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This dataset contains the underlying data for the following publication: Doukas, H., Spiliotis, E., Jafari, M. A., Giarola, S. & Nikas, A. (2021). Low-cost emissions cuts in container shipping: Thinking inside the box. Transportation Research Part D: Transport and Environment, 94, 102815, https://doi.org/10.1016/j.trd.2021.102815.

This data set contains the essential files used as input for the analysis, intermediate files produced during the analysis, and the key output fields. The code of the analysis is available here: https://github.com/VUB-HYDR/2021_Thiery_etal_Science Input fields: - isimip.zip: Postprocessed ISIMIP2b simulation output. This data set is very similar to the data presented in Lange et al. (2020 Earth's Future) but includes selected additional impact models and scenarios (notably RCP8.5). This data set also includes the gridded population data. - GMT_50pc_manualoutput_4pathways.xlsx: Global mean temperature anomaly trajectories from the IPCC SR15 - wcde_data.xlsx: postprocessed cohort size data originally obtained from the Wittgenstein Centre Human Capital Data Explorer. - WPP2019_MORT_F16_1_LIFE_EXPECTANCY_BY_AGE_BOTH_SEXES.xlsx: Postprocessed life expectancy data originally obtained from the UNited Nations World Population Programme Intermediate files *only use if you're interested in reproducing the results*: - workspaces.zip: Postprocessed ISIMIP2b simulation output. These matlab workspaces contain data on land area annually exposed to extreme events which is stored in a format designed to speed up the analysis. - mw_isimip.mat: ISIMIP2 simulations metadata (e.g. model, gcm and rcp name per simulation) - mw_countries.mat: information on the countries used in the analysis (e.g. border polygon coordinates) - mw_exposure.mat: age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic.mat: pre-industrial control age-dependent exposure computed from the ISIMIP and population data - mw_exposure_pic_coldwaves.mat: pre-industrial control age-dependent exposure to coldwaves computed from the ISIMIP and population data Output of the analysis: - mw_output.mat: Matlab workspace containing all variables produced during the analysis presented in thepaper. Use this file if you wish to look up certain numbers or want to use the study results for further analysis.

Thermally integrated pumped-thermal electricity storage (TI-PTES) offers the opportunity to store electricity as thermal exergy at a large scale, and existing studies are primarily focused on TI-PTES systems based on high-temperature thermal energy storage. This paper presents a thermo-economic analysis of a “cold TI-PTES” system which converts electricity into cold energy using a vapor compression refrigeration (VCR) unit and stores it at sub-ambient temperatures during the charging process, and generates electricity by using an organic Rankine cycle (ORC) working between the sub-ambient temperature and an external low-grade heat source during the discharging process. The effects of key parameters, i.e., mass flowrate and temperature of the storage medium, ORC evaporation temperature, component efficiencies, and pinch-point temperature differences, on the system performance are evaluated based on a whole-system thermo-economic model. The results reveal that the roundtrip efficiency and levelized cost of storage (LCOS) of the system increases while the electrical energy storage capacity decreases as the temperatures of the two cold storage tanks approach each other. When the temperature of the cold storage tank 1 rises from 1 °C to 8 °C while the cold storage tank 2 remains as 13 °C, there is an increase of 25% and 20% in the roundtrip efficiency and LCOS respectively while the energy storage capacity decreases by 69%. A roundtrip efficiency of 0.74 and LCOS of 0.32 $/kWh are achieved with a heat source temperature of 85 °C, using a mass flowrate and temperature of the cold storage medium of 50 kg/s and 1 °C. Furthermore, any change in cold storage medium mass flowrate changes both electrical energy storage capacity and power output by the same proportions. With a continuous high-flowrate external heat source, the LCOS can be as low as 0.17 $/kWh. By providing sufficient heat from an external heat source, the proposed system possesses a high potential for medium-to-large scale energy storage with a unique hybrid ...

This work addresses the problem of energy consumption time series forecasting. In our approach, a set of time series containing energy consumption data is used to train a single, parameterised prediction model that can be used to predict future values for all the input time series. As a result, the proposed method is able to learn the common behaviour of all time series in the set (i.e., a fingerprint) and use this knowledge to perform the prediction task, and to explain this common behaviour as an algebraic formula. To that end, we use symbolic regression methods trained with both single- and multi-objective algorithms. Experimental results validate this approach to learn and model shared properties of different time series, which can then be used to obtain a generalised regression model encapsulating the global behaviour of different energy consumption time series.

AbstractGlobal impact models represent process-level understanding of how natural and human systems may be affected by climate change. Their projections are used in integrated assessments of climate change. Here we test, for the first time, systematically across many important systems, how well such impact models capture the impacts of extreme climate conditions. Using the 2003 European heat wave and drought as a historical analogue for comparable events in the future, we find that a majority of models underestimate the extremeness of impacts in important sectors such as agriculture, terrestrial ecosystems, and heat-related human mortality, while impacts on water resources and hydropower are overestimated in some river basins; and the spread across models is often large. This has important implications for economic assessments of climate change impacts that rely on these models. It also means that societal risks from future extreme events may be greater than previously thought.

Large-scale photovoltaic (PV) integration to the network necessitates accurate modeling of PV system dynamics under solar irradiance changes and disturbances in the power system. Most of the available PV dynamic models in the literature are scope-specific, neglecting some control functions and employing simplifications. In this paper, a complete dynamic model for two-stage PV systems is presented, given in entirely state-space form and explicit equations that takes into account all power circuit dynamics and modern control functions. This is a holistic approach that considers a full range of ancillary services required by modern grid codes, supports both balanced and unbalanced grid operation, and accounts for the discontinuous conduction mode of the dc/dc converter of the system. The proposed dynamic model is evaluated and compared to other approaches based on the literature, against scenarios of irradiance variation, voltage sags, and frequency distortion. Simulation results in MATLAB/Simulink indicate high accuracy at low computational cost and complexity.