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32 results for “demand response”
Production line dataset for task scheduling and energy optimization - Demand Response Participation
<p>Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> it was simulated an announcement of a demand response program at period 757, describing a demand response event from period 937 (Friday at 21:00h) to 960 (Friday at 23:00h) , where each period represents five minutes. The demand response program imposed a limit consumption, during its event, of 2.5 kWh. The announcement of the demand response allowed the use of the proposed solution to limit the energy consumption. For that, the algorithm described in section 3.3 was executed at period 769 (Friday at 7:00h).</p> <p>The API can be found at <<a href="http://www.gecad.isep.ipp.pt/api/spear/%3E">http://www.gecad.isep.ipp.pt/api/spear/</a>></p> <p>File Description:</p> <ul> <li>Input_JSON_Demand_Response_Optimization - JSON input data for the demand response participation</li> <li>Output_JSON_Demand_Response_Optimization - JSON output data for the demand response participation</li> <li>Output_Statistics_Demand_Response_Optimization - Excel output demand response participation statistics</li> <li>Comparison_Output_Statistics_Demand_Response - Excel output statistics comparing the before and after the demand response participation</li> </ul>
Joint Optimization of Production and Maintenance for Cost-effective Manufacturing and Demand Response Participation Dataset - Machine Breakdown Event
<p>Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> an announcement of a machine breakdown event was simulated on Friday at 6:00, describing that machine MAQ119 could breakdown at any moment, detected using a predictive maintenance system. Accordingly, the proposed scheduler imposed a machine available frames constraint, during its event, of 0 usable frames, thus removing the machine from production. The proposed genetic algorithm was executed for 1 hour at period 769 (Friday at 7:00) until the remainder of the schedule’s time window. Also, the predefined optimization weights were 1 for total cost and 0 for machine occupancy deviation.</p> <p> </p> <p>File Description:</p> <ul> <li>Input_JSON_Machine_Breakdown_Optimization - JSON input data for the machine breakdown event</li> <li>Output_JSON_Machine_Breakdown_Optimization - JSON output data for the machine breakdown event</li> <li>Output_Statistics_Machine_Breakdown_Optimization - Excel output machine breakdown event statistics</li> </ul>
Joint Optimization of Production and Maintenance for Cost-effective Manufacturing and Demand Response Participation Dataset - Maintenance Optimization
<p>Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> a maintenance optimization scenario was formulated to validate the scheduler's ability to schedule tasks as well as maintenance activities while also minimizing the total costs. Accordingly, it was considered an optimization weight of 1 for the total cost and 0 for machine occupancy deviation, as well as a 2-hour execution time for the genetic algorithm. Each maintenance activity, for every machine, has a duration of 6 hours and 10 minutes, with a labor cost of 3,22 EUR/hour during the stipulated maintenance hours and a monetary penalty, that doubles the cost (i.e., 6.44 EUR/hour) if done out of maintenance hours.</p> <p> </p> <p>File Description:</p> <ul> <li>Input_JSON_Maintenance_Optimization - JSON input data for the maintenance optimization</li> <li>Output_JSON_Maintenance_Optimization - JSON output data for the maintenance optimization</li> <li>Output_Statistics_Maintenance_Optimization - Excel output maintenance optimization statistics</li> </ul>
Joint Optimization of Production and Maintenance for Cost-effective Manufacturing and Demand Response Participation Dataset - Total Cost and Machine Occupancy Deviation Optimization
<p>Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> a total cost and machine occupancy deviation optimization scenario was formulated that aims to demonstrate how the proposed scheduler is able to balance tasks between machines while also reducing overall costs. For this scenario, it was considered an optimization weight of 0.5 for both the total costs and machine occupancy deviation objectives and the genetic algorithm was executed for 2 hours.</p> <p> </p> <p>File Description:</p> <ul> <li>Input_JSON_Total_Cost_Machine_Occupancy_Deviation_Optimization - JSON input data for the total cost and machine occupancy deviation optimization</li> <li>Output_JSON_Total_Cost_Machine_Occupancy_Deviation_Optimization - JSON output data for the total cost and machine occupancy deviation optimization</li> <li>Output_Statistics_Total_Cost_Machine_Occupancy_Deviation_Optimization - Excel output total cost and machine occupancy deviation optimization statistics</li> </ul>
Joint Optimization of Production and Maintenance for Cost-effective Manufacturing and Demand Response Participation Dataset - Energy Cost Optimization with Energy Selling
<p>Using the previous dataset at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>> an energy cost optimization considering the presence of an energy buyer is proposed to validate the scheduler’s ability to maximize profits while also minimizing energy costs. The scenario considers an added sales value corresponding to 50% of the buying. For this scenario, the genetic algorithm was executed for 2 hours, with 1 and 0 for the optimization weights total cost and machine occupancy deviation, respectively.</p> <p> </p> <p>File Description:</p> <ul> <li>Input_JSON_Energy_Cost_Energy_Selling_Optimization - JSON input data for the energy cost optimization with energy selling</li> <li>Output_JSON_Energy_Cost_Energy_Selling_Optimization - JSON output data for the energy cost optimization with energy selling</li> <li>Output_Statistics_Energy_Cost_Energy_Selling_Optimization - Excel output energy cost optimization with energy selling statistics</li> </ul>
Joint Optimization of Production and Maintenance for Cost-effective Manufacturing and Demand Response Participation Dataset - Joint Optimization of Production and Maintenance
