Abstract
Currently, there is increasing implementation of renewable energy communities, where consumers and producers come together to form energy cooperatives. This growing trend has been accompanied by several studies aiming to optimize energy exchanges and sharing inside the community, always taking into account the most favorable tariff regimes for community members. This paper presents an analysis that, based on applying a linear programming model, optimizes energy transactions in a renewable energy community with the integration of storage systems. The results show the developed model's effectiveness, presenting substantial profits for the community.

Abstract
The last two decades have witnessed a new paradigm in terms of electrical energy production. The production of electricity from renewable sources has come to play a leading role, thus allowing us not only to face the global increase in energy consumption, but also to achieve the objectives of decarbonising the economies of several countries. In this scenario, where onshore wind energy is practically exhausted, several countries are betting on constructing offshore wind farms. Since all the costs involved are higher when compared to onshore, optimising the efficiency of this type of infrastructure as much as possible is essential. The main aim of this paper was to develop an optimisation model to find the best wind turbine locations for offshore wind farms and to obtain the wind farm layout to maximise the profit, avoiding cable crossings, taking into account the wake effect and power losses. The ideal positioning of wind turbines is important for maximising the production of electrical energy. Furthermore, a techno-economic analysis was performed to calculate the main economic indicators, namely the net present value, the internal rate of return, and the payback period, to support the decision-making. The results showed that the developed model found the best solution that maximised the profits of the wind farm during its lifetime. It also showed that the location of the offshore substation played a key role in achieving these goals.

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
Wind power is a major source of green energy production. However, the energy generation of wind power is highly affected by uncertainty. Here, we consider the problem of designing the cable network that interconnects the turbines to the substation in wind farms, aiming to minimize both the infrastructure cost and the cost of the energy losses during the wind farm's lifetime. Nonetheless, the energy losses depend on wind direction and speed, which are rarely known with certainty in real situations. Hence, the design of the network should consider these losses as uncertain parameters. We assume that the exact probability distribution of these parameters is unknown but belongs to an ambiguity set and propose a distributionally robust two-stage mixed integer model. The model is solved using a decomposition algorithm. Three enhancements are proposed given the computational difficulty in solving real problem instances. Computational results are reported based on real data.

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.