Find research datasets worth reusing

opencc-by-4.0Apr 2021View details →

Library of price and performance data of domestic and commercial technologies for low-carbon energy systems

This library consists of extensive price and performance data of commercially available technologies for low-carbon energy systems on the UK market, including domestic and commercial applications. All information is obtained from published manufacturer datasheets and pricelists. The library contains useful information for energy-system and technology modellers in their efforts to capture the techno-economic characteristics of different technology options, minimise uncertainties and suggest reliable system- and technology-design strategies. The data analysis shows how technology characteristics vary with component choice, technology size and to capture the spread of values found between different suppliers, which can be used to determine uncertainty bounds on key performance indicators. Fitting techniques can be used to determine relationships arising from the collected data, infer values for which data are not available and quantify the related variability in technology characteristics. This work was conducted by members of the <a href="https://www.imperial.ac.uk/clean-energy-processes/">Clean Energy Processes (CEP) Laboratory</a> and is part of Project 2 of the <a href="https://www.imperial.ac.uk/energy-futures-lab/idles/">Integrated Development of Low-Carbon Energy Systems (IDLES)</a> project. IDLES brings together researchers across Imperial College London and partner organisations and companies to provide the evidence needed to facilitate a cost-effective and secure transition to a low-carbon future. The overarching aim of Project 2 is: (i) to characterise current and new/future technologies in terms of cost and performance to provide evidence for whole-energy system modelling; and (ii) to extend the capabilities of whole-energy-system models so that they can, apart from optimising energy network infrastructures, provide information to manufacturers about the optimal choice of materials, components and the design of key technologies. The library will be updated regularly as more data regarding existing and new technologies are collected.

Alchemical Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision and Reproducibility - Dataset

This zip contains all input structures for paper the: Alchemical Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision and Reproducibility Authors: Alexander D. Wade, Agastya P. Bhati, Shunzhou Wan, Peter V.Coveney The structures of the folders are protein/ligand_transformation/alchemical_leg/input/files The ligand transformation are derived from previous work by wang et al. (https://pubs.acs.org/doi/10.1021/ja512751q) There are two files for the solvent alchemical leg: complex.pdb and complex.prmtop complex.pdb is  structure file that also denotes the alchemical atoms in the pdb beta column. complex.prmtop is an AMBER parameter/topology file For the complex alchemical leg there is an additional file constraints.pdb that contains the constraint information in the pdb beta column. These files can be used with TIES_MD (https://ucl-ccs.github.io/TIES_MD/) or other molecular dynamics engiens that take AMBER input.

Short wavelength/ high energy (14.2 keV) Cubic Insulin helical dataset, beamline ID23-EH2 ESRF

Short wavelength/ high energy (14.2 keV) Cubic Insulin helical dataset, beamline ID23-EH2 ESRF. CBF images from a Pilatus3 2M detector at 100K

opencc-by-4.0Mar 2021View details →

The global wave energy spectra data

The data were downloaded from ftp://ftp.ifremer.fr/ifremer/ww3/HINDCAST/GLOBAL/2017_ECMWF/ef, which are derived by Wavewatch model.

FRESH:COM Dynamic Participation in Local Energy Communities with Peer-to-Peer Trading

This repository contains input data used for the case study in 'Dynamic Participation in Local Energy Communities with Peer-to-Peer Trading'

opencc-by-4.0Nov 2021View details →

Climate model output for "The Unexpected Oceanic Peak in Energy Input to the Atmosphere and its Consequences for Monsoon Rainfall"

Climate model output associated with the manuscript "The Unexpected Oceanic Peak in Energy Input to the Atmosphere and its Consequences for Monsoon Rainfall"

EPSRC-funded Humanitarian Engineering and Energy for Displacement (HEED) datasets

