Abstract
This paper studies the suitability of the novel Light Quasi-Peak (Light-QP) measurement method to assess the high-frequency disturbances generated by the vehicle-to-grid (V2G) technology, by comparing the performance of the new method with respect to the standardized CISPR 16-1-1 method. For this purpose, the quasi-peak (QP) outputs obtained by both methods are compared, a statistical study of the differences in the spectral results is performed and the computational requirements of the two methods are evaluated. This paper demonstrates that the novel Light-QP method is a lighter technique to assess the QP amplitude of the conducted disturbances, as it requires 10 times less Fourier transforms and at least less than 90 % storage to process a 3 s length measurement. Furthermore, the QP outputs provided by the Light-QP method are comparable to the outputs of a digital implementation of the CISPR 16, since the differences in results are within the uncertainty limits defined in IEC 61000-4-30 standard for power-quality instruments in the CISPR Band A. The Light-QP method could be essential for the detection of the V2G disturbances in low-voltage grid, since it can be easily implemented in inexpensive power quality measurement instruments. The Light-QP method was presented in the IEC SC77 A WG9 for its possible inclusion in the next edition of IEC 61000-4-30 standard. © 2023 IEEE.

Abstract
This work addresses the wind farm (WF) optimization layout considering several substations. It is given a set of wind turbines jointly with a set of substations, and the goal is to obtain the optimal design to minimize the infrastructure cost and the cost of electrical energy losses during the wind farm lifetime. The turbine set is partitioned into subsets to assign to each substation. The cable type and the connections to collect wind turbine-produced energy, forwarding to the corresponding substation, are selected in each subset. The technique proposed uses a genetic algorithm (GA) and an integer linear programming (ILP) model simultaneously. The GA creates a partition in the turbine set and assigns each of the obtained subsets to a substation to optimize a fitness function that corresponds to the minimum total cost of the WF layout. The fitness function evaluation requires solving an ILP model for each substation to determine the optimal cable connection layout. This methodology is applied to four onshore WFs. The obtained results show that the solution performance of the proposed approach reaches up to 0.17% of economic savings when compared to the clustering with ILP approach (an exact approach).

Abstract
The increasing number of vehicle to grid (V2G) charging stations connected to the electrical grid changes the characteristics of electrical distribution grids. Active power electronics introduces additional capacitance and inductance to the electrical grid and affects the frequency dependent grid impedance. This study shows the impact of a V2G charging station to the frequency dependent grid impedance up to 500 kHz. The LCL filter, the DC link capacitor and inductors cause parallel and series resonances. Resonance frequencies appear in a wide frequency range starting from 500 Hz up to 30 kHz. It is shown that the V2G charger can represent a source of supraharmonic emissions and the importance to consider supraharmonic emissions and the frequency dependent grid impedance to determine the impact of V2G chargers (active power electronics) to the electrical grid is outlined.

Abstract
Electric mobility has been one of the big bets for the reduction of CO2 in the transport sector. But, the integration of electric vehicles on a large scale, especially the charging of their battery will bring some challenges in the distribution of electricity to avoid problems in their transport. In this paper, the impact of introducing electric vehicle charging stations and renewable energy sources in a 69-node IEEE network will be analysed. The integration of charging stations into the grid leads to high losses and voltage drops that harm the network. On the other hand, the installation of Photovoltaic (PV) panels, besides the advantage of energy production, improves the profile of the grid in terms of voltage drops. The choice of the best location for the charging stations, as well as the best location for the renewable sources, is made using two genetic algorithms. The results obtained show that the genetic algorithms can solve the problem efficiently. © 2023 IEEE.

Abstract
As the world's population grows, there is a need to find new sources of energy that are more sustainable. Photovoltaic (PV) energy is one of the renewable energy sources (RES) expected to have the greatest margin for growth in the near future. Given their intermittency, RES bring uncertainty and instability to the management of the power system, therefore it is essential to predict their behavior for different time frames. This paper aims to find the most effective forecasting method for PV energy production that could be applied to different time frames. PV energy production is directly dependent on solar radiation and temperature. Several forecasting approaches are proposed in this paper. A multiple linear regression (MLR) model is proposed to predict the monthly energy production based on the climatic parameters of the previous year. Different approaches are proposed based on first predicting the temperature and radiation and then applying the PV mathematical models to predict the produced energy. Three methods are proposed to predict the climatic parameters: using the average values, the additive decomposition, or the Holt-Winters method. Comparing the errors of the four proposed forecasting methods, the best model is the Holt-Winters, which presents smaller errors for radiation, temperature, and produced energy. This method is close to additive decomposition. © 2023 IEEE.

Abstract
Renewable energy communities (REC) are an increasingly interesting solution for all energy market stakeholders. In RECs consumers and producers come together to form energy cooperatives with a strong incorporation of renewables in order to make the market and energy trading more advantageous for both sides. This growing trend has been followed by several studies aimed at understanding which are the best models for energy sharing within the community. This paper proposes different models of energy sharing within the community and evaluates their efficiency. Energy sharing can be based on constant coefficients or variable coefficients based on the net consumption of the self-consumers. This study proposes a new methodology based on a hybrid model. The results show the advantages and challenges of the individual energy-sharing models, showing that up to 41% of the energy imports from the grid can be reduced. © 2023 IEEE.