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9 results for “demand flexibility”
New Alternatives to the Flexibility of Electric Demand
<p>Over the past decades, the role of electric consumers has been increasingly active in terms of their connection with the profile of electricity demand they have. This change in approach has also been supported by the integration of renewable generation sources, which add a certain level of uncertainty to the system.</p>
openENTRANCE - Case Study 7 - Power-to-Heat Demand Flexibility: Results
<p>In case study 7 of the openENTRANCE project, Plan4EU, an electricity dispatch model for Europe, is soft-linked to the Flexibility Function, an indirect demand response model, via Frigg, a novel framework for integrating realistic demand response in energy system analysis. This modelling setup is applied to analyse the role of power-to-heat demand flexibility (end-consumer demand response and heat storage) in the Danish electricity system of 2050. This dataset contains results as presented in the related project report.</p>
Data to calculate metal demand from IAM energy projections and add flexibility to the technological mix
<p>In the "Data" folder, you’ll find all necessary data to run the <strong>"IAM_metal_optimisation.py"</strong> script, available on GitHub (<a href="https://github.com/Pelotte/iam-metal-demand-optimizer/tree/master" target="_new" rel="noopener">https://github.com/Pelotte/iam-metal-demand-optimizer/tree/master</a>).</p> <p>The <strong>"IAM_metal_optimisation.py"</strong> script is a tool designed to:</p> <ol> <li>Quantify metal supply and demand by sector through 2050, using energy projections from various IAMs and SSP-RCP scenarios.</li> <li>Optimize the IAM technological mix, minimizing adjustments needed to prevent metal demand from exceeding supply constraints.</li> </ol> <p>This tool supports all IAMs with power projections for SSP-RCP scenarios (except SSP3) provided in the IPCC’s Sixth Assessment Report (AR6), Working Group III, and available in the IIASA database.</p> <p>Data for RCP 2.6, SSPs 1, 2, 4, 5, of the SSP marker IAM models of the IPCC, are organized in the "Capacity Factor IAM," "GDP IAM," and "Power Capacity IAM" folders. See the GitHub README for more details.</p>
Energy demand and its temporal flexibility: approaches, criticalities and ways forward
<p>Data set of the reviewed documents in the contribution 'Energy demand and its temporal flexibility: approaches, criticalities and ways forward' under consideration for publication in 'Renewable & Sustainable Energy Reviews'</p>
Dataset for the publication: Flexible copper: exploring capacity-based energy demand flexibility in the industry
<p>This file contains the inputs for the study (currently under review) "Flexible copper: exploring capacity-based energy demand flexibility in the industry"</p>
Data from: Surprising flexibility in parental care revealed by experimental changes in offspring demand
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Demand-side flexibility enables cost savings in a Reversible pH-swing electrochemical process for Oceanic Co2 Removal
<p>Raw datasets and codes used in our manuscript of the same title. Figure numbers correspond with the figure numbers in the manuscript.</p> <p><strong>Figure 2: </strong>Process Overview(a) Steps of the proposed, reversible, oceanic CDR process, starting with acidification of the ocean water, followed by vacuum stripping of the CO2, basification of the oceanwater, and return to the ocean to equilibrate with the atmosphere. (b) Corresponding effects on pH and dissolved inorganic carbon (DIC) at each step in the oceanic CDR process. Oceanwater pH decreases from 8.1 during acidification to produce a shift in speciation from HCO3 todissolved CO2, which is phase-separated between step 2-3 to capture pure CO2 which decreases the DIC. Basification negates these changes through the addition of the same concentration of OH− ions as H+ ions added to ocean water during acidification, resulting in pH > 8.1 at the end of basification. CO2-starved ocean water will re-equilibrate with the atmosphere and get buffered back to a neutral-pH to re-enter acidification and the cycle continues.</p> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div><strong>Figure 3:</strong> Predicted Performance from 0-D Equivalent Circuit Modeling (Sec. Prediction of current-volatge behavior) for (a,c) acidification and (b,d) basification to determine (a,b) polarization behavior and (c,d) Faradaic efficiencies; including (c) power density produced during acidification.<br>Different mass-transfer conditions and therefore limiting currents are modeled by varying the liquid-side boundary layer thicknesses, (δl). Two different pH-shift conditions are modeled for the low and high pH values at the end of acidification and basification, that are respectively constrained by the concentration of H+ and OH− additions to be equal during both steps – (pHlow, pHhigh): (4,10.7) and (6,9) as described in Sec. S3. Baseline parameter values listed in Table S1 are used in the equivalent circuit models with consideration of desired and competing reactions. Note the differences in the current and potential ranges plotted between (a) acidification and (b) basification steps.