<p>Using the previous datasets at <<a href="https://zenodo.org/record/4106746">https://zenodo.org/record/4106746</a>>, <<a href="https://zenodo.org/record/7055698">https://zenodo.org/record/7055698</a>>, <<a href="https://zenodo.org/record/7055580">https://zenodo.org/record/7055580</a>>, and <<a href="https://zenodo.org/record/7055573">https://zenodo.org/record/7055573</a>> a joint optimization of production and maintenance scenario was formulated which aims at combining all the features from the cited scenarios. For energy selling, it was considered an added sales value corresponding to 50% of the buying. Regarding maintenance activities, it was simulated an announcement of a maintenance activity for MAQ118 from Monday at 07:00 (i.e., period 1) to Monday at 17:00 (i.e., period 120), and another for MAQ120 which can be done at any time. These maintenance activities take 6 hours and 10 minutes to complete and have an associated labor cost of 3,22 EUR/hour in maintenance hours, and a double cost penalty (i.e., 6.44 EUR/hour) if done out of maintenance hours. The scenario was executed in 2 hours, with the corresponding optimization weights of 0.8 and 0.2 for the total cost and machine occupancy deviation, respectively.</p> <p> </p> <p>File Description:</p> <ul> <li>Input_JSON_Joint_Optimization_Production_Maintenance - JSON input data for the joint optimization of production and maintenance</li> <li>Output_JSON_Joint_Optimization_Production_Maintenance - JSON output data for the joint optimization of production and maintenance</li> <li>Output_Statistics_Joint_Optimization_Production_Maintenance - Excel output joint optimization of production and maintenance statistics</li> </ul>
openENTRANCE - Case Study 1 - Residential Demand Response - Data and Scripts
<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>
Data from: Peregrine Falcons shift mean and variance in provisioning in response to increasing brood demand
<p><span>The hierarchical model of provisioning posits that parents employ a strategic, sequential use of three provisioning tactics as offspring demand increases (e.g., due to increasing brood size and age). Namely, increasing delivery rate (reducing intervals between provisioning visits), expanding provisioned diet breadth, and adopting variance-sensitive provisioning. We evaluated this model in an Arctic breeding population of Peregrine falcons (<em>Falco peregrinus tundrius</em>) by analyzing changes in inter-visit-intervals (IVIs) and residual variance in IVIs across 7 study years. Data was collected using motion-sensitive nest camera images and analyzed using Bayesian mixed effect models. We found strong support for a decrease in IVIs (i.e., increase in delivery rates) between provisioning visits and an increase in residual variance in IVIs with increasing nestling age, consistent with the notion that peregrines shift to variance-prone provisioning strategies with increasing nestling demand. However, support for predictions made based on the hierarchical model of tactics for coping with increased brood demand was equivocal as we did not find evidence in support of expected covariances between random effects (i.e., between IVI to an average-sized brood (intercept), change in IVI with brood demand (slope) or variance in IVI). Overall, our study provides important biological insights into how parents cope with increased brood demand. </span></p>
AMIRIS demand response workflow input
<p>This upload contains the <strong>input data</strong> necessary to run a workflow applying the agent-based power market model <strong><a href="https://gitlab.com/dlr-ve/esy/amiris/amiris">AMIRIS</a> </strong>in order to study the impact of power tariffs design on <strong>demand response</strong> profitability.</p> <h2>Usage</h2> <p>The data has to be copied into the "./inputs/data/" folder of the <a href="https://github.com/jokochems/demand_response_analyses_workflow"><em>demand response analyses workflow</em></a> and unpacked there. See the description of the workflow on the dependencies how to execute the model.</p> <p>Important note: You will need a version of AMIRIS that is not yet open source and contains the demand response implementation. Feel free to contact the author of this data set in order to request it. Also, you will need a solver, such as Gurobi or CPLEX for instance.</p> <h2>Background</h2> <p>Data has been obtained from previous <a href="https://github.com/pommes-public/pommesinvest"><em>pommesinvest </em></a>model runs.</p> <p>It has been put together by executing a data <a href="https://github.com/pommes-public/pommesevaluation/blob/main/amiris_converter.ipynb">converter script</a> from the <a href="https://github.com/pommes-public/pommesevaluation/">pommesevaluation </a>repository that compiles the inputs into a format understood by AMIRIS, thus allowing for a soft model coupling (sequential execution) with full input harmonization.</p>
"Demand-side policies for power generation in response to the energy crisis: A model analysis for Italy", scripts and data
<p>This repository contains the data, scripts and results for the paper "Demand-side policies for power generation in response to the energy crisis: A model analysis for Italy", https://doi.org/10.1016/j.esr.2024.101329.</p> <p>Results in the paper are divided into three sections, corresponding to the numbers of the folders inside this dataset. They are described as follows:</p> <p>1 - EU policy impact: What is the impact on the Italian electricity of the european proposal of cutting power demand and shifting it during peak hours on gas consumption, system costs and emissions?</p> <p>2 - Gas cost sensitivity: Which would be Italy’s most convenient power system considering different gas prices?</p> <p>3 - DSM in mitigation: What could be the role of demand side measures in power systems with a high penetration of RES?</p>
Fig. 1 in Rapid endangerment of the lizard Podarcis pityusensis by an invasive snake demands an immediate conservation response
Fig. 1. Basic elements of the system. (A) Map showing the location of Ibiza island in relation to the nearest mainland areas in the Iberian Peninsula and northern Africa. (B) The endemic lacertid lizard, Podarcis pityusensis. (C) The invasive colubrid snake, Hemorrhois hippocrepis. Photos by: Javier Pleguezuelos (A), Juan M. Pleguezuelos (B), and Federico Rey (C).
