Abstract
Energy efficiency in buildings can be enhanced by several actions: encouraging users to comprehend and then adopt more energy-efficient behaviors; aiding building managers in maximizing energy savings; and using automation to optimize energy consumption, generation, and storage of controllable and flexible devices without compromising comfort levels and indoor air-quality parameters. This paper proposes an integrated Information and communications technology (ICT) based platform addressing all these factors. The gamification platform is embedded in the ICT platform along with an interactive energy management system, which aids interested stakeholders in optimizing "when and at which rate" energy should be buffered and consumed, with several advantages, such as reducing peak load, maximizing local renewable energy consumption, and delivering more efficient use of the resources available in individual buildings or blocks of buildings. This system also interacts with an automation manager and a users' behavior predictor application. The work was developed in the Horizon 2020 FEEdBACk (Fostering Energy Efficiency and BehAvioral Change through ICT) project.

Abstract
The energy efficiency requirements of most energy-consuming sectors have increased recently in response to climate change. For buildings, this means targeting both facility managers and building users with the aim of identifying potential energy savings and encouraging more energy-responsible behaviors. The Information and Communication Technology (ICT) platform developed in Horizon 2020 FEEdBACk project intends to fulfill these goals by enabling the optimization of energy consumption, generation, and storage and control of flexible devices without compromising comfort levels and indoor air quality parameters. This work aims to demonstrate the real-world implementation and functionality of the ICT platform composed of Load Disaggregation, Net Load Forecast, Occupancy Forecast, Automation Manager, and Behavior Predictor applications. Particularly, the results obtained by individual applications during the test phase are presented alongside the specific metrics used to evaluate their performance.

Abstract
The increasing replacement of conventional generators by variable renewable energy sources is reducing the flexibility of the power system, and consequently reducing its reliability indexes. To compensate for this reduction of flexibility, market participation of aggregators of multi-energy systems has been proposed in the literature. Under this scope, this paper presents a network-secure bidding optimization strategy to assist aggregators of multi-energy systems calculating electricity (energy and reserve), gas and carbon bids, considering multi-energy network constraints. This strategy is a distributed approach based on the alternating direction method of multipliers, where the aggregator collaborates with the operators of electricity, gas and heat networks to calculate network-secure bids. The proposed strategy is benchmarked against two other approaches. The results show that the newly developed strategy computes multi-energy and network-secure bids with execution times that suit the timelines of the electricity, gas, and carbon markets. The joint optimization of multi-energy systems reduced the aggregator's costs by 89% compared to a single energy-vector approach. Furthermore, two sensibility studies were also performed. The first study revealed that in the presence of slow ramp-rate resources (e.g. combined heat and power systems), aggregator's costs can decrease up to 87% when considering slower response times to the secondary reserve signal. In the second study, it was observed that the bidding behavior of the aggregator only starts changing significantly with carbon prices higher than 200euro/tCO2.

Abstract
Increased levels of renewable generation and electric vehicles require grid operators to adapt their assets to ensure that they can maintain safe and reliable grid operations. This paper presents a methodology and a business case to determine the size and location of multiple storage options with respect of traditional wires grid upgrade. The work is focused on the analysis of a business case of a DSO that owns a distribution network and exploit the possibility to install energy storage to defer network infrastructure upgrade caused by peak power flow that exceed the existing capacity or give rise to voltage quality problems. The proposed method is validated by simulations considering a real distribution network in the northern Portugal in three different scenarios. The results show that installing energy storage is still more expensive than traditional wires upgrade.

Abstract
The planning of the transmission network is an issue that, over the years, has received much attention, particularly due to the impact that this infrastructure has on the safe and reliable functioning of electrical systems. The search for solutions addressing climate change has led to several changes in the functioning of electrical systems, particularly concerning the increasing integration of renewable electricity production. However, in recent years, changes in the load side of the electrical system have also emerged. In particular, electric mobility has been developing, and a high penetration of electric vehicles (EVs) is expected in near future. This consumption is supplied by the distribution system but will impact the transmission network. Naturally, the amount of energy used by EVs is subject to uncertainties, which makes the problem complex. Those uncertainties cannot be easily modeled using statistical distributions because of the reduced history of available information. The transmission system operator (TSO) needs an efficient tool to analyze the adequacy of the transmission network to supply the distribution networks with high penetration of EVs. In this paper, a methodology based on symmetric/constrained fuzzy power flow is proposed to find the optimal investment policy at the transmission level while satisfying the technical constraints. The concept of dual variables provided by Lagrange multipliers, the natural result of the nonlinear optimization problem, is used to obtain the most promising reinforcement options considering the actual structure of the transmission network. The proposed model is tested on an IEEE 14-bus system.

Abstract
Matrix converters (MCs) allow the implementation of single-stage AC/AC power conversion systems (PCS) with inherent bidirectional power flow capability. By avoiding the typical DC-link capacitor, MCs have the potential to achieve higher power density with a more reliable operation and less maintenance when compared with conventional two-stage AC/DC/AC PCS. For these reasons, matrix converters have been receiving significant attention from the academic sector but have not yet been implemented on a large industrial scale. This article reviews the Direct Matrix Converter (DMC) and the Indirect Matrix Converter (IMC) along with the respective actual and most important modulation methods. Simulation results are provided to validate the theoretical analysis and to get a deep insight about the implementation of space vector modulation (SVM) and respective switching pattern generator.