Abstract
Increasing the penetration of renewable resources has aggravated the operational flexibility at distribution level. In this study, a flexibility-oriented scheduling of microgrids (MGs) is suggested to reduce the power fluctuations in distribution feeders caused by the high penetration of wind turbines (WTs) in MGs. A flexibility constraint as viable and practical solution is used in MG scheduling to address this challenge. The presented scheduling model, implemented using mixed integer linear programming (MILP) and a stochastic framework, exercises risk constraints to capture the uncertainties associated with wind turbines, loads and market prices. The effectiveness of the model is investigated on a MG with high penetration of WTs in the presence of demand response (DR) and energy storage systems (ESSs). Numerical studies show the influence of risk parameters' changing on operation costs. In addition, the flexibility constraint mitigates the sharp variation of the net load at distribution level, which improves the flexibility of the distribution system.

Abstract
The importance of price forecasting has gained attention over the last few years, with the growth of aggregators and the general opening of the European electricity markets. Market participants manage a tradeoff between, bidding in a lower price market (day-ahead), but with typically higher volume, or aiming for a lower volume market but with potentially higher returns (balance energy market). Companies try to forecast the extremes of revenues or prices, in order to manage risk and opportunity, assigning their assets in an optimal way. It is thought that in general, electricity markets have quasi-deterministic principles, rather than being based on speculation, hence the desire to forecast the price based on variables that can describe the outcome of the market. Many studies address this problem from a statistical approach or by performing multiple-variable regressions, but they very often focus only on the time series analysis. In 2019, the Loss of Load Probability (LOLP) was made available in the UK for the first time. Taking this opportunity, this study focusses on five LOLP variables (with different time-ahead estimations) and other quasi-deterministic variables, to explain the price behavior of a multi-variable regression model. These include base production, system load, solar and wind generation, seasonality, day-ahead price and imbalance volume contributions. Three machine-learning algorithms were applied to test for performance, Gradient Boosting (GB), Random Forest (RF) and XGBoost. XGBoost presented higher performance and so it was chosen for the implementation of the real time forecast step. The model returns a Mean Absolute Error (MAE) of 7.89 £/MWh, a coefficient of determination (R2 score) of 76.8% and a Mean Squared Error (MSE) of 124.74. The variables that contribute the most to the model are the Net Imbalance Volume, the LOLP (aggregated), the month and the De-rated margins (aggregated) with 28.6%, 27.5%, 14.0%, and 8.9% of weight on feature importance respectively.

Abstract

Abstract

Abstract
The digital terrain model (DTM), the representation of earth's surface at regularly spaced intervals, is the first input in the computational modelling of atmospheric flows. The ability of computational meshes based on high- (2 m; airborne laser scanning, ASL), medium- (10 m; military maps, Mil) and low-resolution (30 m; Shuttle Radar Topography Mission, SRTM) DTMs to replicate the Perdigao experiment site was appraised in two ways: by their ability to replicate the two main terrain attributes, elevation and slope, and by their effect on the wind flow computational results. The effect on the flow modelling was evaluated by comparing the wind speed, wind direction and turbulent kinetic energy using VENTOS (R)/2 at three locations, representative of the wind flow in the region. It was found that the SRTM was not an accurate representation of the Perdigao site. A 40m mesh based on the highest-resolution data yielded an elevation error of less than 1.4m and an RMSE of less than 2.5m at five reference points compared to 5.0m in the case of military maps and 7.6m in the case of the SRTM. Mesh refinement beyond 40m yielded no or insignificant changes on the flow field variables, wind speed, wind direction and turbulent kinetic energy. At least 40m horizontal resolution - threshold resolution based on topography available from aerial surveys is recommended in computational modelling of the flow over Perdigao.

Abstract
Elite swimmers and swimming pool employees are likely to be at greater health risk due to their regular and intense exposure to air stressors in the indoor swimming pool environment. Since data on the real long-term exposure is limited, a long-term monitoring and sampling plan (22 non-consecutive days, from March to July 2017) was carried out in an indoor Olympic-size pool with a chlorine-based disinfection method to characterize indoor environments to which people involved in elite swimming and maintenance staff may be exposed to. A comprehensive set of parameters related with comfort and environmental conditions (temperature, relative humidity (RH), carbon dioxide (CO2) and monoxide and ultrafine particles (UFP)) were monitored both indoors and outdoors in order to determine indoor-to-outdoor (I/O) ratios. Additionally, an analysis of volatile organic compounds (VOC) concentration and its dynamics was implemented in three 1-hr periods: early morning, evening elite swimmers training session and late evening. Samplings were simultaneously carried out in the air layer above the water surface and in the air surrounding the pool, selected to be representative of swimmers and coaches/employees' breathing zones, respectively. The results of this work showed that the indoor climate was very stable in terms of air temperature, RH and CO 2 . In terms of the other measured parameters, mean indoor UFP number concentrations (5158 pt/cm(3)) were about 50% of those measured outdoors whereas chloroform was the predominant substance detected in all samples collected indoors (13.0-369.3 mu g/m(3)), among a varied list of chemical compounds. An I/O non-trihalomethanes (THM) VOC concentration ratio of 2.7 was also found, suggesting that, beyond THM, other potentially hazardous VOC have also their source(s) indoors. THM and non-THM VOC concentration were found to increase consistently during the evening training session and exhibited a significant seasonal pattern. Compared to their coaches, elite swimmers seemed to be exposed via inhalation to significantly higher total THM levels, but to similar concentrations of non-THM VOC, during routine training activities. Regarding swimming employees, the exposure to THM and other VOC appeared to be significantly minimized during the early morning period. The air/water temperature ratio and RH were identified as important parameters that are likely to trigger the transfer processes of volatile substances from water to air and of their accumulation in the indoor environment of the swimming pool, respectively.