The use of electronic loads has improved many aspects of everyday life, permitting more efficient, precise and automated process. As a drawback, the nonlinear behavior of these systems entails the injection of electrical disturbances on the power grid that can cause distortion of voltage and current. In order to adopt countermeasures, it is important to detect and classify these disturbances. To do this, several Machine Learning Algorithms are currently exploited. Among them, for the present work, the Long Short Term Memory (LSTM), the Convolutional Neural Networks (CNN), the Convolutional Neural Networks Long Short Term Memory (CNN-LSTM) and the CNN-LSTM with adjusted hyperparameters are compared. As a preliminary stage of the research, the voltage and current time signals are simulated using MATLAB Simulink. Thanks to the simulation results, it is possible to acquire a current and voltage dataset with which the identification algorithms are trained, validated and tested. These datasets include simulations of several disturbances such as Sag, Swell, Harmonics, Transient, Notch and Interruption. Data Augmentation techniques are used in order to increase the variability of the training and validation dataset in order to obtain a generalized result. After that, the networks are fed with an experimental dataset of voltage and current field measurements containing the disturbances mentioned above. The networks have been compared, resulting in a 79.14% correct classification rate with the LSTM network versus a 84.58% for the CNN, 84.76% for the CNN-LSTM and a 83.66% for the CNN-LSTM with adjusted hyperparameters. All of these networks are tested using real measurements.

In this work, a strategy for scheduling a battery energy storage system (BESS) in a renewable energy community (REC) is proposed. RECs have been defined at EU level by the 2018/2001 Directive; some Member States transposition into national legislation defined RECs as virtual microgrids since they still use the existing low voltage local feeder and share the same low-medium voltage transformer. This work analyzes a REC which assets include PV generators, BESS and non-controllable loads, operating under the Italian legislative framework. A methodology is defined to optimize REC economic revenues and minimize the operation costs during the year. The proposed BESS control strategy is composed by three different modules: (i) a machine learning-based forecast algorithm that provides a 1-day-ahead projection for microgrid loads and PV generation, using historical dataset and weather forecasts; (ii) a mixed integer linear programming (MILP) algorithm that optimizes the BESS scheduling for minimal REC operating costs, taking into account electricity price, variable feed-in tariffs for PV generators, BESS costs and maximization of the self-consumption; (iii) a decision tree algorithm that works at the intra-hour level, with 1 min timestep and with real load and PV generation measurements adjusting the BESS scheduling in real time. Validation of the proposed strategy is performed on data acquired from a real small-scale REC set up with an Italian energy provider. A 10% average revenue increase could be obtained for the prosumer alone when compared to the non-optimized BESS usage scenario; such revenue increase is obtained by reducing the BESS usage by around 30% when compared to the unmanaged baseline scenario.

Depletion of fossil fuel deposits is the main current issue related to the world’s power generation. Renewable energy sources integrated with energy efficiency represent an effective solution. The electrification of end-use coupled with renewable power generation integration is considered as an important tool to achieve these tasks. However, the current electric power system does not currently have the suitable features to allow this change. Therefore, in the future, it has to allow two-way direction power flows, communication, and automated controls to fully manage the system and customers. The resulting system is defined as the smart grid. This article analyses the smart grid state of play within China, the US, and the EU, assessing the completion state of each smart grid technology and integrated asset. The analysis related to these countries presented here shows that the smart grid overall state of play in China, the US, and the EU are equal to 18%, 15%, and 13%, respectively, unveiling the need related to further efforts and investments in these countries for the full smart grid development.

The present work provides an analysis of the potential impact of fossil-based Low Carbon Aviation Fuels (LCAF) for the European aviation sector, with a time horizon to 2050. LCAF are a crude-derived alternative to kerosene, offering some Green House Gas (GHG) savings, and have been defined by ICAO as eligible fuels for mitigating the environmental impact of aviation. A methodological framework to evaluate the EU technical potential for LCAF production is developed, based on data on crude utilization for jet fuel production in EU refineries, relevant carbon intensity reduction technologies, market prices, and aviation fuel volumes. Two different baselines for fossil-derived kerosene carbon intensity (CI) are considered: a global figure of 89 g_CO2e/MJ and an EU-27-specific one of 93.1 g_CO2eq/MJ. Three scenarios considering increasing levels of CI reduction are then defined, taking into account the current and potential commercial availability of some of the most relevant carbon intensity reduction technologies. The analysis demonstrates that, even if LCAF could offer GHG saving opportunities, their possible impact, especially when compared to the ambition level set in the most recent European legislative proposals, is very limited in most of the analysed scenarios, with the exception of the most ambitious ones. At 2030, a non-zero technical potential is projected only in the higher CI reduction scenario, ranging between 1.8% and 14.2% of LCAF market share in the EU-27 (equal to 0.6 to 4.75 Mtoe), depending on the considered Baseline for CI. At 2050, almost all considered scenarios project a larger technical potential, ranging between 6.9% and 22.2% for the global Baseline (2.21 to 7.13 Mtoe), and between 1.8% and 16.2% for the EU-27 Baseline (0.58 to 5.2 Mtoe). LCAF additional costs to current production costs are also discussed, given their relevance in large-scale deployment of these technologies, and are projected to range between 39 and 46.8 USD/toe.