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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.

The dataset contains two separate files: NREL_Trainset40000.mat and NREL_Testset10000.mat. The stored input enviormental and operational parameters are: Significant wave height, m (Hs), peak period, s (Tp), wave direction, deg (Wave_dir); Wind speed, m/s (Vw_mean, Vw_std), wind direction, deg (Wdir_mean, Wdir_std); Turbine rotational speed, rpm (Rpm_mean, Rpm_std), blade pitch, deg (Pitch_mean, Pitch_std), turbine yaw angle, deg (Yaw_mean, Yaw_std). The output of the simulations includes the time series, sampled at 50 Hz, of the reaction force and bending moments at the mudline: Fzz, N Mxx, Nm Myy, Nm contact: nandar.hlaing@uliege.be {"references": ["OpenFAST. Availabe at https://github.com/OpenFAST/openfast.\"", "Jonkman, K., Butterfield, S., Musial, W., & Scott, G. (2009). Definition of a 5-MW Reference Wind Turbine for Offshore System Development. 1617 Cole Boulevard, Golden, Colorado 80401-3393: National Renewable Energy Laboratory.", "Jonkman, J., & Musial, W. (2010). Offshore Code Comparison Collaboration (OC3) for IEA Task 23 Offshore Wind Technology and Deployment. 1617 Cole Boulevard, Golden, Colorado 80401-3393: National Renewable Energy Laboratory."]} - Funded by Belgian Energy Transition Fund (FPS Economy) through "PhairywinD" project. - Computing equipment facilitated by "Consortium des ��quipements de Calcul Intensif".

Abstract. Wake steering is a technique that optimizes the energy production of a wind farm by employing yaw control to misalign upstream turbines with the incoming wind direction. This work highlights the important dependence between wind direction variations and wake steering optimization. The problem is formalized over time as the succession of multiple steady-state yaw control problems interconnected by the rotational constraints of the turbines and the evolution of the wind. Then, this work proposes a reformulation of the yaw optimization problem of each time step by augmenting the objective function by a new heuristic based on a wind prediction. The heuristic acts as a penalization for the optimization, encouraging solutions that will guarantee future energy production. Finally, a synthetic sensitivity analysis of the wind direction variations and wake steering optimization is conducted. Because of the rotational constraints of the turbines, as the magnitude of the wind direction fluctuations increases, the importance of considering wind prediction in a steady-state optimization is empirically demonstrated. The heuristic proposed in this work greatly improves the performance of controllers and significantly reduces the complexity of the original sequential decision problem by decreasing the number of decision variables.

Walkability has become a research topic of great concern for preserving public health, especially in the era of the COVID-19 outbreak. Today more than ever, urban and transport policies, constrained by social distancing measures and travel restrictions, must be conceptualized and implemented with a particular emphasis on sustainable walkability. Most of the walkability models apply observation and subjective methods to measure walkability, whereas few studies address walkability based on sense perception. To fill this gap, we aim at investigating the perceived neighbourhood walkability (PNW) based on sense perception in a neighbourhood of Brussels. We designed a survey that integrates 22 items grouped into 5 dimensions (cleanness, visual aesthetics, landscape and nature, feeling of pressure, feeling of safety), as well as the socio-demographic attributes of the participants. Using various statistical methods, we show that socio-demographics have almost no effects on perceived neighbourhood walkability. Nonetheless, we found significant differences between groups of different educational backgrounds. Furthermore, using a binomial regression model, we found strong associations between PNW and at least one item from each grouping dimension. Finally, we show that based on a deep neural network for classification, the items have good predictive capabilities (78% of classification accuracy). These findings can help integrate sense perception into objective measurement methods of walkable environments. Additionally, policy recommendations should be targeted based on differences of perception across socio-demographic groups.

A case study dealing with long-term voltage instability in systems hosting active distribution networks (DN) is reported in this paper. It anticipates future situations with high penetration of dispersed generation (DG), where the latter are used to keep distribution voltages within desired limits, in complement to load tap changers. The interactions between transmission and active DN are investigated on a 3108-bus test system. It involves transmission grid, large generators, and 40 DN, each with DG steered by a controller inspired by model predictive control. The reported simulations show the impact of distribution network voltage restoration, as well as the benefit of load voltage reduction actuated by the dispersed generators.

Net zero energy buildings and positive energy buildings are gaining more and more interest. This paper evaluates the impact of the integration of a battery in a positive energy building used to increase its self-consumption of electricity. Parametric studies are carried out by varying the building envelope characteristics, the power supply system, the climate, the lighting and appliances profiles, the roof tilt angle, the battery size and the electricity tariffs, leading to 3200 cases. The analysis is performed on an annual basis in terms of self-consumption and self-production rate and payback period. It is shown that the battery size leading to the minimum payback period within the input range is comprised between 2.6 and 4.5 kWh. The lowest payback periods, (~7 years), are reached with a well-insulated building envelope, a high lightning and appliance consumption, a low feed-in tariff and a 3.7 kWh battery. Finally, simple correlations (based on the feed-in tariff, the annual electrical consumption and production) to predict the optimal size of battery and the lowest payback period are proposed.

This paper proposes a systematic framework to assess the complementarity of renewable resources over arbitrary geographical scopes and temporal scales which is particularly well-suited to exploit very large data sets of climatological data. The concept of critical time windows is introduced, and a spatio-temporal criticality indicator is proposed, consisting in a parametrised family of scalar indicators quantifying the complementarity between renewable resources in both space and time. The criticality indicator is leveraged to devise a family of optimisation problems identifying sets of locations with maximum complementarity under arbitrary geographical deployment constraints. The applicability of the framework is shown in a case study investigating the complementarity between the wind regimes in continental western Europe and southern Greenland, and its usefulness in a power system planning context is demonstrated. Besides showing that the occurrence of low wind power production events can be significantly reduced on a regional scale by exploiting diversity in local wind patterns, results highlight the fact that aggregating wind power production sites located on different continents may result in a lower occurrence of system-wide low wind power production events and indicate potential benefits of intercontinental electrical interconnections.