Abstract
This paper provides an answer to the problem of State Estimation (SE) with multiple simultaneous gross errors, based on Generalized Error Correntropy instead of Least Squares and on an interior point method algorithm instead of the conventional Gauss-Newton algorithm. The paper describes the mathematical model behind the new SE cost function and the construction of a suitable solver and presents illustrative numerical cases. The performance of SE with the data set contaminated with up to five simultaneous gross errors is assessed with confusion matrices, identifying false and missed detections. The superiority of the new method over the classical Largest Normalized Residual Test is confirmed at a 99% confidence level in a battery of tests. Its ability to address cases where gross errors fall on critical measurements, critical sets or leverage points is also confirmed at the same level of confidence.

Abstract
Classical Weighted Least Squares (WLS) State estimation (SE) in power systems is known for not performing well in the presence of Gross Errors (GE). The alternative using Correntropy proved to be appealing in dealing with outliers. Now, a novel SE method, generalized correntropy interior point method (GCIP) is being proposed, taking advantage of the properties of the Generalized Correntropy and of the Interior Point Method (IPM) as solver. This paper discusses how the choice of different central path neighborhoods, an essential concept in IPM, is critical in the identification of gross errors. The simulation results indicate that a one-sided infinity norm neighborhood successfully identifies outliers in the SE problem, making GCIP a competitive method. © 2019 IEEE.

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
The following paper presents a novel strategy named Maximum Search Limitations (MS) for the Evolutionary Particle Swarm Optimization (EPSO). The approach combines EPSO standard search mechanism with a set of rules and position-wise statistics, allowing candidate solutions to carry a more thorough search around the neighborhood of the best particle found in the swarm. The union of both techniques results in an EPSO variant named MS-EPSO. MS-EPSO crucial premise is to enhance the exploration phase while maintaining the exploitation potential of EPSO. Algorithm performance is measured on eight unconstrained and two constrained engineering design optimization problems. Simulations are made and its results are compared against other techniques including the classic Particle Swarm Optimization (PSO). Lastly, results suggest that MS-EPSO can be a rival to other optimization methods. © Springer Nature Switzerland AG 2019.

Abstract
The integration of renewable generation in electricity networks is one of the most widespread strategies to improve sustainability and to deal with the energy supply problem. Typically, the reinforcement of the generation fleet of an existing network requires the assessment and minimization of the installation and operating costs of all the energy resources in the network. Such analyses are usually conducted using peak demand and generation data. This paper proposes a method to optimize the location and size of different types of generation resources in a network, taking into account the typical evolution of demand and generation. The importance of considering this evolution is analyzed and the methodology is applied to two standard networks, namely the Institute of Electrical and Electronics Engineers (IEEE) 30-bus and the IEEE 118-bus. The proposed algorithm is based on the use of particle swarm optimization (PSO). In addition, the use of an initialization process based on the cross entropy (CE) method to accelerate convergence in problems of high computational cost is explored. The results of the case studies highlight the importance of considering dynamic demand and generation profiles to reach an effective integration of renewable resources (RRs) towards a sustainable development of electric systems.

Abstract
Spatial data mining aims at the discovery of unknown, useful patterns from large spatial datasets. This article presents a thorough analysis of the Portuguese adopters of distributed energy resources using explorative spatial data mining techniques. These resources are currently passing the early adoption stage in the study area. Results show adopter clustering during the current stage. Furthermore, spatial adoption patterns are simulated over a 20-year horizon, analyzing technology concentration changes over time while comparing three different energy policy designs. Outcomes provide useful indication for both electrical network planning and energy policy design. © Springer Nature Switzerland AG 2019.