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Creating a century-long homogenized near-surface wind speed (WS) observation dataset is essential to improve our knowledge about the uncertainty and causes of WS stilling and recovery. We rescued paper-based WS records dating back to the 1920s at 13 stations in Sweden and established a four-step homogenization procedure to generate the first 10-member centennial homogenized WS dataset (HomogWS-se) for community uses among climatology, ecology, hydrology and energy industry. HomogWS-se can be used to study the WS variability and change, assess climate reanalysis, and constrain climate simulations for better future projection of changes in the WS and wind energy potential. HomogWS-se contains 13 individual text files with 10-member century-long homogenized monthly WS series, as well as the member-mean series. {"references": ["Zhou, C., C. Azorin-Molina, E. Engstr\u00f6m, L. Wern, S. Hellstr\u00f6m, and D. Chen, 2022: A century-long homogenized dataset of near-surface wind speed observations since 1925 rescued in Sweden. Earth Syst. Sci. Data, 1-24, 10.5194/essd-2022-29."]}

Dataset from the outdoor characterization of a B Series module from Insolight at the rooftop of the Instituto de Energía Solar - Universidad Politécnica de Madrid. These are measurements of a module of the same type as “Outdoor monitoring data of an Insolight B-series module - CPV sub-module” however, in the previous measurements the module was mounted on a two-axis tracker to benchmark its performance, while in these measurements, the module’s integrated planar micro tracking system was used. This data was presented at IEEE PVSC 46 in June 2019 in Chicago. See preprint of conference article. Monitoring campaign: Location: 40.453°N, -3.727°E. Instituto de Energía Solar, Universidad Politécnica de Madrid. 28040 Madrid, Spain. Fixed Mounting Angle: Due South, Slope Angle = 30° Starting date: 30 May 2019 End date: 14 June 2018 Description of data file: Data files format: single comma-separated text file; headers in first row; all of the following parameters; order below is the same as order in file Measurement time: the vector of times represents the times at which the Insolight module firmware sampled the current values of the III-V and Si outputs (measured simultaneously). Date Time (dd/mmm/yyyy HH:MM:SS): time in CEST / UTC+2 Measured meteorological data: these values are measured directly by the IES meteorological station with 1-minute resolution. They have been re-interpolated to match the measurement times. DNI (W/m2): direct normal irradiance as measured by a Normal Incidence Pyrheliometer from Eppley on a solar tracker. Spectral Range: 250-3000 nm. Field of view: 5° DNI_Top (W/m2): equivalent direct normal irradiance as measured by a top component cell of a lattice-matched III-V triple-junction cell in the ICU-3J35 Triband Spectro-heliometer from Solar Added Value on a solar tracker. Spectral range: 300 - 680 nm. Field of view: 5.7º DNI_Mid (W/m2): equivalent direct normal irradiance as measured by a middle component cell of a lattice-matched III-V triple-junction cell in the ICU-3J35 Triband Spectro-heliometer from Solar Added Value on a solar tracker. Spectral range: 680 - 900 nm. Field of view: 5.7º GNI (W/m2): global normal irradiance at the aperture plane as measured with a pyranometer on a solar tracker. Spectral range: 305 – 2800 nm. G(41°) (W/m2): Global Inclined Irradiance as measured with a pyranometer mounted facing due south and at a slope angle of 41° (near to local latitude). Spectral range: 305 – 2800 nm. T_Amb (°C): ambient temperature Wind Speed (m/s): wind speed Wind Dir. (m/s): wind direction Processed meteorological data: these values are calculated from the above meteorological data and provided for convenience DII (W/m2): Direct Inclined (plane of array) Irradiance corresponding to the module slope angle has been calculated using the sun’s known declination and hour angle from the time. GII (W/m2): The Global Inclined (plane of array) Irradiance is calculated by first calculating the DII(41°), that is the DII corresponding to the G(41°) measurement, and finding the Diffuse Inclined Irradiance Diff(41°) = G(41°) – DII(41°). It is assumed that the Diffuse Inclined Irradiance at 41° and 30° is equal, so GII = DII + Diff(41°). SMR_Top_Mid (n.d.): “Spectral Matching Ratio”. This is the ratio between DNI_Top and DNI_Mid. A value of unity indicates a spectrum that is equivalent to AM1.5D with regards to the energy balance between top and middle subcells. Measured module data: The module was placed in a short-ciruit condition and allowed to track using its integrated tracking system. The short circuit current was measured using shut resistors and integrated A/D channels. This hybrid module features both III-V micro cells (under concentration, with planar microtracking) and large area silicon solar cells (for diffuse capture). ISC_measured_IIIV (A): ISC_measured_Si (A) Estimated module data: As is explained in the IEEE PVSC 46 manuscript (see Preprint) the following values are estimated using the previously listed measured data. T_Backplane (°C) PMP_estimated_IIIV (W) PMP_estimated_Si (W)

Sheets with information on the FOODRUS KPIs, including: general description, calculation methodology, and observations.

Editorial Complex Optimization and Simulation in Power Systems

The complex interaction between city and climate crisis is converting design-based disciplines from deterministic to flexible approaches. In this regard, Decision-Making Under Deep Uncertainty (DMDU) methods and operational strategies can be valuable support mechanisms to cope with the emerging climate fragilities of urban systems. In light of recent advances in the field of adaptive approaches, this paper discusses key concepts, current limitations and the potential to introduce the DMDU in the method and practices of regenerative design. Our critical discussion aims to restore the designer’s role within the DMDU and to reduce current and future climate fragilities in European cities.

Abstract To achieve the EU climate and energy objectives, a transition towards a future sustainable energy system is needed. The integration of the huge potential for industrial waste heat recovery into smart energy system represents a main opportunity to accomplish these goals. To successfully implement this strategy, all the several stakeholders' conflicting objectives should be considered. In this paper an evolutionary multi-objective optimization model is developed to perform a sustainability evaluation of an energy system involving an industrial facility as the waste heat source and the neighbourhood as district heating network end users. An Italian case study of heat recovery from a steel casting facility shows how the model allows to properly select the district heating network set of users to fully exploit the available waste energy. Design directions such as the thermal energy storage capacity can be also provided. Moreover, the model enables the analysis of the trade-off between the stakeholders’ different perspectives, allowing to identify possible win-win solutions for both the industrial sector and the citizenship.

This document contains the Strategic Research Agenda to Innovation on Blue Energy developed in the framework of the PELAGOS project (D.4.2.1). Relying on both the current Research & Innvation guidelines and priorities established at European level for exploitating in the most effective way the potential of Ocean Energy and the knowledge acquired the activities of PELAGOS project at Mediterranean level, this document considers the strategic focus areas related to the most promising Marine Renewables Energy technologies in the Mediterranean area.

Particle receivers are gaining importance in the field of Concentrating Solar Power (CSP) due to the high temperature that particles can achieve without degradation. Several researchers are studying the potential of this technology by means of system analyses, which need simple and light models of the system. This study presents two simple models for the particle receiver. The simplest model is a correlation obtained by fitting the results calculated with a more complex receiver model simulated in CFD. The other model is a 1D model, which is benchmarked against the same CFD results. Although both models achieve high coefficient of determination, R2, when compared to CFD results, the 1D model seems to provide more accurate results (especially during sunsets and sunrises). Both models are integrated into a tecno-economic model developed in previous work. The LCOE obtained with the 1D model is between 7% and 10% greater than the one obtained with the correlation.