Abstract
Evaluating the effectiveness of teaching and learning core knowledge outcomes and professional skills is a highly challenging task that has not yet been satisfactorily addressed at higher education level. The iTeach European project consortium developed a framework for assessing the effectiveness of various pedagogical methodologies in chemical engineering education, including those aiming to promote important core competencies related to employability, in a range of geographical and educational contexts. The framework was firstly implemented in a core chemical engineering area (reaction engineering) to check its usability and robustness, and subsequently was also tested on a range of subject areas from various branches of engineering and other disciplines, one of which is analysed in more detail in this contribution. The results of this broader assessment encompassed a much more diverse student body with varying educational experiences and a wider range of different teaching methodologies. The outcomes of this assessment are highlighted and the benefits of such an objective approach for evaluating teaching effectiveness is discussed. Crown Copyright

Abstract
The development of small-scale power generation units based on biomass gasification is an effective mean to meet the growth interest of deployment of local power generation exploiting endogenous renewable energy sources. However, significant research and development activities are required towards the deployment of cost-effective solutions suitable to be used in several applications and with different biomass feedstock. For this purpose, a flexible experimental setup is required to be developed. This paper proposes a critical review of the current state of the art of the available technologies suitable for small-scale power generation using biomass gasification. The main guidelines to develop cost-effective solutions are identified and the conceptual framework of the experimental setup is proposed. Also, the operational specifications are presented.

Abstract
Distribution system operators (DSO) are currently moving towards active distribution grid management. One goal is the development of tools for operational planning of flexibility from distributed energy resources (DER) in order to solve potential (predicted) congestion and voltage problems. This work proposes an innovative flexibility management function based on stochastic and chance-constrained optimization that copes with forecast uncertainty from renewable energy sources (RES). Furthermore, the model allows the decision-maker to integrate its attitude towards risk by considering a trade-off between operating costs and system reliability. RES forecast uncertainty is modeled through spatial-temporal trajectories or ensembles. An AC-OPF linearization that approximates the actual behavior of the system is included, ensuring complete convexity of the problem. McCormick and big-M relaxation methods are compared to reformulate the chance-constrained optimization problem. The discussion and comparison of the proposed models is carried out through a case study based on actual generation data, where operating costs, system reliability and computer performance are evaluated.

Abstract
This paper analyzes the electricity prices of the MIBEL electricity spot market with respect to a set of possible explanatory variables. Understanding the main drivers of the electricity price is a key aspect in understanding price formation and in developing forecasting models, which are essential for the selling and buying strategies of market agents. For this analysis, different techniques have been applied in this work, including standard and lasso regression models, causal analysis based on bayesian networks and classification trees. Results from the different approaches are coherent and show strong dependency of the electricity prices with the Portuguese imported coal for lower non-dispatchable net demands, which has been progressively replaced by gas for larger non-dispatchable net demands. Hydro reservoirs and hydro production are also main explanatory variables of the electricity price for all non-dispatchable net demand levels.

Abstract
Actual integration of high-tech devices brings opportunities for better monitoring, management and control of low voltage networks. In this new paradigm, efficient tools should cope with the great amount of dispersed and considerably distinct data to support smarter decisions in almost real time. Besides the use of tools to enable an optimal network reconfiguration and integration of dispersed and renewable generation, the impact evaluation of integrating storage systems, accurate load forecasting methods must be found even when applied to individual consumers (characterized by the high presence of noise in time series). As this effort becomes providential in the smart grids context, this article compares three different approaches: one based on Kernel Density Estimation, an alternative based on Artificial Neural Networks and a method using Support Vector Machines. The first two methods revealed unequivocal benefits when compared to a Naive method consisting of a simple reproduction of the last available day.

Abstract
While the transmission system benefits from a high observability, the distribution system has a relatively low level of observability. This problem is already being addressed with the deployment of smart meters, in an effort to make the smart grid concept a reality. Nevertheless, as observability increases, so too does the volume of data, which makes the development of advanced software tools a very important subject. In this paper, the application of image analysis techniques to a low voltage grid is explored, by converting voltage data into an image format, using a cognitive network to evaluate and cluster grid operating modes. The proposed method is applied to a 33-bus low voltage grid to evaluate voltage profiles at each bus and the associated technical limits (voltage limits alarms).