Abstract
Hydrothermal systems should be characterized by a transmission-intensive nature in order to deal with climatic phenomena which, for example, can determine dry conditions in one region while there are large rainfalls in another one. Thus, the grid must be robust to deal with the different export/import patterns among regions and accommodate several economic dispatches. This paper describes a multiyear probabilistic Transmission Expansion Planning, TEP, model that uses Evolutionary Particle Swarm Optimization (EPSO) to deal with the uncertainties present in hydrothermal systems. The numerical simulations were conducted using the IEEE 24 bus reliability test system in which the planning horizon is 10 years and the load growth is 2,5% per year. The results highlight the importance of adopting expansion strategies to reduce the risk and consider the inflow variations in this type of systems.

Abstract
The increasing integration of distributed renewable energy sources, such as photovoltaic (PV) systems, requires adequate regulatory schemes in order to reach economic sustainability. Incentives such as Feed-in Tariffs and Net Metering are seen as key policies to achieve this objective. While the Feed-in Tariff scheme has been widely applied in the past, it has now become less justified mainly due to the sharp decline of the PV system costs. Consequently, the Net Metering scheme is being adopted in several countries, such as Brazil, where it has is in force since 2012. In this context, this paper aims to estimate the minimum monthly residential demand for prosumers located in the different distribution concession areas in the interconnected Brazilian system that ensures the economic viability of the installation of PV systems. In addition, the potential penetration of PV based distributed generation (DG) in residential buildings is also estimated. This study was conducted for the entire Brazilian interconnected system and it demonstrates that the integration of distributed PV systems is technical-economic feasible in several regions of the country reinforcing the role of the distributed solar energy in the diversification of Brazilian electricity matrix.

Abstract
This work presents a method based on metaheuristics to solve the problem of Static (STNEP) and Dynamic (DTNEP) Transmission Network Expansion Planning in electrical power systems. The result of this formulation is mixed-integer nonlinear programming (MINLP), where the difficulties are intensified in the DTNEP by the temporal coupling. Therefore, a methodology was developed to reach the solution in three different stages: The first one is responsible for obtaining an efficient set of best candidate routes for the expansion; the metaheuristic optimization process, Harmony Search (HS), is used to find STNEP's optimal solution and its neighborhood that provides a DTNEP candidate zone; lastly, a hybrid algorithm that mixes the HS and Branch & Bound (B&B) concepts is adapted to provide the optimal DTNEP. In this study, the lossless linearized modeling for load flow is used as a representation of the transmission network. Tests with the Garver and southern Brazilian systems were carried out to verify the performance method. The computational time saving for the STNEP and DTNEP prove the efficacy of the proposed method.

Abstract
The unbundling of the electricity sector in several activities, some of them provided in a regulated way and some others under competition, poses a number of challenging problems namely because in several areas there are conflicting objectives associated to different stakeholders. These different views and objectives paved the way to the development of new multiobjective tools able to represent this new paradigm. In this scope, this paper presents a multiobjective (MO) formulation for the Transmission Expansion Planning (TEP) problem using a new solution approach that combines concepts of evolutionary computation and multi agent population algorithms. The new proposed tool is termed as Multi-Population and Multiobjective Evolutionary Particle Swarm Optimization - MEPSO-II. The TEP problem is handled in a realistic way preserving the holistic view over the entire planning horizon and the true grid behavior because it considers the multi-stage nature of the problem and we use an AC Optimal Power Flow (AC-OPF) model to gain insight on the operation conditions of the network. The multi objective formulation considers the total system cost, on one side, and the Expected Power Not Supplied (EPNS), on the other. The total system cost comprises the investment cost in new equipment and the operation costs while the EPNS takes into account the uncertainties related to the non- ideal behavior of system components using a non-chronological Monte Carlo simulation. Numerical simulations are conducted using the IEEE 24 and the 118 Bus Test Systems in order to compare the proposed MO tool against other algorithms through performance evaluation indices. Although being a higher time-consuming tool, the MEPSO-II enables improving the Pareto-Front and therefore it gives more insight to transmission network planners when compared with other consolidated algorithms described in the literature.

Abstract
Electric vehicles will certainly play an important and increasing role in the transport sector over the next years. As their number grows, they will affect the behavior of the electricity demand seen not only by distribution but also by transmission networks and so changes will also occur in the operation and expansion planning of the power systems. In this sense, this paper addresses the impact of large fleets of Plug-in-Electric Vehicles (PEVs) in transmission equipment investments. The developed model uses evolutionary particle swarm optimization (EPSO) to handle the planning problem over different scenarios regarding the evolution of PEVs and their impact on the demand. These scenarios consider the PEVs penetration level, the availability of charging and the related charging policies. The paper includes a Case Study based on the IEEE 24 busbar power system model for a 10-year period. The model uses an AC Optimal Power Flow to analyse the operation of the system for different investment paths over the years and the results show that coordinating the charging of PEVs can be an interesting solution to postpone the investments in transmission equipment thus reducing the associated costs.

Abstract
A flexible control of the distribution system is an efficient strategy to enhance the grid reliability and quality of service, in this sense, the maneuver devices play an important role to reach flexibility under network faults. In this direction, the present work proposes a new approach to solve the allocation problem of optimal maneuver devices in electric distribution systems (EDS) that considers both permanent and temporary faults. The considered maneuver devices are normally closed switches coupled to the beginning or to the end of distribution branches. The objective is to improve the system reliability with minimal investment cost. The metaheuristic and bio-inspired technique known as artificial immune system (AIS) is applied to handle the discrete feature of the switch allocation problem. The index considered to evaluate the reliability is the system expected outage cost to customers due to supply outages (ECOST). The paper includes a case study with four different simulations using the well-know RBTS Bus 4 test system The obtained results were compared to the literature ones and proved that the proposed approach can lead to promising solutions that establish a suitable trade-off between the reliability and the utility costs. © 2018 Power Systems Computation Conference.