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
The power system is facing a transition from its traditional centralized model to a more decentralized one, through the emergence of proactive consumers on the network, known as prosumers. This paradigm shift favors the emergence of new electricity market designs. Peer-to-Peer (P2P) based structures have been gaining prominence worldwide. In the P2P market, the prosumer assumes a more active role in the system, being able to directly trade its energy without the need for intermediaries. This paper contributes with a comprehensive overview of consumer-centric electricity markets, providing background on different aspects of P2P sharing, in particular the inclusion of peer preferences in the electricity trading process through product differentiation. A performance assessment of the different modeled preferences was carried out using key performance indicators (KPIs). Different user preferences under the product differentiation mechanism were simulated. The results demonstrate that consumer-centric markets increase the penetration of renewable energy sources into the network and tend to affect loads flexibility according to the renewable generation.

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
The decarbonisation of the building sector is crucial for Portugal's goal of achieving economy-wide carbon neutrality by 2050. To mobilize communities towards energy efficiency measures, it is important to understand the primary drivers and barriers that must be overcome through policymaking. This paper aims to review existing Energy Policies and Actions (EPA) in Portugal and assess their effectiveness in improving Energy Efficiency (EE) and reducing CO2 emissions in the building sector. The Local Energy Planning Assistant (LEPA) tool was used to model, test, validate and compare the implementation of current and alternative EPAs in the North of Portugal, including the national EE plan. The results indicate that electrification of heating and cooling, EE measures, and the proliferation of Renewable Energy Sources (RES) are crucial for achieving climate neutrality. The study found that the modelling of alternative EPAs can be improved to reduce investment costs and increase Greenhouse Gas (GHG) emissions reduction. Among the alternatives assessed, the proposed one (Alternative 4) presents the best returns on investment in terms of cost savings and emissions reduction. It allows for 52% investment cost savings in the residential sector and 13% in the service sector when compared to the current national roadmap to carbon neutrality (Alternative 2). The estimated emission reduction in 2050 for Alternative 4 is 0.64% for the residential sector and 3.2% for the service sector when compared to Alternative 2.