing the transmission side and the IEEE <a href="https://cmte.ieee.org/pes-testfeeders/resources/">34-bus</a> and 123-bus systems representing the distribution side.</p>
Open Data Set for the article Analysing the impact of renewables on Iberian wholesale electricity market prices using machine learning techniques. Green Finance, 2024, 6 (2), 363-382
<p>The datasets available for open access from the article ‘<em>Ballester, C. and Furió, D. (2024). Analysing the impact of renewables on Iberian wholesale electricity market prices using machine learning techniques. Green Finance, 6 (2), 363-382</em>’ are provided here. This study has been supported by funding from the Spanish Ministry of Science, Innovation, and Universities (Project PGC2018-093645-B-100).</p> <p>The data encompasses the price series of the components of the final wholesale prices, other than the day-ahead market price, at an hourly frequency, from January 2017 to December 2021. In particular, the daily average of the hourly price series of the intraday market, which captures the net effect of the six sessions of the intraday market on the final price, the daily average of the hourly net effect on the final price of the procedure to solve technical constraints, the daily average of the hourly costs resulting from ancillary services and deviation management, the daily average of the hourly costs related to capacity payments and the daily average of the hourly costs associated with the interruptibility service. In addition, we compute the daily average of the hourly series of bids (price and amount) individually submitted by market participants to buy or sell energy, distinguishing between matched and non-matched bids, both in the day-ahead market and in the first session of the intraday market. Other energy-related price series included in the analysis are: the Dutch TTF futures price, the API2 index for the coal price, the EUA futures price, the percentage of hours with 100% use from the France-Spain interconnection, the spread from the France-Spain interconnection, the percentage of water reserves in the reservoirs of the Iberian Peninsula. All shareable data are made available in accordance with open data principles to promote transparency and reproducibility in research. However, there is a specific dataset that we are not authorized to share publicly. Specifically, this includes the data corresponding to the Dutch TTF futures price and the API2 index. Consequently, this dataset is published under restricted access.</p>
Open Data Set for the article Dynamic demand response to electricity prices: Evidence from the Spanish retail market. Utilities Policy, 88 (2024), 101763
<p><span>The datasets available for open access from the article ‘Furió, D. and Moreno-del-Castillo, J.<em> Dynamic demand response to electricity prices: Evidence from the Spanish retail market. Utilities Policy, 88 (2024), 101763</em>’ are provided here.</span></p> <p><span>The datasets comprise time series data on wholesale market global prices and quantity demanded by reference suppliers and competing retailers, along with day-ahead market prices for each of the 24 hours of the day spanning from January 1, 2007, to March 31, 2022. These series have been downloaded from the CNMC website. Additionally, primary temperature data were obtained from the Spanish Meteorological Agency’s website. This dataset provided hourly weather information from a nationwide network of weather stations. Given the scope of the demand series data, which reflects the entire Spanish market, the temperature series were constructed to ensure national representativeness. These data were then aggregated to develop comprehensive national temperature series, aligning them with the demand series. <span>Finally, a dummy variable for each day t of the studied period was constructed to capture the business/non-business day effect on electricity demand. It takes the value of 1 if </span></span><span>𝑡</span><span> corresponds to a working day (non-holiday, Monday to Friday) and 0 otherwise. The national holidays considered are as follows: January 1<sup>st</sup>, January 6<sup>th</sup>, May 1<sup>st</sup>, August 15<sup>th</sup>, October 12<sup>th</sup>, November 1<sup>st</sup>, December 6<sup>th</sup>, December 8<sup>th</sup>, and December 25th. Additionally, the corresponding Good Friday for each year included in the study period was also considered.</span></p>
Dataset and code related to the publication "Aligning heat pump operation with market signals: A win-win scenario for the electricity market and its actors?"
