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.

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
This study aims to develop an approach for the management of a forest of maritime pine located in the north region of Portugal. The forest is classified into five public lands, the so-called baldios, extending over 4432 ha. These baldios are co-managed by the Official Forest Services and the local communities mainly for timber production purposes. The forest planning involves non-spatial and spatial constraints. Spatial constraints dictate a maximum clearcut area and an exclusion time. An integer programming model is presented and the computational results are discussed.

Abstract
The study addresses the special case of a management plan for maritime pine (Pinus pinaster Ait.) in common lands. The study area refers to 4,432 ha of maritime pine stands in North Portugal (Perimetro Florestal do Barroso in the county of Ribeira de Pena), distributed among five common lands called baldio areas. Those lands are co-managed by the Official Forest Services and the local communities, essentially for timber production, using empirical guidance. As the current procedure does not guarantee the best thinning and clear-cutting scheduling, it was considered important to develop "easy-to-use" models, supported by optimization techniques, to be employed by the forest managers in the harvest planning of these communitarian forests. Planning of the thinning and clear-cutting operations involved certain conditions, such as: (1) the optimal age for harvesting; (2) the maximum stand density permitted; (3) the minimum volume to be cut; (4) the guarantee of incomes for each of the five baldios in at least a two year period; (5) balanced incomes during the length of the projection period. In order to evaluate the sustainability of the wood resources, a set of constraints lower bounding the average ending age was additionally tested. The problem was formulated as an integer linear programming model where the incomes from thinning and clear-cutting are maximized while considering the constraints mentioned above. Five major scenarios were simulated. The simplest one allows for silvicultural constraints only, whereas the other four consider these constraints besides different management options. Two of them introduce joint management of all common areas with or without constraints addressing balanced distribution of incomes during the plan horizon, whilst the other two consider the same options but for individual management of the baldios. The proposed model is easy to apply, providing immediate advantages for short and mid-term planning periods compared to the empirical methods of harvest planning. Results showed that maximization of production is reached when there are silvicultural restrictions only and when forest management units are regarded as a joint undertaking. The individualized management with a balanced distribution of incomes is an interesting option as it does not drastically reduce the optimal solution while assuring benefits at least every two years.

Abstract
One of the classical problems in the structural optimization field is the Truss Topology Design Problem (TTDP) which deals with the selection of optimal configuration for structural systems for applications in mechanical, civil, aerospace engineering, among others. In this paper we consider a TTDP where the goal is to find the stiffest truss, under a given load and with a bound on the total volume. The design variables are the cross-section areas of the truss bars that must be chosen from a given finite set. This results in a large-scale non-convex problem with discrete variables. This problem can be formulated as a Semidefinite Programming Problem (SDP problem) with binary variables. We propose a branch and bound algorithm to solve this problem. In this paper it is considered a binary formulation of the problem, to take advantage of its structure, which admits a Knapsack problem as subproblem. Thus, trying to improve the performance of the Branch and Bound, at each step, some valid inequalities for the Knapsack problem are included.