Abstract
The Smart Transformer (ST) is being envisioned as the possible backbone of future distribution grids given the enhanced controllability it provides. Moreover, the ST offers DC-link connectivity, making it an attractive solution for the deployment of hybrid AC/DC distribution grids which offer important advantages for the deployment of Renewable Energy Sources, Energy Storage Systems (ESSs) and Electric Vehicles. However, compared to traditional low-frequency magnetic transformers, the ST is inherently more vulnerable to fault disturbances which may force the ST to disconnect in order to protect its power electronic converters, posing important challenges to the hybrid AC/DC grid connected to it. This paper proposes a Fault-Ride-Through (FRT) strategy suited for grid-tied ST with no locally available ESS, which exploits a dump-load and the sensitivity of the hybrid AC/DC distribution grid's power to voltage and frequency to provide enhanced control to the ST in order to handle AC-side voltage sags. The proposed FRT strategy can exploit all the hybrid AC/DC distribution grid (including the MV DC sub-network) and existing controllable DER resources, providing FRT against balanced and unbalanced faults in the upstream AC grid. The proposed strategy is demonstrated in this paper through computational simulation.

Abstract
With the expansion of power electronics possibilities for smart homes, new perceptions for power control are emerging, suggesting new possibilities also for smart grids. In this prospect, the solid-state transformer (SST) has a substantial impact to interface smart homes with smart grids, guaranteeing high levels of power quality in both grid (consumed current) and load side (produced voltage). Nevertheless, as advanced contributions, the SST can deal with other possibilities of controllability. In such situation, an analysis of new operation opportunities for the SST into smart homes and smart grid perspectives is offered in this paper. It is discussed the SST principle of operation, with a thorough clarification concerning the proposed control algorithms, as well as an intuitive computational validation contemplating contingencies of operation about power quality effects for the load and grid side. The attained results strengthen the attractiveness of the new operation opportunities for the SST when utilized as interface between homes and smart grids.

Abstract
Future Secondary Substation (SS) design requires a more integrated approach, from the building envelope design, electromechanically equipments to the advanced monitoring and control, taking into account new technical, environmental and economic requirements. The main objective of the project is to develop an integrated solution for the SSS, considering innovative solutions for the building envelope and thermal behavior, power transformer and switching equipment as well as monitoring, protection and control system. This paper presents the specification of future SS developed in Portuguese project NEXTSTEP - Next Distribution SubsTation ImprovEd Platform and describes its mains innovative solution and advanced control functionalities. © 2021 The Institution of Engineering and Technology.

Abstract
Geographically distributed wind turbines, photovoltaic panels and sensors (e.g., pyranometers) produce large volumes of data that can be used to improve renewable energy sources (RES) forecasting skill. However, data owners may be unwilling to share their data, even if privacy is ensured, due to a form of prisoner's dilemma: all could benefit from data sharing, but in practice no one is willing to do do. Our proposal hence consists of a data marketplace, to incentivize collaboration between different data owners through the monetization of data. We adapt here an existing auction mechanism to the case of RES forecasting data. It accommodates the temporal nature of the data, i.e., lagged time-series act as covariates and models are updated continuously using a sliding window. A test case with wind energy data is presented to illustrate and assess the effectiveness of such data markets. All agents (or data owners) are shown to benefit in terms of higher revenue resulting from the combination of electricity and data markets. The results support the idea that data markets can be a viable solution to promote data exchange between RES agents and contribute to reducing system imbalance costs.

Abstract
Cooperation between different data owners may lead to an improvement in forecast quality-for instance, by benefiting from spatiotemporal dependencies in geographically distributed time series. Due to business competitive factors and personal data protection concerns, however, said data owners might be unwilling to share their data. Interest in collaborative privacy-preserving forecasting is thus increasing. This paper analyzes the state-of-the-art and unveils several shortcomings of existing methods in guaranteeing data privacy when employing vector autoregressive models. The methods are divided into three groups: data transformation, secure multi-party computations, and decomposition methods. The analysis shows that state-of-the-art techniques have limitations in preserving data privacy, such as (i) the necessary trade-off between privacy and forecasting accuracy, empirically evaluated through simulations and real-world experiments based on solar data; and (ii) iterative model fitting processes, which reveal data after a number of iterations.

Abstract
Forecasts of residual demand curves (RDCs) are valuable information for price-maker market agents since it enables an assessment of their bidding strategy in the market-clearing price. This paper describes the application of deep learning techniques, namely long short-term memory (LSTM) network that combines past RDCs and exogenous variables (e.g., renewable energy forecasts). The main contribution is to build up on the idea of transforming the temporal sequence of RDCs into a sequence of images, avoiding any feature reduction and exploiting the capability of LSTM in handling image data. The proposed method was tested with data from the Iberian day-ahead electricity market and outperformed machine learning models with an improvement of above 35% in both root mean square error and Frechet distance.