<h3>General remarks</h3> <p>This dataset and code was developed and used for the publication</p> <blockquote> <p><em>E. Sperber, C. Schimeczek, U. Frey, K. K. Cao, V. Bertsch: Aligning heat pump operation with market signals: A win-win scenario for the electricity market and its actors? In: Energy Reports, Volume 13, June 2025, pp. 491-513, https://doi.org/10.1016/j.egyr.2024.12.028<br></em></p> </blockquote> <h3>Data description</h3> <p>The dataset "<em>heat_pump_results.zip</em>" includes all relevant results underlying the above publication. </p> <p>The data is organized according to the scenarios considered in the analysis. Therefore, each folder is labelled according to the scenario structure, starting with the building efficiency scenario ("BAU" / "EFF"), followed by the PV scenario ("no-PV" / "with-PV"), the HP operation scenario ("ModFlex_V", "ModFlex_F", "HighFlex_V"), the simulation year (2030 / 2040) and the weather year (2017 / 2019 / 2023).</p> <p>Within each result directory per scenario, the data is further structured according to the results at the market level ("AMIRIS") and at the user level ("GAMS" and "INFLEX"). The "INFLEX" directory contains results for the inflexible reference operation, while the "GAMS" directory contains results for the cost-minimized heat pump operation at the level of building types. The data at the user level is further structured by location. Data is given in hourly resolution.</p> <p>The scenario evaluation indicators presented in the publication, as well as other indicators, are summarized in the "MPI" directory.</p> <p>Abbreviations used:</p> <ul> <li>HP: heat pump</li> <li>SH: space heating</li> <li>DHW: domestic hot water</li> <li>AW: air/water</li> <li>BW: brine/water</li> <li>PV: photovoltaic</li> <li>Ti: indoor air temperature</li> <li>Ttes: temperature of domestic hot water storage tank</li> </ul> <h3>Code description</h3> <p>The code to generate the results is given in "<em>heat_pump_workflow.zip</em>". Please follow the instructions in the README.md that you can find in this archive.</p> <h3>Conctact</h3> <p>Please contact Evelyn Sperber at evelyn.sperber@dlr.de if you have any questions.</p>
Replication Package for: Welfare and Redistribution in Residential Electricity Markets with Solar Power
<p>The Stata Do-Files, Matlab Scripts and Functions in this folder can be used to replicate the tables and graphs of the paper "Welfare and Redistribution in Residential Electricity Markets with Solar Power" by Fabian Feger, Nicola Pavanini, and Doina Radulescu.</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>
Electronic Companion - Distribution and Transmission Coordinated Dispatch Under Joint Electricity and Carbon Day-Ahead Markets
<p>This release is associated with a paper entitled "Distribution and Transmission Coordinated Dispatch Under Joint Electricity and<br> Carbon Day-Ahead Markets".</p> <p>In this document, we provide the mathematical manipulation employed to linearize an MPEC formulation for the strategic bidding of a DSO participating in the electricity and carbon allowance wholesale day-ahead markets.</p> <p>We also aggregate all the information regarding the transmission and distribution power systems, as well as the power sources connected to them, adopted to obtain the results shown in the paper. The power 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.</p>
Data for Assessing the renewable energy policy paradox: a scenario analysis for the Italian electricity market
<p>This page contains the datasets and codes used to generate the figures for the article Assessing the renewable energy policy paradox: a scenario analysis for the Italian electricity market.</p> <p>Below you will find two datasets (.dta) and four codes (.do) files. Please note that the .do files contain the original paths to where the datasets were saved, you should change them to where they are saved in your computer. </p> <ol> <li>The file <em>clustering_prer_ok.do</em> uses dataset <em>yearlyvars_raw.dta</em> to generate three additional datasets <em>clusters.dta</em>, <em>yearly_scenarios.dta</em> and <em>xwalk.dta</em> <ul> <li><em>clusters.dta</em> is used in <em>Figs_2-5-6_ok.do</em></li> <li><em>yearly_scenarios.dta</em> is used in <em>Figs_3_ok.do</em></li> </ul> </li> <li>Dataset <em>hprice_raw.dta</em> is used together with the generated <em>xwalk.dta</em> in <em>Fig_4_ok.do</em>.</li> </ol>
Nordic44 - 2015 Powerflow Data: An Open Data Repository of an Equivalent Nordic Grid Model Matched to Historical Electricity Market Data for 2015
