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The use of technologies such as Internet of Things (IoT), data processing and blockchain have allowed companies to serve their customers with better quality, efficiency, reliability and in the shortest possible time. The growing adoption of electric vehicles on the market has increased the demand for batteries that may have numerous manufacturers. Life expectancy is affected on manufacture, but also on operational conditions. A large number of parameters have a role on battery's health and thousands of data need to be evaluated and combined. The present work investigates the scenario of the battery industry in order to implement a blockchain-based platform for the supply chain implementation thus allowing a better control on performance of batteries and environmental impact. To achieve this goal, the authors carried out a systematic review with the following steps: identification of relevant studies, evaluation and summary of similar studies, comparison and extraction of data from the papers. The main motivation of this work is the use of the literature for justifying the use of the blockchain technology to track batteries and for identifying the main challenges in the related markets that can be addressed by this technology. The results of this systematic review show that the development of a blockchain-based platform for battery tracking will allow for greater transparency across the entire supply chain: production, reuse, recycling, disposal. Trasparency and traceability prevent clandestine markets, misuse and release of pollutants. Adressing these topics forsters the successful implemention of electric vehicles in the market.

The paper analyses the dramatic effects of the first wave of the COVID-19 pandemic on the Italian power system, considering the impacts on load and generation, market issues and ancillary services provision. During the period between March and May 2020, the Italian scenario was considered as exemplary due to the large reduction of loads and thermal generation caused by the hard lockdown across the Country. Italy has experienced the most severe effects of pandemic in terms of increase of death rates and of pressure on the health system as well as severity of the countermeasures. As a domino effect, the total lockdown of one of the most industrialized countries in the world has created an emergency situation that has put a big stress on the power system, reducing the possibility for the system to recover under contingencies. The weakness of the system in such an extreme situation is analyzed in depth using open data, while some countermeasures for providing resilience to the power systems in such cases are analyzed in simulation. Two technologies are here considered as exemplary countermeasures for providing resilience to the Italian power system: Fast Frequency Response by Photovoltaic and Wind plants with rated power above 10 MVA and the massive implementation of Demand Response programs through Energy communities and aggregation using Blockchain technology. The simulations show, on one hand, that the frequency recovery under contingency seems compromised in the current situation and that the support from such innovative technologies can provide a significant relief bringing the operational features close to those before the COVID-19 pandemic.

The new European Directive RED-II on the promotion of the use of renewable energy sources has introduced the concept of the Renewable Energy Community. This new entity has the potential to change the relations among end-users and the latter and the other actors of the energy sector. In particular, the electrical sector appears the most influenced by the implementation of this paradigm. Indeed, the need for self-consumption and for exploiting the full potential of renewable energy in a Renewable Energy Community influences how the end-users exchange energy with the power grid, the rules for their connection to the grid, the implementation of Demand Response and other energy management programs. For this reason, after a review of the existing legal framework, this paper investigates various aspects concerning the interaction of Renewable Energy Communities with the power system, highlighting what issues must still be addressed for a complete implementation of Renewable Energy Communities and their integration in the power system, with a focus on the Italian context.

This paper proposes a new model for short-term forecasting power generation capacity of large-scale solar power plant (SPP) in Vietnam considering the fluctuations of weather factors when applying the Long Short-Term Memory networks (LSTM) algorithm. At first, a configuration of the model based on the LSTM algorithm is selected in accordance with the weather and operating conditions of SPP in Vietnam. Not only different structures of LSTM model but also other conventional forecasting methods for time series data are compared in terms of error accuracy of forecast on test data set to evaluate the effectiveness and select the most suitable LSTM configuration. The most suitable configuration has been selected and applied on Thanh Thanh Cong No 1 (TTC) SPP with 2 input cases: real historical weather data and forecasted weather data. The results show that second case gives a much larger Mean Absolute Percentage Error (MAPE) than that of first case (10.857% versus 3.491%). Based on above experiment, new additional features are proposed to improve the selected LSTM model precision and cope with the problem of error due to weather forecast data. The result of the application of the new prediction model for TTC solar plant indicates that the MAPE is reduced from 10.857% to 9.881%.

The paper presents a complete technique, based on the combination of algorithms, devoted to minimize losses and increase efficiency of Total Cross Tied (TCT) connected photovoltaic (PV) systems under non-homogeneous solar irradiation, based on irradiance equalization criterion. Irradiance equalization is achieved by changing the connections of the solar panels adaptively by a dynamic switching matrix so that total solar radiation on parallel circuits is the most equalized. In this paper, the authors introduce two algorithms. The first one is SmartChoice (SC) algorithm, which is combined with Dynamic Programming (DP) in order to create a hybrid method and obtain better results as compared to established methods for irradiance equalization. The second one is the control algorithm improvement method from Munkres' Assignment Algorithm (MAA) that helps to increase processing speed and lengthen the lifetime of the solar power system by 56% compared with the older MAA. By emulating and experiencing the operation of the PV system under non-homogeneous irradiation condition, obtained results show efficiency and benefits of the proposed method applied to the solar power system operation while lengthening the lifetime of the switching matrix.