This repository contains raw datasets gathered under the EPSRC-funded Humanitarian Engineering and Energy for Displacement (HEED) research project (EP/P029531/1). The project aimed to understand energy needs of displaced communities by creating an evidence base on the usage of seven different energy interventions, and provide recommendations for improved design of future energy interventions to better meet the needs of people. Below is a brief description of the interventions. <ol> <li>Stove-use monitoring systems (July 2019 to October 2019) - Stove-use monitoring systems (SUMs) were deployed on clay stoves in Kigeme camp, Rwanda in July 2019. The aim of the study was to evaluate stove usage patterns by measuring temperature profiles within stove enclosure and on the surface of stoves. The SUMs consisted of 2 sensors - a thermocouple to measure temperature within the stove and a Si7021 sensor to measure temperature outside the stove, connected to an Arduino MKR GSM 1400 board. The data measured by the sensors was stored only if the change in values exceeded a set threshold for either of the readings. The SUMs were powered by a re-chargeable Li-Ion battery of 3.7V and a rating of 7.59Wh. The study was conducted in 2 phases. In phase 1 (02 July 2019 to 30 September 2019), data was collected from 15 SUMs and stored locally on SD cards as well as communicated to a remote server via GSM. The time of data collection was recorded using GSM functionality. However, several GSM and MQTT failures were noted leading to loss of timestamp values as well as shorter battery lifetime due to re-transmission tries. In phase 2 (02 October 2019 to 17 October 2019), data was collected from 9 SUMs and only stored locally on SD cards. The time of data collection was recorded using an external RTC clock connected to the Arduino board. The data from both phases of study is deposited here in SUM.zip. The cleaned dataset is available at <a href="https://doi.org/10.5281/zenodo.3946999">10.5281/zenodo.3946999</a>.  </li> <li>Mobile Lantern monitoring systems (July 2019 to December 2019) - Mobile lantern monitoring systems (LMSs) were deployed in Nyabiheke camp, Rwanda, in July 2019. The aim of the study was to evaluate lantern usage (static or mobile) and consumption (charge and discharge) patterns. The monitors consisted of a D.light S30 solar lantern fitted with an Arduino-based monitoring device. The most integral part of the device was the Arduino MKR GSM 1400 board connected to an ADXL345 inertial motion unit sensor. The ADXL was used to calculate step count of a user based on activity and freefall interrupts. Additionally, the voltage of lantern battery was measured using an in-house voltage monitor to understand the discharging and charging patterns. The step count and battery voltage data were stored only if a significant change in the step count was detected. The LMSs were powered through a re-chargeable Li-Ion battery of 3.7V and a rating of 7.59Wh. The study was conducted in 2 phases. In phase 1 (03 July 2019 to 30 September 2019), data was collected from 60 lanterns and stored locally on SD card as well as communicated to a remote server via GSM. The time of data collection was recorded using GSM functionality. However, several GSM and MQTT failures were noted leading to loss of timestamp values as well as shorter battery lifetime due to re-transmission tries. In phase 2 (09 October 2019 to 18 December 2019), the design of lantern monitors was modified to circumvent these issues. The data was collected from 54 lanterns data and only stored locally on SD cards. The time of data collection was recorded using an external RTC clock connected to the Arduino board. Additionally, an internal watchdog timer was used to reset the device in case of failures. While certain failures persisted, the data yield was considerably higher than phase 1 of the study. The data from both phases of study is deposited here in LMS.zip. The cleaned dataset is available at <a href="https://doi.org/10.5281/zenodo.4269809">10.5281/zenodo.4269809</a>.  </li> <li>Individual appliance monitors (December 2018 to January 2020) - Individual appliance monitors (IAMs) were deployed in Uttargaya settlement, Nepal, in December 2018. The IAMs were simple, cost-effective and unobtrusive devices to collect data on the energy usage of connected appliances. The aim of the study was to understand energy consumption and usage patterns of different household appliances in grid-connected sub-metered displaced communities. The monitoring system consisted of 2 types of devices – Energenie MiHome Smart Plugs MIHO005 (referred to as the IAM) to sense data relating to power and voltage drawn by the connected appliance, and gateway nodes to collect data from IAM. The main component of the gateway node was a Raspberry Pi fitted with an Energenie ENER314-RT (receiver-transmitter) add-on board to allow the Pi to communicate with the smart plugs. The data collected by the RPi gateway was stored locally in an SD card as well as sent to a remote server hosted at Coventry University. The study was conducted until January 2020. The raw data from the study is deposited here in IAM.zip. The cleaned dataset is available at <a href="https://doi.org/10.5281/zenodo.4271714">10.5281/zenodo.4271714</a>.  </li> <li>Footfall monitoring systems (December 2018 to January 2020) - Seven footfall monitoring systems (FMSs)  were deployed alongside seven solar streetlights to measure step count of passers-by in the Uttargaya settlement, Nepal, in December 2018. The aim of the study was to understand the level of pedestrian movement in the area and evaluate the effect of streetlights on the level of activity. Therefore, the footfall monitors were deployed prior to commissioning of streetlights to gather baseline data. The footfall monitors consisted of a Raspberry Pi 3B, PiFace Real Time Clock and CAM008 70º night vision IR sensor to detect footfall. Upon detection, footfall count along with the direction of movement and the timestamp (measured from PiFace RTC) was stored onto an SD card and communicated to a remote server hosted at Coventry University.  The study was conducted until January 2020. The raw data from the study is deposited here in FMS.zip. The cleaned dataset is available at <a href="https://doi.org/10.5281/zenodo.4271730">10.5281/zenodo.4271730</a>.  </li> <li>Standalone Solar System for a Community Hall (June 2019 to March 2021) - A standalone solar system was deployed in a Community Hall in Nyabiheke camp, Rwanda in June 2019. The aim of the study was to understand the energy consumption behavior within a set location, and create an evidence base on the value of energy and its benefits for growing cooperatives and learning communities. The standalone system comprised of 2kW of solar panels and 12.2 kWh GEL battery storage capacity. Additional components included a Victron 150/35 charge controller and a Venus GX and 48/3000 MultiPlus Inverter. The system powered four AC 2-pin sockets, a 30 W entrance light, and six 30 W indoor lights. Each light and socket were individually metered and controlled via a remote monitoring unit. This allowed for quotas, maximum draws and periods of use to be remotely controlled. The study was conducted until March 2021. The raw data from the study is deposited here in Hall.zip. The cleaned dataset until March 2020 is available at <a href="https://doi.org/10.5281/zenodo.3949776">10.5281/zenodo.3949776</a>.  </li> <li>PV-battery Microgrid (July 2019 to March 2021) - A PV-battery Microgrid was deployed in Kigeme camp, Rwanda in July 2019. The microgrid powered a playground and two nursery buildings.  The aim of the study was to identify best practice in the construction, control and operation of a micro-grid as a shared resource, understand optimal design features for user interfaces that allow negotiation over energy priorities and needs and understand community priorities for energy in the context of early years education and the rate of growth in energy utilization. The micro-grid system comprised of a 2.5 kW of solar panels and 21.1 kWh GEL battery storage capacity. Additional components included a Victron 250/60 charge controller, Venus GX 48/1200 MultiPlus Inverter and BMV-700 series battery monitor. Each Nursery building had three classrooms (A, B and C) with separate entrances. Each classroom was fitted with an AC socket, five 10 Watt indoor lights and a 10 Watt outdoor entrance light. A spare socket was located in the first classroom of each nursery building (Classroom A). Two outdoor double sockets were installed at the playground, and fifteen 10 Watt lights were located in the roof structure. Three transmission line poles were fitted with three 10 Watt lights for safety and security purposes, which also enabled them to act as streetlights. Each light and socket was individually monitored and controlled via a programmable remote monitoring unit (RMU). Wireless AC smart meters were used to control and measure power consumption at the socket loads. These meters communicated with the RMU to receive commands and notified the RMU when a command had been received and to transmit usage data. Each light was connected to a CPE (customer-premises equipment) unit, with three lights per CPE, which communicated wirelessly with the RMU. The CPEs received information from the RMU on when to turn the lights on/off and set the brightness. The CPE also monitored the power consumption of the three lights.  The study was conducted until March 2021. The raw data from the study is deposited here in Microgrid.zip. The cleaned dataset until March 2020 is available at <a href="https://doi.org/10.5281/zenodo.3949776">10.5281/zenodo.3949776</a>.  </li> <li>Standalone solar streetlights (Nepal - June 2019 to October 2020; Rwanda - July 2019 to March 2021) - Seven advanced streetlights were installed by HEED in Khalte, Nepal, in July 2019. Four advanced streetlights and eight normal solar streetlights have been installed in Gihembe, Rwanda, in July 2019. Each advanced streetlight consisted of a solar streetlight and an electrical socket box for excess energy use. The aim of the study was to pilot community co-designed solar streetlights with ground-level sockets to demonstrate alternative energy governance models using new technologies to build community resilience and capacity. The solar light comprised a 300 Watt solar panel, Victron charge controller, 2 kWh li-ion batteries, reprogrammable 60 W LED light, Victron Venus GX for data logging, Victron BMV 700 series battery monitor, ground-level sockets/USB ports and a footfall sensor (only in Nepal). A Victron Battery Protect and remote relay on the Venus GX is used to control access to the secondary load to ensure that there is always sufficient energy to power the light.  The study was conducted until October 2020 in Nepal and March 2021 in Rwanda. The raw data from the study is deposited here in SL.zip. The cleaned dataset until March 2020 is available at <a href="https://doi.org/10.5281/zenodo.3947992">10.5281/zenodo.3947992</a>.</li> </ol>