<p>This repository is used to provide documentation related to the model and data development process, provide source (raw) data for the model in different forms (i.e. Modelica, CIM 14, and PSS/E) for an equivalent Nordic grid model that has been matched to historical power flow data.</p> <p>The repository is documented in the paper below, see [Ref00].</p> <p><strong>Using this model, data or related software = cite our publications!</strong></p> <p>We are happy to contribute with this dataset, however, if you use any of the data or software provided, we will appreciate if you cite the following publications, as follows:</p> <p>A) Cite that "the raw and processed data files corresponding to the model are available as an open data set and documented in [Ref00]."</p> <p>B) Cite that the first appearance of the model, i.e. "the model is first presented in [Ref01]"</p> <p>[Ref00] L. Vanfretti, S.H. Olsen, V. S. Narasimham Arava, G. Laera, A. Bibadafar, T. Rabuzin, H. Jackobsen, J. Lavenius, and M. Baudette, "An Open Data Repository and a Data Processing Software Toolset of an Equivalent Nordic Grid Model Matched to Historical Electricity Market Data," submitted for publication, Data in Brief, 2016.</p> <p>[Ref01] L. Vanfretti, T. Rabuzin, M. Baudette, M. Murad, iTesla Power Systems Library (iPSL): A Modelica library for phasor time-domain simulations, SoftwareX, Available online 18 May 2016, ISSN 2352-7110, http://dx.doi.org/10.1016/j.softx.2016.05.001.</p> <p><strong>Acknowledgment:</strong></p> <p>This model was originally developed in the context of the FP7 iTesla project, and further extended within the ITEA3 openCPSproject.</p> <p>Structure of the repository:</p> <p><strong>01_PSSE_Resources</strong>:</p> <ol> <li> <p><strong>Models</strong> :</p> <ul> <li> <p>A folder with PSS/E files of the base case</p> </li> <li> <p>A folder with a 7zip archive containing files of the original N44 system that has been modified to have the PSS/E base case</p> </li> </ul> </li> <li> <p><strong>Snapshots</strong> :</p> <ul> <li> <p><strong>N44_2015xxxx</strong> are folders named according to the day they refer to (for example <em>N44_20150401</em> refers to the 1st of April 2015). In each folder there are Excel files (<em>Consumption_xx.xlsx</em>, <em>Exchange_xx.xlsx</em>, <em>Production_xx.xlsx</em>) with data downloaded from Nord Pool website, an Excel file (<em>PSSE_in_out.xlsx</em>) summarizing the results from the Python script <em>Nordic44.py</em> in the folder <strong>04_Python_Resources</strong>, PSS/E snapshots for each hour before solving the power flow (<em>hx_before_PF.raw</em>) and after solving the power flow (<em>hx_after_PF.raw</em>)</p> </li> <li> <p><em>N44_BC.sav</em> is the PSS/E solved base case that Python script <em>Nordic44.py</em> (put the reference)</p> </li> </ul> </li> </ol> <p><strong>02_CIM14_Snapshots</strong>:</p> <ul> <li> <p><strong>N44_2015xxxx</strong> are folders named according to the day they refer to (e.g. <strong>N44_20150401</strong> refers to the 1st of April 2015). In each folder there are CIM files for each hour (<em>N44_hx_EQ.xml</em>, <em>N44_hx_SV.xml_, _N44_hx_TP.xml</em>)</p> </li> <li> <p><strong>N44_noOL_RDFIDMAP.xml</strong> is the file with IDs mapping of those cases (<em>N44_hx_noOL_EQ.xml</em>, <em>N44_hx_noOL_SV.xml</em>, <em>N44_hx_noOL_TP.xml</em>) with fixed overloading problems.</p> </li> <li> <p><strong>N44_RDFIDMAP_2015-1.xml</strong> and <strong>N44_RDFIDMAP_2015-2.xml</strong> are the files with IDs mapping of the remaining snapshots from 2015</p> </li> </ul> <p><strong>03_Modelica</strong>:</p> <ol> <li> <p><strong>iTesla_Platform</strong></p> <ul> <li> <p><strong>iPSL</strong> folder contains the version of the library which can be used to simulate snapshots generated from the iTesla Platform</p> </li> <li> <p><strong>Modelica_snapshots</strong> Modelica models generated from the snapshots by iTesla Platform</p> </li> </ul> </li> <li> <p><strong>SmarTSLab</strong></p> <ul> <li> <p><strong>OpenIPSL</strong> folder contains the version of the forked iPSL library which can be used to simulate the manually generated Modelica model of N44 with the record structures corresponding to the snapshots</p> </li> <li> <p><strong>Snapshots</strong> folder contains Modelica records automatically generated from the PSS/E records</p> </li> <li> <p><em>N44_Base_Case.mo</em> is the handmade N44 model with the loaded record of the power flow results from the PSS/E base case. It can be used to load other PF results from the folder <strong>03_Modelica/Snapshots</strong></p> </li> </ul> </li> </ol>
Dataset for "Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market"
<p>This is the dataset for the manuscript "Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market" submitted to the journal PLOS ONE. </p>
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