Normalized profile of industrial demand response
<p>Normalized profile of industrial demand response based on an industrial facility in Austria in 2019</p>
Dataset article "Demand-Response Control of Electric Storage Water Heaters Based on Dynamic Electricity Pricing and Comfort Optimization"
<p>"README-SupplementaryMaterial.txt" explains the information gathered in each csv files, and including the DHW consumption profiles generated, the hourly electricity pricing for 2022 (Spain), and the experimental data utilized for the validation of the model. </p>
Data from: Peregrine Falcons shift mean and variance in provisioning in response to increasing brood demand
Open the record for dataset details and reuse information.
Raw data belonging to paper "Calculating Retail Prices from Demand Response Target Schedules to Operate Domestic Electric Water Heaters"
<p>The Zip file contains the raw data used for drawing conclusions in the paper "Calculating Retail Prices from Demand Response Target Schedules to Operate Domestic Electric Water Heaters" accepted for publication in Energy Informatics 2018.</p> <p>The raw data is the parameters of a sample of 50 domestic electric water heaters (DEWHs)<br> used for evaluating the algorithm in the paper. It is explained in the readme.txt.</p>
Plasticity of the gastrocnemius elastic system in response to decreased work and power demand during growth
<p class="MsoBodyText">Elastic energy storage and release can enhance performance that would otherwise be limited by the force-velocity constraints of muscle. While functional influence of a biological spring depends on tuning between components of an elastic system (the muscle, spring, driven mass, and lever system), we do not know whether elastic systems systematically adapt to functional demand. To test whether altering work and power generation during maturation alters the morphology of an elastic system, we prevented growing guinea fowl (<i>Numida Meleagris</i>) from jumping. At maturity, we compared the jump performance of our treatment group to that of controls and measured the morphology of the gastrocnemius elastic system. We found that restricted birds jumped with lower jump power and work, yet there were no significant between-group differences in the components of the elastic system. Further, subject-specific models revealed no difference in energy storage capacity between groups, though energy storage was most sensitive to variations in muscle properties (most significantly operating length and least dependent on tendon stiffness). We conclude that the gastrocnemius elastic system in the guinea fowl displays little to no plastic response to decreased demand during growth and hypothesize that neural plasticity may explain performance variation.</p>
Data from: Variable parental responses to changes in offspring demand have implications for life history theory
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Plasticity of the gastrocnemius elastic system in response to decreased work and power demand during growth
Open the record for dataset details and reuse information.
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>
Nitrogen fixation and gas exchange response to evaporative demand in soybean
<p><span>Atmospheric vapor pressure deficit (VPD) is increasing in many regions and has a large impact on plant productivity. A VPD increase leads to raising transpiration rate (TR) and soil-water demand, risking productivity penalties. Like water, nitrogen is critical to productivity, but the effect of VPD on legume nitrogen fixation is undocumented. To address this, we developed a portable system for quantifying nitrogen fixation noninvasively and at a high temporal resolution by tracking the rate of hydrogen gas evolution by root nodules. Combining field and controlled-environment experiments where we measured leaf gas exchange and H<sub>2</sub> production by nodules, we confirmed the ability of the system to track nitrogen-fixation dynamics. Raising VPD from 0.5 to 3 kPa within ~2.5 hrs under well-watered conditions increased nitrogen fixation by up to 25% in addition to TR, consistent with the hypothesis that raising VPD in that range might have alleviated nitrogenase feedback inhibition. Genotypic differences were found in this response, indicating a potential for breeding. Our study provides evidence for an important environmental effect on nitrogen fixation that is not taken into account in current crop and vegetation models, pointing to untapped avenues for better understanding climate change effects on legumes and nitrogen cycling. </span></p>
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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.