opencc-by-4.0Jan 2022View details →

Capturing features of hourly-resolution energy models through statistical annual indicators

Dear colleagues, This is the official repository of the Task 7.4 of H2020 Locomotion project. Feel free to use our data by citing this work and comment about our work by referencing the main authors of it. The article explaining this work is under revision. it will be referenced as soon as posible. Python scripts ("create_inputs.txt" and "run_simulations.txt") creates the input files for EnergyPLAN. The second one runs iteratively EnergyPLAN to generate the outputs of combinations (which are saved in the "EU_Iterate_case.xlsx" file). Hourly distributions of demands and supply technologies are contained in the RAR file ("EUdist.rar") and "EU_start_v2_noFlex.txt" initialize the starting configuration of the European energy system. Those files are required to run EnergyPLAN. The PowerPoint file ("EnergyPLAN_instructions.pptx") explains the procedure to carry out the runs of combinations in Python/Excel. In case you couldn't properly do the combinations, the Excel file ("EU.xlsx") saves this information, so the steps of the approach could be followed from this point with the Excel file. We have used Power Query (Excel) to prepare the data for the next step of building the regression models. The Matlab file ("CreateRegressionModels.m") automatically generates the regression models for the European region of WILIAM (official model of the Locomotion project). Best regards, Gonzalo.

opencc-by-4.0Jan 2022View details →

Balancing profitability of energy production, societal impacts and biodiversity in offshore wind farm design

Dataset related to the article: Virtanen, E.A., Lappalainen, J., Nurmi, M., Viitasalo, M., Tikanmäki, M., Heinonen, J., Atlaskin, E., Kallasvuo, M., Tikkanen, H., Moilanen, A. (2022) Balancing profitability of energy production, societal impacts and biodiversity in offshore wind farm design. Renewable and Sustainable Energy Reviews 158, 112087. Dataset includes suitability maps for offshore windfarms, where priority values are scaled between 0-1 (note the reversed value scale): analysis solution (A) economy, (B) society, (C) biodiversity, (D) restrictions, (E) A+B+C without restrictions and (F) A+B+C with restrictions. Dataset includes also the conflict map (and R script), where each three main solutions (A, B, C) are mapped onto an RGB color composite map.  Additional details can be found from the published article: <a href="https://doi.org/10.1016/j.rser.2022.112087">https://doi.org/10.1016/j.rser.2022.112087</a>

opencc-by-4.0Nov 2021View details →

Code for data and figures published in "Solar energy as an early just transition opportunity for coal-bearing states in India"

The following code and data were used to generate the figures in the article "Solar energy as an early just transition opportunity for coal-bearing states in India". The article was published in Environmental Research Letters (<a href="https://iopscience.iop.org/article/10.1088/1748-9326/ac5194">https://iopscience.iop.org/article/10.1088/1748-9326/ac5194</a>) The code is written in R. Before running the Rmd file, create a folder called "Data" and store all the files there, except the Rmd file.

opencc-by-4.0Jan 2022View details →

Supplementary Data: A peer-to-peer market mechanism incorporating multi-energy coupling and cooperative behaviors

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>): A peer-to-peer market mechanism incorporating multi-energy coupling and cooperative behaviors   Before using the dataset, please 1. refer to the article for the details of the data used in the case study, 2. read README.txt for the structure of the dataset.   Please also kindly cite the journal article when using the dataset.

dryad40/100

Incorporating effects of age on energy dynamics predicts non-linear maternal allocation patterns in iteroparous animals

