Abstract
The digital transformation associated with the huge volume of data that healthcare organizations deal with today is based on transforming this complex knowledge-driven industry to turn data into knowledge. The healthcare industry requires comprehensive models that help identify priorities to implement business intelligence (BI) solution. Business intelligence can help organizations make better decisions by showing current and historical data within their business context. This paper systematizes and analyzes three business intelligence maturity models into one and also attempts to understand the main key performance indicators in adopting business intelligence maturity model in healthcare organizations. For this purpose, we present a questionnaire that was based on the systemized business intelligence maturity model that was sent to X% of the Portuguese hospitals with the objective of identifying not only the business intelligence maturity stage of the Portuguese hospitals but also to infer the most important key performance indicators that will characterize each stage. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract

Abstract
This paper presents a multi-stage day-ahead and real-time optimization algorithm for scheduling of system's energy resources in the normal and external shock operational conditions. The main contribution of this paper is that the model considers the non-utility electricity generation facilities capacity withholding opportunities in the optimal scheduling of system resources. The real-time simulation of external shock impacts is another contribution of this paper that the algorithm simulates the sectionalizing of the system into multi-microgrids to increase the resiliency of the system. The optimization process is categorized into two stages that compromise normal and contingent operational conditions. Further, the normal operational scheduling problem is decomposed into three steps. At the first step, the optimal day-ahead scheduling of system resources and the switching of normally opened switches are determined. At the second step, the optimal real-time market scheduling is performed and the switching of normally closed switches is optimized. At the third step, different extreme shock scenarios are simulated in the real-time horizon and the effectiveness of sectionalizing the system into multi-micro grids are assessed. Finally, at the contingent operational conditions, the optimal topology of the system and scheduling of energy resources are determined. The proposed method was successfully assessed for the 33-bus and 123-bus test systems. The algorithm were reduced the expected cost of the worst-case contingencies for the 33-bus and 123-bus systems by about 97.89% and 88.11%, respectively. Further, the average and maximum values of the 123-bus system capacity-withholding index for real-time conditions reduced by about 67.40% and 71.05%, respectively. © 2021 Elsevier Ltd

Abstract
The fast-charging units have become a more efficient and attractive option recently for reducing the challenges due to the long charging time of electric vehicles (EVs). To evaluate the impacts of the EV fast charging stations (EVFCS) on the power grid and also to assess their contributions to the system operation through the vehicle-to-grid (V2G) technology, two control methods, namely, sliding mode control (SMC) and fuzzy logic control (FLC), are developed in this study for a DC microgrid including EVFCS and distributed generation sources. In these methods, the EV battery is used as a DC source of a distribution static compensator (D-STATCOM) with the objective of mitigating the voltage sag in the microgrid. Various simulations are conducted in MATLAB Simulink/SimPowerSystems environment in order to examine the effectiveness of the proposed control approaches in terms of ensuring the stability and improving the dynamic performance of the EVFCS. The results show that considerable improvements can be achieved, especially in the case of using the SMC method.

Abstract
In an earlier work we described and applied a methodology to find an adequate distribution for Nearly Gaussian (NG) random variables. In this work, we compare two different methods, m1 and m2 to estimate a power transform parameter for NG random variables. The m1 method is heuristic and based on sample kurtosis. Herein, we describe and apply it using a new reduced data set. The second method m2 is based on the maximization of a pseudo-log-likelihood function. As an application, we compare the performance of each method using high power statistical tests for the null hypothesis of normality. The data we use are the daily errors in the forecasts of maximum and minimum temperatures in the city of Porto. We show that the high kurtosis of the original data is due to high correlation among data. We also found that although consistent with normality the data is better fitted by distributions of the power normal (PN) family than by the normal distribution. Regarding the comparison of the two parameter estimation methods we found that the m1 provides higher p-values for the observed statistics tests except for the Shapiro-Wilk test.

Abstract
The adoption of battery-powered electric vehicles in the EU is expected to grow to 30-40 million by 2030. This, together with the large adoption of other Distributed Energy Resources (DERs), represents a great challenge for Distribution System Operators (DSOs) in multiple perspectives, such as providing the needed charging infrastructure and ensuring that everyone is served with the expected Quality of Service (QoS), by having a secure and reliable system operation capable of mitigating grid congestion and voltage violation events. One of the mechanisms to mitigate these events can be the usage of these DER, such as Electric Vehicles (EVs), as flexibility sources for the improvement of the planning and operation of power distribution systems. This paper proposes harmonising the coordination of the prequalification process for flexibility provision (product and grid prequalification) among System and Market Operators from Portugal, Spain and France, enabling the participation of flexibility providers in multiple markets from cross-border countries through a harmonised and non-redundant prequalification process. © 2022 CIRED workshop on E-mobility and power distribution systems. All rights reserved.