</div> <div> </div> <div><strong>Figure 4: </strong>Energy Intensity Comparisons (a-c) Predictions for electrochemical energy intensity normalized by the amount of CO2 captured (Eq. (14)) in (GJ/tCO2 ) as a function of the current density when jbase = jacid (a) without and (b) with competing reactions modeled. Dashed lines on the plot indicates a baseline cell resistance of 2.18 Ω cm2 and the shaded areas are indica-tive of this ohmic resistance in the range of 0 - 5 Ω cm2; the shades of blue are indicative of theliquid-side boundary layer thicknesses, δl, modeled. Electrochemical energy intensities from (a)and (b) where δl = 50 μm are shown in (c), and compared with state-of-the-art ocean water CDRprocess intensities reported by Digdaya et al.,15 Kim et al.,23 and Yan et al.22 The gray filled areashows possible operational points with varying extents of competing reactions with the maximumbounded by competing reactions occurring at mass-transfer limited rates. (d) Overall energy inten-sity (GJ/tCO2 ) including electrochemical and parasitic loads (as described in Sec. Energy IntensityCalculations) for the proposed process without competing reactions, calculated at an industrially-relevant current density of 100 mAcm−2, the baseline cell resistance, and pH shift of 6 to 9.6,compared with electrochemical and thermally-driven direct air capture (DAC) processes with data obtained from Singh et al.;24 error bars for the thermal DAC processes are from Viebahn et al.</div> <div> </div> <div><strong>Figure 5: </strong>Operating Energy Profiles and Effects of Load Shifting (a) Integration of solar and wind electricity production in California, CAISO 2022-2023,40 with predicted energy demand for the proposed oceanic CO2 removal process as a function of varying extents of yearly CO2 captured, 0 – 16 MtCO2,year. (b) Hourly power demand to capture 1 tCO2 per day. The gray curve indicates the process shown in (a), while the red curves shows a constant power demand process occurring at an equivalent energy intensity, i.e., the area underneath both curves are the same. (c) Hourly cost to capture 1 tCO2 per day. The gray curve indicates the hourly cost associated with variable energy process shown in (b) if all produced electricity is sold at market value, while the red curve compares it to the hourly cost associated with constant power consumption process shown in (b). Operating conditions of the proposed, variable process shown in (a-c) include jacid = 75 mA cm−2 and jbase = 150 mA cm−2, while still constraining the same volume of ocean water being treated over a 24-hour day in the individual steps (i.e. tbase = 0.5tacid). The process switches between acidification (occurring early mornings and evenings/nights when electricity price is high) and basification (occurring mid-day when electricity price is low) based on the supply and the cost of electricity purchased from CAISO (Sec S8.5). (d) Electricity cost savings for the proposed process operation compared to a process with constant energy usage at different ratios of tbase to tacid, assuming a time-weighted average operating current density of 100 mA cm−2. Different line styles indicate different proportions of market value at which produced electricity can be sold (0%, 50%, and 100%). For all cases: competing reactions are not considered, a thin boundary layer is assumed (δl = 10 μm), and the pH shift is from 6 to 9.6. Parasitic energy costs are included for CO2 phase separation, but not for pumping as we assume co-location with a desalination plant. Parasitics are assumed to only operate during the basification process, at high enough power to remove all CO2 treated over the day.</div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
Data from: Independence among physiological traits suggests flexibility in the face of ecological demands on phenotypes
Phenotypic flexibility allows animals to adjust their physiology to diverse environmental conditions encountered over the year. Examining how these varying traits covary gives insights into potential constraints or freedoms that may shape evolutionary trajectories. In this study we examined relationships among hematocrit, baseline corticosterone concentration, constitutive immune function and basal metabolic rate in red knot Calidris canutus islandica individuals subjected to experimentally manipulated temperature treatments over an entire annual cycle. If covariation among traits is constrained, we predict consistent covariation within and among individuals. We further predict consistent correlations between physiological and metabolic traits if constraints underlie species level patterns found along the slow-fast pace-of-life continuum. We found no consistent correlations among hematocrit, baseline corticosterone concentration, immune function and basal metabolic rate either within or among individuals. This provides no evidence for constraints limiting relationships among these measures of the cardiovascular, endocrine, immune and metabolic systems in individual red knots. Rather, our data suggest that knots are free to adjust individual parts of their physiology independently. This makes good sense if one places the animal within its ecological context where different aspects of the environment might put different pressures on different aspects of physiology.
Data from: Independence among physiological traits suggests flexibility in the face of ecological demands on phenotypes
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Allen Brain Atlas
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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.