Iteroparous parents face a trade-off between allocating current resources to reproduction versus maximizing survival to produce further offspring. Optimal allocation varies across age, and follows a hump-shaped pattern across diverse taxa, including mammals, birds and invertebrates. This non-linear allocation pattern lacks a general theoretical explanation, potentially because most studies focus on offspring number rather than quality and do not incorporate uncertainty or age-dependence in energy intake or costs. Here, we develop a life history model of maternal allocation in iteroparous animals. We identify the optimal allocation strategy in response to stochasticity when energetic costs, feeding success, energy intake, and environmentally-driven mortality risk are age-dependent. As a case study, we use tsetse, a viviparous insect that produces one offspring per reproductive attempt and relies on an uncertain food supply of vertebrate blood. Diverse scenarios generate a hump-shaped allocation: when energetic costs and energy intake increase with age; and also when energy intake decreases, and energetic costs increase or decrease. Feeding success and mortality risk have little influence on age-dependence in allocation. We conclude that ubiquitous evidence for age-dependence in these influential traits can explain the prevalence of non-linear maternal allocation across diverse taxonomic groups.

opencc-zeroJan 2022View details →

Life cycle-based environmental impacts of energy scenarios - additional data

This data set documents additional results of the paper "Life cycle-based environmental impacts of energy system transformation strategies for Germany: Are climate and environmental protection conflicting goals?" (Tobias Naegler and co-authors, published in Energy Reports (2020), https://doi.org/10.1016/j.egyr.2022.03.143). It shows life cycle-based environmental impacts for 10 different transformation strategies for the German energy and transport system.

Categorical variables based on cross country household survey on energy consumption

The data used in this file was collected via two large-scale surveys conducted in Italy, Switzerland and the Netherlands. A total of 6,138 responses were recorded, containing information on socio-demographic and socio-psychological characteristics, dwelling and household characteristics, technologies and energy services used, and their metered electricity consumption. There were a large number of missing responses for metered electricity consumption in the Netherlands, leading to an under-representation of data from this country. The survey responses were used to construct newly defined energy efficiency indicators, and energy service indicators. This allows two distinct factors to be separated: service consumption, and energy efficiency relative to the demanded service. Firstly, dwelling characteristics and survey responses related to energy services (e.g. floorspace, ownership of specific appliances and number of lightbulbs), were regressed to the collected metered electricity data. For each household, this allowed us to calculate the expected lighting and appliance electricity demand based on the level service that the household demanded, which is referred to as lighting and appliance service demand indicators. The idea is that a larger house, or a house with more appliances for example is expected to use more electricity. Relative to this expected electricity demand energy efficiency can be calculated. All variables are categorised in categorical variables deducted based on the questions asked in the two surveys. The survey responses were clustered based on the lighting service demand, appliance service demand and the efficiency gap (k-means clustering with Jaccard dissimilarity measure) which is described in Edelenbosch, Miu et al (2022). Translating observed household energy behaviour to agent-based technology choices in an integrated modelling framework. Iscience (accepted).

Harmonized remodeled energy system transformation strategies for Germany - additional data

This dataset compares 10 different scenarios for the transformation of the German energy system by 2050. These scenarios were used in the <a href="https://www.innosys-projekt.de">InNOSys project</a> as a starting point for a multidimensional impact assessment and evaluation of different transformation strategies (see also <a href="https://www.mdpi.com/2071-1050/13/9/5217">https://www.mdpi.com/2071-1050/13/9/5217</a>). As a source of inspiration for these scenarios, 10 different transformation strategies were used, as published for Germany in 2012-2018. However, for the present document, the original scenarios were re-modeled in a harmonized way. An additional documentation of the scenarios is also available on ZENODO.

Data for Horowitz et al. (2022). The Energy System Transformation Needed to Achieve the U.S. Long-Term Strategy

