Abstract

Abstract
In power system static security analysis, it often requires to calculate continuous power flow from a certain load condition to a bifurcation point along a given direction, which is referred to as the maximum loadability problem. This paper proposes a convex optimization method for maximum loadability problem over meshed power grids based on the semidefinite relaxation approach. Because the objective is to maximize the load increasing distance, convex relaxation model is generally inexact, unlike the situation in cost-minimum optimal power flow problem. Inspired by the rank penalty method, this paper proposes an iterative procedure to retrieve the maximum loadability. The convex quadratic term representing the penalty on the rank of matrix variable is updated in each iteration based on the latest solution. In order to expedite convergence, generator reactive power is also included in the objective function, which has been reported in literature. Numeric tests on some small-scale systems validate its effectiveness. Any sparsity-exploration and acceleration techniques for semidefinite programming can improve the efficiency of the proposed approach. © 2019 IEEE.

Abstract
The charging behavior of electric vehicles (EVs) is a key concern to system operators and retailers given a massive adoption these resources. System operators and retailers may profit from the handling of EVs as flexible loads. The former has interest to move their charging into periods without congestion and voltage problems. Similarly, the latter wants them to only charge at periods when energy is cheaper. This paper addresses the problem by modelling a real-time tariff to encourage flexible EVs charging behavior. More precisely, different tariffs are modelled based on the relation between wind power generation, load consumption and spot price, while assuming Denmark as showcase. The EVs behavior entails three different patterns and a socioeconomic term that defines the anxiety of EVs users' to be responsive to the tariffs. An important conclusion is that a proper real-time tariff design can reduce the energy costs for the retailer and EVs.

Abstract
Absenteeism is a major problem faced particularly by companies with a large number of employees. Therefore, the existence of absenteeism prediction tools is essential for such companies depending on intensive human-resources. This paper focuses on using machine learning technologies for predicting the absences of employees from work. More precisely, a few prediction models were tuned and tested with 241 features extracted from a population of 13.805 employees. This target population was sampled from the help desk work force of a major Brazilian phone company. The features were extracted from the profile of the help desk agents and then filtered by processes of correlation and feature selection. The selected features were then used to compare absenteeism prediction given by different classification algorithm (cf. Random Forest, Multilayer Perceptron, Support Vector Machine, Naive Bayes, XGBoost and Long Short Term Memory). The parameterization of these ML models was also studied to reach the classifier best suited for the prediction problem. Such parameterizations were tuned through the use of evolutionary algorithms, from which considerable precision was reached, the best being 72% (XGBoost) and 71% (Random Forest).

Abstract
We present the status and plans for the Keck All sky Precision Adaptive optics (KAPA) program. The program includes four key science projects, an upgrade to the Keck I laser guide star (LGS) adaptive optics (AO) facility to improve image quality and sky coverage, AO telemetry based point spread function (PSF) estimates for all science exposures, and an educational component focused on broadening the participation of women and underrepresented groups in instrumentation. All of these elements have pathfinder relevance for the ELTs. For the purpose of this conference we will focus on the AO facility upgrade which includes implementation of a new laser, wavefront sensor and real-time controller to support laser tomography, the laser tomography system itself, and modifications to an existing near-infrared tip-tilt sensor to support multiple natural guide star (NGS) and focus measurements.

Abstract
This work demonstrates a system that provides decision support to players in electricity market negotiations. This contribution is provided by ALBidS (Adaptive Learning strategic Bidding System), a decision support system that includes a large number of distinct market negotiation strategies, and learns which should be used in each context in order to provide the best expected response. The learning process on the best negotiation strategies to use at each moment is developed by means of several integrated reinforcement learning algorithms. ALBidS is integrated with MASCEM (Multi-Agent Simulator of Competitive Electricity Markets), which enables the simulation of realistic market scenarios using real data.