Abstract
Due to the complexity and great relevance of the transmission network expansion planning (TNEP) for electrical systems, this topic remains on the focus of the academic and industry communities. Therefore, this paper proposes a new approach to deal efficiently with the basic formulation of this problem, combining low computational effort and good quality of the obtained solutions. In this approach four factors contribute to solve TNEP problem more efficiently: (i) the investment decisions are selected using a new Constructive Heuristic Algorithm (CHA); (ii) the proposed CHA includes two stages, using the relaxation of the decision integers variables through the hyperbolic tangent function and the setting of its function's slope; (iii) the performance index that was adopted was modified regarding what was reported in the literature; (iv) the use of the primal-dual interior point optimization technique allows the representation of the nonlinearities in the problem: transmission power losses and the hyperbolic tangent function (investment decision). The quality and effectiveness of the proposed algorithm is verified using two real power systems, where the proposed CHA is able to lead to better quality solutions than the ones reported in the literature.

Abstract
Feed in generation was introduced in Portugal in 1988 to induce investments in endogenous and renewable energy resources. The feed in mechanism was adapted along time and its application was very successful so that currently more than 40% of the installed capacity is under this regime. Typically feed in tariffs are larger than average market prices so that there is a recurrent debate on whether this regime is pressing or not the end-user tariffs. This paper reports the main results obtained by the first author in his MSc Thesis in assessing the total generation cost under the current legal provisions on one side and, on the other, eliminating feed in generation from the mix. The results obtained using public data for 2017 indicate that the generation cost with feed in generation is 2,70% larger than the value obtained if it was eliminated from the market clearing process. © 2020 IEEE.

Abstract
This paper presents the results of an Agent-Based Model developed to simulate the Iberian Electricity Market, with special focus on the modelling of hydro power plants. To simulate the agent's dynamics in the day-ahead market, it was developed a bidding strategy based on a Q-Learning procedure. In the computation area, the recent years brought the discussion around artificial intelligence to a new upper level to complement traditional models, driven by the increased hardware computer capabilities, as well as new developments in the machine learning area. Reinforcement Learning models, as Q-Learning, are being widely used to represent complex systems such as electricity markets. The developed model is designed to simulate in a detailed way the hydro units that have a large impact in the electricity market common to Portugal and Spain. Apart from describing the developed model, this paper also includes results from its application to the Iberian Market case along 2018. © 2020 IEEE.

Abstract
The green hydrogen (H-2) technology has an important role to play in the European Union energy strategy towards decarbonization. Apart from traditional H-2 industrial usages, there is an increasing attention to its use in the heavy transport sector, in other energy-intensive industries, and in heating applications. Green H-2 production is planned to be based on renewable electricity generation and its production at an industrial scale may have a significant impact on the electricity markets. This research assesses the electricity cost of producing H-2 and its impact on the Iberian electricity market. Different evolution scenarios including a partially flexible H-2 demand, based on the Spanish and Portuguese energy and climate plans, have been considered for this assessment.

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.