Data repository for Horowitz, et al. 2022, "The Energy System Transformation Needed to Achieve the U.S. Long-Term Strategy" All data shown in the paper is included here. Data includes results from: 1. U.S. LTS GCAM scenarios (labeled "GCAM") 2. U.S. LTS NEMS scenarios (labeled "NEMS") 3. Princeton's Net-Zero America scenarios (labeled "NZA")a 4. Williams et al. (2020) scenarios (labeled "Williams")b Results are included in the following files: 1. waterfall.csv - Figure 1. Emissions decomposition by scenario. GCAM only. 2. clean_fuels.csv - Figure 2. Clean fuel (bioliquids, biogas, and blue/green hydrogen) consumption. All models. 3. coal.csv - Figure 2. Primary energy consumption of coal without CCS. All models. 4. ZEV_stock.csv - Figure 2. Zero-emission vehicle percentage of light-duty vehicle stock. All models. 5. ghg_by_type.csv - Figure 3. Greenhouse gas emissions by gas/source. GCAM only.   Reference: a) Larson, E., Greig, C., Jenkins, J.,Mayfield, E., Pascale, A., Zhang, C., Drossman, J., Williams, R., Pacala, S., Socolow, R., et al. (2021). Net-zero America: Potential pathways, infrastructure, and impacts. (Princeton University). b) Williams, J. H., Jones, R. A., Haley, B., Kwok, G., Hargreaves, J., Farbes, J., & Torn, M. S. (2021). Carbon-Neutral Pathways for the United States. AGU Advances, 2, e2020AV000284. <a href="https://doi.org/https://doi.org/10.1029/2020AV000284">https://doi.org/https://doi.org/10.1029/2020AV000284</a>.

opencc-by-4.0Feb 2021View details →

Carbon and energy Eddy-covariance fluxes dataset collected at La Guette peatland (23 ha, Loiret, France)

Fluxes and energy data measured by Eddy-covariance on La Guette peatland (ec1). Measurements start on 20-01-2017 and are regularly updated with new data. Data include carbon dioxide fluxes (CO2, µmol/m²/s), methane fluxes (CH4, µmol/m²/s), sensible heat fluxes (H, W/m²), latent heat fluxes (LE, W/m²) and evapotranspiration (ETR, mm/h). Zip file contain : <ul> <li>metadata file (TOUR_en.json) which describe stations, sensors, variables and process</li> <li>csv file contain time series data for all variables by station</li> </ul> Additional information on the measurement can be found in this website : <a href="https://data-snot.cnrs.fr/data-access/">https://data-snot.cnrs.fr/data-access/</a> We also recommend to contact sno-tourbieres to talk about data acquisition and use : <a href="mailto:contact.sno-tourbieres@cnrs-orleans.fr">contact.sno-tourbieres@cnrs-orleans.fr</a>

opencc-by-4.0Mar 2021View details →

Carbon and energy Eddy-covariance fluxes dataset collected at Frasne peatland (192ha, Jura Mountains, France)

luxes and energy data measured by Eddy-covariance at Frasne peatland (ec1). Measurements start on 20-07-2018 and are regularly updated with new data. Data include carbon dioxide fluxes (CO2, µmol/m²/s), methane fluxes (CH4, µmol/m²/s), sensible heat fluxes (H, W/m²), latent heat fluxes (LE, W/m²) and evapotranspiration (ETR, mm/h). Zip file contain : <ul> <li>metadata file (TOUR_en.json) which describe stations, sensors, variables and process</li> <li>csv file contain time series data for all variables by station</li> </ul> Additional information on the measurement can be found in this website : <a href="https://data-snot.cnrs.fr/data-access/">https://data-snot.cnrs.fr/data-access/</a> We also recommend to contact sno-tourbieres to talk about data acquisition and use : <a href="mailto:contact.sno-tourbieres@cnrs-orleans.fr">contact.sno-tourbieres@cnrs-orleans.fr</a>

opencc-by-4.0Jan 2021View details →

Data and code: Climate policy accelerates structural changes in energy employment

The file contains code to create the figures used in main text and supplementary information of the paper Climate policy accelerates structural changes in energy employment. To run the RMD file and see the resulting figures, press Knit on R studio (requires the package knitr), or else see the attached HTML file, already created through such a process.

Evaluating the Usability of Open Source Frameworks in Energy System Modelling (Supplementary Material)

Dataset and source code for analysis of the Energy System Modelling Usability Testing (ESMUT) procedure applied in the open_MODEX project. This is supplementary material for  the publication: <pre>Berendes et al. (2022). Evaluating the Usability of Open Source Frameworks in Energy System Modelling. Renewable and Sustainable Energy Reviews. DOI: <a href="https://doi.org/10.1016/j.rser.2022.112174">https://doi.org/10.1016/j.rser.2022.112174</a></pre>