Abstract
This paper tackles the efficient Power-Flow solution of ill-conditioned cases. In that sense, those methods based on the Continuous Newton's philosophy look very promising, however, these methodologies still present some issues mainly related with the computational efficiency or the robustness properties. In order to overcome these drawbacks, we suggest several modifications about the standard structure of the Continuous Newton's method. Thus, the standard Continuous Newton's paradigm is firstly modified with a frozen Jacobian scheme for reducing its computational burden; secondly, it is extended for being used with High-order Newton-like method for achieving higher convergence rate and, finally, a regularization scheme is introduced for improving its robustness features. On the basis of the suggested improvements, a Power-Flow solution paradigm is developed. As example, a novel Power-Flow solver based on the introduced solution framework and the 4th order Runge-Kutta formula is developed. The novel technique is validated in several realistic large-scale ill-conditioned systems. Results show that the suggested modifications allow to overcome the drawbacks presented by those methodologies based on the Continuous Newton's method. On the light of the results obtained it can be also claimed, that the developed solution paradigm constitutes a promising framework for developing robust and efficient Power-Flow solution techniques.

Abstract
Electrification of society and economy is crucial to fight against climate changes assuming simultaneously a large-scale integration of electricity generation exploiting Renewable Energy Sources (RES). The increasing presence of RES and Electric Vehicles (EV) in Low Voltage (LV) networks, and the emergence of the Smart Grid paradigm will require relevant changes in the operational management of both LV and Medium Voltage (MV) networks. In this paper, two different strategies (separated and coordinated management) for the operational management of MV and LV networks are compared regarding their ability to integrate large amounts of RES and to accept increased electrification of consumption, including EV. Each management strategy is modeled through optimization problems, being then applied to an electrical distribution system consisting of MV and LV networks. Results show that a coordinated operational management outperforms the separated strategy, by allowing the integration of much higher volumes of RES and EV.

Abstract
Electrical utilities apply condition monitoring on power transformers (PTs) to prevent unplanned outages and detect incipient faults. This monitoring is often done using dissolved gas analysis (DGA) coupled with engineering methods to interpret the data, however the obtained results lack accuracy and reproducibility. In order to improve accuracy, various advanced analytical methods have been proposed in the literature. Nonetheless, these methods are often hard to interpret by the decision-maker and require a substantial amount of failure records to be trained. In the context of the PTs, failure data quality is recurrently questionable, and failure records are scarce when compared to nonfailure records. This work tackles these challenges by proposing a novel unsupervised methodology for diagnosing PT condition. Differently from the supervised approaches in the literature, our method does not require the labeling of DGA records and incorporates a visual representation of the results in a 2D scatter plot to assist in interpretation. A modified clustering technique is used to classify the condition of different PTs using historical DGA data. Finally, well-known engineering methods are applied to interpret each of the obtained clusters. The approach was validated using data from two different real-world data sets provided by a generation company and a distribution system operator. The results highlight the advantages of the proposed approach and outperformed engineering methods (from IEC and IEEE standards) and companies legacy method. The approach was also validated on the public IEC TC10 database, showing the capability to achieve comparable accuracy with supervised learning methods from the literature. As a result of the methodology performance, both companies are currently using it in their daily DGA diagnosis.

Abstract
The power distribution grid is centrally managed concerning the requirements of the end-users, however, with the appearance of smart grids, new technologies arc arising. Therefore, distributed energy resources, mainly, renewables, energy storage systems, electric mobility, and power quality are viewed as encouraging contributions for improving power management. In these circumstances, this paper presents a power electronics perspective for the power distribution grid, considering innovative features, and including a power quality perception. Throughout the paper are presented relevant concepts for a concrete realization of a smart grid, supported by the integration of power electronics devices as the interface of the mentioned technologies. Aiming to support the innovative power electronics systems for interfacing the mentioned technologies in smart grids, a set of developed power electronics equipment was developed and, along with the paper, are shown and described, supporting the most important contributions of this paper.

Abstract
Minimising the total power losses and enhancing the voltage profile is achieved using a proposed multi-objective chaotic salp swarm algorithm with fuzzy logic decision-making. The proposed multi-objective chaotic salp swarm algorithm is utilised to determine the optimal size and location of photovoltaic in radial distribution system to minimise the total power losses, total voltage deviation, and maximise the voltage stability index. In addition, the proposed multi-objective chaotic salp swarm algorithm is used to find suitable scheduling for battery energy storage charge/discharge during 24 h considering the intermittent nature of photovoltaic power generation. The proposed algorithm is tested on standard and practical radial distribution systems (IEEE 33-bus and 94-bus Portuguese systems). The performance of the proposed algorithm is validated by comparing its results with those obtained by other competitive optimisation techniques. The obtained results prove the ability of the proposed algorithm to achieve an efficient setting for photovoltaics and battery energy storages and determine their optimal allocations in order to minimise the power losses and enhance the voltage profile with satisfying all operating constraints.

Abstract
Due to the complexity and great relevance of the transmission network expansion planning (TNEP) for electrical systems, this topic remains on the focus of the academic and industry communities. Therefore, this paper proposes a new approach to deal efficiently with the basic formulation of this problem, combining low computational effort and good quality of the obtained solutions. In this approach four factors contribute to solve TNEP problem more efficiently: (i) the investment decisions are selected using a new Constructive Heuristic Algorithm (CHA); (ii) the proposed CHA includes two stages, using the relaxation of the decision integers variables through the hyperbolic tangent function and the setting of its function's slope; (iii) the performance index that was adopted was modified regarding what was reported in the literature; (iv) the use of the primal-dual interior point optimization technique allows the representation of the nonlinearities in the problem: transmission power losses and the hyperbolic tangent function (investment decision). The quality and effectiveness of the proposed algorithm is verified using two real power systems, where the proposed CHA is able to lead to better quality solutions than the ones reported in the literature.