Abstract

Abstract
Context. The low wind effect (LWE) occurs at the aperture of 8-meter class telescopes when the spiders holding the secondary mirror get significantly cooler than the air. The effect creates phase discontinuities in the incoming wavefront at the location of the spiders. Under the LWE, the wavefront residuals after correction of the adaptive optics (AO) are dominated by low-order aberrations, pistons, and tip-tilts, contained in the pupil quadrants separated by the spiders. Those aberrations, called petal modes, degrade the AO performances during the best atmospheric turbulence conditions. Ultimately, the LWE is an obstacle for high-contrast exoplanet observations at a small angular separation from the host star. Aims. We aim to understand why extreme AO with a Shack-Hartmann (SH) wavefront sensor fails to correct for the petal tip and tilt modes, while these modes imprint a measurable signal in the SH slopes. We explore if the petal tip and tilt content of the LWE can be controlled and mitigated without an additional wavefront sensor. Methods. We simulated the sensitivity of a single subaperture of a SH wavefront sensor in the presence of a phase discontinuity across this subaperture. We explored the effect of the most important parameters: the amplitude of the discontinuity, the spider thickness, and the field of view. We then performed end-to-end simulations to reproduce and explain the behavior of extreme AO systems based on a SH in the presence of the LWE. We then evaluated the efficiency of a new mitigation strategy by running simulations, including atmosphere and realistic LWE phase perturbations. Results. For realistic parameters (i.e. a spider thickness at 25% of a SH subaperture, and a field of view of 3.5 lambda/d), we find that the sensitivity of the SH to a phase discontinuity is dramatically reduced, or even reversed. Under the LWE, a nonzero curl path is created in the measured slopes, which transforms into vortex-structures in the residuals when the loop is closed. While these vortexes are easily seen in the residual wavefront and slopes, they cannot be controlled by the system. We used this understanding to propose a strategy for controlling the petal tip and tilt modes of the LWE by using the measurements from the SH, but excluding the faulty subapertures. Conclusions. The proposed mitigation strategy may be of use in all extreme AO systems based on SH for which the LWE is an issue, such as SPHERE and GRAVITY+.

Abstract
Although electricity is a clean and relatively safe form of energy when it is used, the generation and transmission of electricity have severe effects on the environment. An alternative to diminish the polluting emissions released by the generating units is the Emission Constrained Economic Dispatch (ECED). This is an optimization problem where the total fuel cost is minimized while treating emissions as a constraint with a pre-specified limit. Usually, the fuel cost and emission functions of the generating units must be experimentally derived, introducing then uncertainties in the obtained models. However, these uncertainties are often neglected and the ECED problem is solved considering the coefficients of the functions involved as exact (totally known) values. In this investigation we analyzed the effect of the uncertainties associated to the experimental derivation of the input–output curves of thermal power plants. Particularly, when polynomial models are fitted through multiple linear regression, we proposed an approach that, based on the respectively prediction intervals, can provide solutions immunized, in some sense, against variability in the coefficients estimates. We tested the proposed approach in a real system from the Chilean electrical power network. For the analyzed system we noted that, when uncertainties are not considered, the deterministic optimal solutions can be environmentally infeasible in some scenarios; whereas solutions obtained through the proposed approach, can significantly diminish the risk of environmental violations. The robustness of the prediction interval-based solutions was obtained with a negligible increase of the total fuel cost in all the cases studied.

Abstract

Abstract
Covid-19 posed several challenges to all organisations in general and to social solidarity cooperatives in particular. However, the challenges faced by these cooperatives have unique features arising from their special characteristics compared to other types of cooperatives. Therefore it is vital to study these challenges and the impacts of covid-19. This study has as main goal to understand those challenges and their impact. An exploratory study was undertaken by applying 11 interviews to 11 social solidarity cooperatives. The cooperatives were chosen to be heterogeneous among the existent cooperatives in Portugal. This study corresponds to the first phase of a project that is still underway. This article presents the main results of the content analysis of the data collected from the interviews. Data show cooperatives could promptly adapt and continue their mission under pressure from the pandemic despite the first difficulties encountered in a new and unknown situation, showing a capacity to adapt and serve their members. However, these members were also submitted to several increasing and new challenges. The adaptations were possible due to legal changes in the work organisation law, from layoff to telework, government support involving financial programs, VAT, and other tax relaxation, as well as due to human resources reorganisation and the cooperatives' staff positive attitude towards the difficulties (both leaders and general workers). Differences between the social solidarity cooperatives under study concerning digital technologies showed that those already having some infrastructure had minor adapting difficulties.

Abstract
Accurate ultra-short-term PV power forecasting is essential for the power system with a high proportion of renewable energy integration, which can provide power fluctuation information hours ahead and help to mitigate the interference of the random PV power output. Most of the PV power forecasting methods mainly focus on employing local ground-based observation data, ignoring the spatial and temporal distribution and correlation characteristics of solar energy and meteorological impact factors. Therefore, a novel ultra-short-term PV power forecasting method based on the satellite image data is proposed in this paper, which combines the spatio-temporal correlation between multiple plants with power and cloud information. The associated neighboring plant is first selected by spatial-temporal cross-correla-tion analysis. Then the global distribution information of the cloud is extracted from satellite images as additional inputs with other general meteorological and power inputs to train the forecasting model. The proposed method is compared with several benchmark methods without considering the information of neighboring plants. Results show that the proposed method outperforms the benchmark methods and achieves a higher accuracy at 4.73%, 10.54%, and 4.88%, 11.04% for two target PV plants on a four-month validation dataset, in terms of root mean squared error and mean absolute error value, respectively.