Abstract
Transmission Expansion Planning (TEP) is an optimization problem that has a non-convex and combinatorial search space so that several solution algorithms may converge to local optima. Therefore, many works have been proposed to solve the TEP problem considering its relaxation or reducing its search space. In any case, relaxation and reduction approaches should not compromise the quality of the final solution. This paper aims at analyzing the performance of a search space technique using a Constructive Heuristic Algorithm (CHA) admitting that the TEP problem is then solved using a Discreet Evolutionary Particle Swarm Optimization (DEPSO). On one hand the reduction quality is performed by analyzing whether the optimal expansion routes are included in the CHA constrained set and, on the other hand, the relaxation quality of the DC model is analyzed by checking if the optimal solution obtained with it violates any constraint using the AC model. The simulations were performed using three different test systems. The results suggest that the proposed CHA provides very good results in reducing the TEP search space and that the adoption of the DC model originates several violations if the full AC model is used to model the operation of the power system.

Abstract
Multiyear Transmission Expansion Planning (TEP) aims to determine how and when a transmission network capacity should be expanded taking into account an extended horizon. This is an optimization problem very difficult to solve and that has unique characteristics that increase its complexity such as its non-convex search space and its integer and nonlinear nature. This paper describes a hybrid tool to solve the TEP problem, including a first phase to select a list of equipment candidates conducted by a Constructive Heuristic Algorithm (CHA), and a second phase that uses Discrete Evolutionary Particle Swarm Optimization (DEPSO) for the final planning. Both phases use the AC power flow model as a way to improve the realism of the developed tool. The paper includes a case study based on the IEEE 24-Bus Reliability Test System and the results show that tools based on swarm intelligence applied to reduced search spaces are able to find good quality solutions with low computational effort.

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
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
One of the major concerns in Power Systems is surely related with their reliability. Long-term expansion planning studies traditionally use the well-known deterministic "N-1" contingency criterion. However, this criterion is applied based on worst-case analyses and the obtained plan may originate over-investments. Differently, probabilistic reliability approaches can incorporate different type of uncertainties that affect power systems. In this work, a long term multi-criteria AC Transmission Expansion Planning model was developed considering two objectives - the probabilistic reliability index Expected Energy Not Supplied (EENS) and the investment cost. The Pareto-Front associated with these two objectives was obtained using Genetic Algorithms and the final solution was selected using a fuzzy decision making function. This approach was applied to the IEEE 24 Bus Test System and the results ensure its robustness and efficiency.

Abstract
In recent years, power systems have been watching important advancements related with Plug-in-Electrical Vehicles (PEVs), Demand Side Management (DSM), Distributed Generation (DG), Microgrid and Smart Grid installations that directly affect distribution networks while impacting indirectly on Transmission studies. These changes will lead to an extra flexibility on the transmission-distribution boundary and to a significant modification of the load patterns, that are an essential input to planning studies. In this scope, this paper describes a multiyear Transmission Expansion Planning (TEP) solved by Evolutionary Particle Swarm Optimization (EPSO) and incorporating the impact of solar DG penetration. The primary substation load profiles and the solar generation profiles are taken into account on the planning problem. The numerical simulations were conducted using the IEEE 24 bus reliability test system in which the planning horizon is 3 years and the load growth is 2.5 % per year. If demand and solar DG peaks are coincident, then the liquid demand seen by the transmission network gets reduced enabling a reduction of investment costs. In the tested cases, these peaks were not coincident so that the optimal expansion plan remains unchanged even though the injected power from DG is large. This stresses the fact that solar DG may not on an isolated way contribute to alleviate the demand seen by transmission networks but should be associated with storage devices or demand side management programs.