Abstract
Demand response services and energy communities are set to be vital in bringing citizens to the core of the energy transition. The success of load flexibility integration in the electricity market, provided by demand response services, will depend on a redesign or adaptation of the current regulatory framework, which so far only reaches large industrial electricity users. However, due to the high contribution of the residential sector to electricity consumption, there is huge potential when considering the aggregated load flexibility of this sector. Nevertheless, challenges remain in load flexibility estimation and attaining data integrity while respecting consumer privacy. This study presents a methodology to estimate such flexibility by integrating a non-intrusive load monitoring approach to load disaggregation algorithms in order to train a machine-learning model. We then apply a categorization of loads and develop flexibility criteria, targeting each load flexibility amplitude with a corresponding time. Two datasets, Residential Energy Disaggregation Dataset (REDD) and Refit, are used to simulate the flexibility for a specific household, applying it to a grid balancing event request. Two algorithms are used for load disaggregation, Combinatorial Optimization, and a Factorial Hidden Markov model, and the U.K. demand response Short Term Operating Reserve (STOR) program is used for market integration. Results show a maximum flexibility power of 200–245 W and 180–500 W for the REDD and Refit datasets, respectively. The accuracy metrics of the flexibility models are presented, and results are discussed considering market barriers.

Abstract
Interoperability is a challenge for the realisation of smart grids. In this work, we apply the methodology for interoperability testing and the design of experiments developed at the Smart Grids Interoperability Laboratory of the Joint Research Centre of the European Commission on a simple use case. The methodology is based on the Smart Grid Architecture Model (SGAM) of CEN/CENELEC/ETSI and includes the concept of Basic Application Profiles (BAP) and Basic Application Interoperability Profiles (BAIOP). The relevant elements of the methodology are the design of experiments and the sensitivity/uncertainty analysis, which can reveal the limits of a system under test and give valuable feedback about the critical conditions which do not guarantee interoperability. The design and analysis of experiments employed in the Joint Research Centre (JRC) methodology supply information about the crucial parameters that either lead to an acceptable system performance or to a failure of interoperability. The use case on which the methodology is applied describes the interaction between a data concentrator and one or more smart meters. Experimental results are presented that show the applicability of the methodology and the design of experiments in practice. The system is tested under different conditions by varying two parameters: the rate at which meter data are requested by the data concentrator and the number of smart meters connected to the data concentrator. With this use case example the JRC methodology is illustrated at work, and its effectiveness for testing interoperability of a system under stress conditions is highlighted.

Abstract
The adoption of electric vehicles (EV) has to be complemented with the right charging infrastructure roll-out. This infrastructure is already in place in many cities throughout the main markets of China, EU and USA. Public policies are both taken at regional and/or at a city level targeting both EV adoption, but also charging infrastructure management. A growing trend is the increasing idle time over the years (time an EV is connected without charging), which directly impacts on the sizing of the infrastructure, hence its cost or availability. Such a phenomenon can be regarded as an opportunity but may very well undermine the same initiatives being taken to promote adoption; in any case it must be measured, studied, and managed. The time an EV takes to charge depends on its initial/final state of charge (SOC) and the power being supplied to it. The problem however is to estimate the time the EV remains parked after charging (idle time), as it depends on many factors which simple statistical analysis cannot tackle. In this study we apply supervised machine learning to a dataset from the Netherlands and analyze three regression algorithms, Random Forest, Gradient Boosting and XGBoost, identifying the most accurate one and main influencing parameters. The model can provide useful information for EV users, policy maker and network owners to better manage the network, targeting specific variables. The best performing model is XGBoost with an R 2 score of 60.32% and mean absolute error of 1.11. The parameters influencing the model the most are: The time of day in which the charging sessions start and the total energy supplied with 22.35%, 15.57% contribution respectively. Partial dependencies of variables and model performances are presented and implications on public policies discussed.

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Since the entry into service of the so-called Primary Network in 1951 until the 1960s, when the first electrical power interconnection between Portugal and Spain was created, that the Portuguese National Transmission Network could be considered a closed system, however, since the year 1961, that was no longer true. Taking these facts into account, the need to think and draw up new standards and regulations, with the widest possible coverage, has arisen in order to oblige European operators to maintain a level of control, security and knowledge of their transmission networks, which ensure that these do not influence each other negatively and that there is a coordinated response to incidents that may occur. © 2019 IEEE.