Abstract
This paper introduces the critical need to report reliability performance metrics by distinguishing between different customer-groups, load demand and network types, within very large service areas managed by distribution network operators. Based on various factors, power distribution systems supplying residential demand are categorised in this study into rural, suburban and urban networks. An enhanced time-sequential Monte Carlo simulation procedure is used to carry out reliability assessment for each subsector, enabling disaggregation of reliability indices typically reported for the whole supplied system. Realistic distribution network modelling is achieved by the addition of smart grid technologies such as photovoltaic energy, demand side response and energy storage, to assess their impacts in different networks. Finally, both system and customer-oriented indices, measuring the frequency and duration of interruptions, as well as energy not supplied, are evaluated for a comprehensive analysis.

Abstract
In this paper we consider the problem of motion planning for perception of a target position. A robot has to move to a position from where it can sense the target, while minimizing both motion and perception costs. The problem of finding paths for robots executing perception tasks can be solved optimally using informed search. In perception path planning, the solution when considering a straight line without obstacles is used as heuristic. In this work, we propose a heuristic that can improve the search efficiency. In order to reduce the node expansion using a more informed search, we use the robot Approximate Visibility Map (A-VM), which is used as a representation of the observability capability of a robot in a given environment. We show how the critical points used in A-VM provide information on the geometry of the environment, which can be used to improve the heuristic, increasing the search efficiency. The critical points allow a better estimation of the minimum motion and perception cost for targets in non-traversable regions that can only be sensed from further away. Finally, we show the contributed heuristic with improvements dominates the base PA* heuristic built on the euclidean distance, and then present the results of the performance increase in terms of node expansion and computation time.

Abstract
The growing dependence of modern-day societies on electricity increases the importance of effective monitoring and maintenance of power lines. Endowing UAVs with the appropriate sensors for inspecting power lines, the costs and risks associated with the traditional foot patrol and helicopter-based inspections can be reduced. However, this implies the development of algorithms to make the inspection process reliable and autonomous. Visual methods are usually applied to locate the power lines and their components, but poor light conditions or a background rich in edges may compromise their results. To overcome those limitations, we propose to address the problem of power line detection and modeling based on LiDAR. A novel approach to the power line detection was developed, the PL2DM -Power Line LiDAR-based Detection and Modeling. It is based in a scan-by-scan adaptive neighbor minimalist comparison for all the points in a point cloud. The power line final model is obtained by matching and grouping several line segments, using their collinearity properties. Horizontally, the power lines are modeled as a straight line, and vertically as a catenary curve. The algorithm was validated with a real dataset, showing promising results both in terms of outputs and processing time, adding real-time object-based perception capabilities for other layers of processing.

Abstract
The adoption of energy transition technologies for residential use is accelerated through incentive designs. The structure of such incentives affects technology adoption patterns, that is, the locations where new technologies are installed and used. These spatial adoption patterns influence network expansion costs and provide indication on potential cross-subsidization between population groups. While until today, most programs have been involuntarily favoring households with high-income and above-average educated population groups, incentive designs are currently under review. This paper presents a spatiotemporal technology adoption model that can predict adoption behavior of residential electric vehicle (EV) chargers and photovoltaic (PV) modules up to a predefined time horizon. A set of EV and PV adoption patterns for nine incentive design combinations are compared in order to assess potential synergies that may arise under orchestrated EV and PV adoption. Effects on adoption asymmetries are evaluated using an Information-Theoretic inequality metric. Results for Continental Portugal show that global network expansion costs can be reduced while minimizing technology adoption asymmetries, if specific incentive designs are combined.

Abstract
One common characteristic among current lock-free memory allocators is that they rely on the operating system to manage memory since they lack a lower-level mechanism capable of splitting and coalescing blocks of memory. In this paper, we discuss this problem and we propose a generic scheme for an efficient lock-free best-fit coalescing-capable mechanism that is able of satisfying memory allocation requests with desirable low fragmentation characteristics.

Abstract
We report on the development of numerical module for the HiLight simulation platform based on GPGPU supercomputing to solve a system of coupled fields governed by the Generalized Nonlinear Schrodinger Equation with local and/or nonlocal nonlinearities. This models plays an important role in describing a plethora of different phenomena in various areas of physics. In optics, this model was initially used to describe the propagation of light through local and/or nonlocal systems under the paraxial approximation, but more recently it has been extensively used as a support model to develop optical analogues. However, establishing the relation between the original system and the analogue, as well as, between their model and the actual experimental setup is not an easy task. First and foremost because in most cases the governing equations are nonintegrable, preventing from obtaining analytical solutions and hindering the optimization of the experiments. Alternatively, despite numerical methods not providing exact solutions, they allow to test different experimental scenarios and provide a better insight to what to expect in an actual experiment, while giving access to all the variables of the optical system being simulated. However, the numerical solution of a system of N-coupled Schrodinger fields in systems with two or three spatial dimensions requires massive computation resources, and must employ advanced supercomputing and parallelization techniques, such as GPGPU. This paper focuses on the numerical aspects behind this challenge, describing the structure of our simulation module, its performance and the tests performed.