Abstract
This paper aims to describe a new tool to solve the Transmission Expansion Planning problem (TEP). The Non-Dominative CHA-Climbing Genetic Algorithm uses the standard blocks of Genetic Algorithms (GA) associated with an improvement of the population building block using Constructive Heuristic Algorithms (CHA) and Hill Climbing Method. TEP is a hard optimization problem because it has a non convex search space and integer and nonlinear nature, besides, the difficulty degree can be further increased if it includes more than one objective. In this work, a multi-objective TEP approach is detailed using an AC Optimal Power Flow to generate the set of Pareto solutions using the investment cost and the level of CO2 emissions, i.e. two conflicting objectives.

Abstract
The current Portuguese Tariff Code dates from December 2014 and requires that the Distribution Network Operators (DSO), submit to the Portuguese Energy Services Regulatory Agency, ERSE, a plan for a pilot experiment and a Cost Benefit Analysis (CBA) regarding the introduction of dynamic options in the Access Tariffs in Portugal. In view of this request, EDP Distribuicao, the main Portuguese DSO, established a contract with INESC TEC to conduct these studies and to prepare a report to submit to ERSE by June 2016. In this scope, this paper reports the results obtained so far namely regarding the CBA analysis. This analysis includes the identification of critical hours during which dynamic tariffs can be activated, the estimate of the impact of demand transfers to adjacent hours on the electricity market Social Welfare Function, on network losses, on the investment network avoided costs due to the possible deferral of reinforcements or expansions and on the costs of contracting reserves. These items were estimated along a period of 15 years and together with the estimate of the implementation costs of dynamic tariffs were used to conduct the mentioned CBA analysis.

Abstract
This paper presents two new approaches to solve the reconfiguration problem of electrical distribution systems (EDSs) with variable demands, using the CLONALG and the SGACB algorithms. The CLONALG is a combinatorial optimization technique inspired by biological immune systems, which aims at reproducing the main properties and functions of the system. The SGACB is an optimization algorithm inspired by natural selection and the evolution of species. The reconfiguration problem with variable demands is a complex combinatorial problem that aims at identifying the best radial topology for an EDS, while satisfying all technical constraints at every demand level and minimizing the cost of energy losses in a given operation period. Both algorithms were implemented in C++ and test systems with 33, 84, and 136 nodes, as well as a real system with 417 nodes, in order to validate the proposed methods. The obtained results were compared with results available in the literature in order to verify the efficiency of the proposed approaches.

Abstract
This paper describes the application of the specialized genetic algorithm of Chu-Beasley to solve the Distribution System Reconfiguration, DSR, problem considering different demand scenarios. This algorithm is an approach inspired in the natural selection and evolution of species. The reconfiguration problem of distribution networks taking into account different demand scenarios aims at identifying the most adequate radial topology of a distribution system assuming that this topology is used for all demand scenarios under study. This search is driven by the minimization of the cost of energy losses in the network along a full operation year. The performance of the algorithm is evaluated considering test systems having 33, 70, 84 and 136 buses and a real system with 417 buses. The obtained results confirm the robustness and efficiency of the developed approach and its potential to be used in distribution control centers.

Abstract
This paper describes a hybrid tool to perform Static Transmission Expansion Planning, STEP, studies and its application to the Garver6-Bus academic system and to the Southern Brazilian Transmission equivalent real system. The developed STEP tool integrates two phases as follows. The first one uses Constructive Heuristic Algorithms (CHA) to reduce the search space, and the second uses Particle Swarm Optimization (PSO) to identify the final solution. This hybridization between CHAs and PSO proved to be very effective and shows good performance to reduce the size of the STEP search space and to identify good quality solutions. These are relevant issues given the combinatorial nature of investment problems leading to the explosion of the number of alternative plans, one of the greatest difficulties faced in this planning problem.

Abstract
The Iberian power systems went through important changes at the legal, regulatory and organizational levels in the last 20 years. One of the most relevant ones was the increasing penetration of distributed generation, namely wind parks, together with the development of the common market involving Portugal and Spain. In Portugal, distributed generation is paid using feed in tariffs while in Spain it can choose between receiving a regulated feed in tariff or the market price plus a participation prize. The feed in scheme is now under discussion since it is argued that it represents an excessive cost that is internalized in the end user tariffs. However, this discussion is frequently conducted without complete knowledge of the real impact of wind power on the electricity market price, since it contributes to reduce the demand on the market thus inducing a price reduction. To clarify these issues we used a long term System Dynamics based model already reported in a previous publication to estimate the long term evolution of the market price. This model was applied to the Iberian generation system using different shares of wind power capacity to quantify the impact of wind power on the day-ahead electricity market price.