Abstract
Lithium-ion battery is one of the most used energy storage technologies in recent times. Enhancing the life span of the Lithium-ion battery is an issue that has technical and economic contributions. This paper addresses improving the life span of Lithium-ion batteries through the appropriate way of charging in the PV system. A modified control methodology is applied for the DC-DC converter-based charger that implements the maximum power point tracking algorithm with considering the battery-safe charging recommendations. The control methodology has been implemented in Matlab Simulink and effectively verified through the simulation results.

Abstract
A method is presented to determine the appropriate number of photovoltaic panels that should be installed in an end-user photovoltaic installation to guarantee the supply of energy to the load during the hours of solar radiation, according to factors such as the installation area and global solar radiation. Solar radiation is predicted by approximating the daily distribution of global irradiance through a Gaussian function, which is subsequently corrected using a heuristic approach. Meteorological parameters are used as input data such as the daily solar insolation and the maximum global irradiance for each day; this last parameter is obtained through an expert system based on fuzzy logic that was programmed and trained with the data of ambient temperature and relative humidity that were obtained in the processing stage. Output from this expert system is the predicted values of maximum radiation obtained for each day for a selectable time interval. With the predicted solar radiation, the generation of electrical energy from the photovoltaic panels is calculated. The load is randomly modeled from a pattern of the energy demand of the building to be powered by the photovoltaic system. The number of photovoltaic panels needed is found with the information acquired in the previous stages and the information of the energy demand of the load and the installation area. The results are the number of solar panels that would be needed at all hours of the day from which the radiation prediction was made.

Abstract
The present work aims to compare several load disaggregation methods. While the supervised alternative was found to be the most competent, the semi-supervised is proved to be close in terms of potential, while the unsupervised alternative seems insufficient. By the same token, the tests with long-lasting data prove beneficial to confirm the long-term performance since no significant loss of performance is noticed with the scalar of the time-horizon. Finally, the patchwork of new parametrization and methodology fine-tuning also proves interesting for improving global performance in several methods.

Abstract
In Europe, clean distributed generation, DG, is perceived as a crucial instrument to build the path towards carbon emission neutrality. DG already reached a large share in the generation mix of several countries and the reduction of technical losses is one of its most mentioned advantages. In this scope, this paper discusses the weaknesses of this postulation using real networks. The adopted methodology involves the power flow simulation of a collection of real networks, using 15 min real measurements of loads and generations for a whole year. The clustering of similar cases allows identifying the situations that cause higher losses. A complementary objective of this research was to define an approach to mitigate this problem in terms of identifying the branches that, if reinforced, most contribute to losses reduction. The results obtained confirm the rationality of the proposed methodology.

Abstract
The financial planning of distribution systems usually includes the prediction of annual mandatory investments, concerning the resources that the DSO is compelled to allocate as a result of new network connections, required by new consumers or new energy producers. This paper presents a methodology to estimate the mandatory investments that the DSO should do in the distribution network. These estimations are based on historical data, load growth expectations and various socioeconomic indices. However, the available database contains very few annual investment examples (one aggregated value per year since 2002) compared to the large number of variables (potential inputs), which is a factor of regression overfitting. Thus, the applicable regression techniques are restrained to simple but efficient models. This paper describes a new methodology to identify the most suitable estimation models. The implemented application automatically builds, selects, and tests estimation models resulting from combinations of input variables. The final forecast is provided by a committee of models. Results obtained so far confirm the feasibility of the adopted methodology.

Abstract
Recent advances in electric vehicle (EV) charging capability have seen a wide growth in the consumer market, which will continue to increase in future years with favourable policy incentives. However, the uncontrolled connection and charging of EV may have an adverse effect on three-phase distribution grids operation. This paper presents the impact of EV integration in a real LV Portuguese urban network. It analyses the network loading, energy losses, and voltage imbalances, under different scenarios of EV charging location and phase connection. The DIgSILENT Power Factory software is used in the voltage imbalance studies. Preliminary results show that the voltage drop in the analysed network is significantly affected by the location of the EV. Furthermore, as expected, the unbalanced EV loading leads to an increase of voltage unbalance between phases which is more pronounced when higher levels of EV are considered. © 2021 The Institution of Engineering and Technology.