Abstract
Transmission Expansion Planning (TEP) is a complex optimization problem that has the purpose of determining how the transmission capacity of a network should be enlarged, satisfying the increasing demand. This problem has combinatorial nature and different alternative plans can be designed so that many algorithms can converge towards local optima. This feature drives the development of tools that combine high robustness and low computational effort. This paper presents a comparative analysis and a detailed review of the main Constructive Heuristic Algorithms (CHA) used in the TEP problem. This kind of tools combine low computational effort with reasonable quality solutions and can be associated with other tools to use in a subsequent step in order to improve the final solution. CHAs proved to be very effective and showed good performance as the test results will illustrate.

Abstract
Simulated Annealing (SA) is a powerful tool for optimization problems that have several local optima. This tool has the ability to escape from a local optima accepting relatively bad solutions for a period and searching for good solutions in your neighborhood. This paper describes the use of SA based on Gaussian Probability Density Function as a decision support criteria in resolution of Transmission Expansion Planning (TEP) problem. This method consists in starting from an initial solution with all possible circuits added and over the iterations removing, replacing or adding new circuits. The method proved to be a reasonable computational effort and proved able to find optimal values known in the literature.

Abstract
Transmission Expansion Planning (TEP) problem aims at identifying when and where new equipment as transmission lines, cables and transformers should be inserted on the grid. The transmission upgrade capacity is motivated by several factors as meeting the increasing electricity demand, increasing the reliability of the system and providing non-discriminatory access to cheap generation for consumers. However, TEP problems have been changing over the years as the electrical system evolves. In this way, this paper provides a detailed historical analysis of the evolution of the TEP over the years and the prospects for this challenging task. Furthermore, this study presents an outline review of more than 140 recent articles about TEP problems, literature insights and identified gaps as a critical thinking in how new tools and approaches on TEP can contribute for the new era of renewable and distributed electricity markets.

Abstract
Transmission Expansion Planning (TEP) is traditionally carried out based on long-term forecasts for the peak load, which is viewed as the worst-case scenario. However, with the increasing renewable penetration, the peak load may not be longer the only worst-case to quantify new investment requirements. In fact, high off-peak load scenarios combined with low renewable generation can originate unforeseen bottlenecks. Besides, as TEP is a time-consuming problem, relaxed decision-making processes are often proposed in the literature to address the problem, however there is no guarantee that optimal planning has been achieved when some costs in the decision-making process are neglected. In this sense, this paper proposes a novel methodological framework to ensure that the system is sufficiently robust to overcome conditions with high electricity demand and low renewable energy, furthermore, this paper also presents a broad comparison between the common decision making processes adopted in the TEP literature aiming at providing a more insightful understanding of its impact on the total system cost. The optimization model, which is based on a multi-stage planning strategy, considers an AC-OPF model to enforce operational constrains, including the N-1 contingency criterion. The proposed model is tested through an evolutionary algorithm on a large test system with 118 bus. The uncertainties inherent to wind-solar-hydrothermal systems, demand and the life cycle of generation and transmission equipment are duly considered in the simulations. The results demonstrate the effectiveness of the proposed methodology in providing solution plans able to meet the demand even in scenarios with high off-peak load and low renewable generation, unlike the planning carried out considering only the peak load. Besides, the results also demonstrate that relaxed decision-making models may generate insufficient expansion plans.

Abstract
This paper proposes a new probabilistic model for active low-voltage prosumers suitable for distribution expansion planning studies. The load uncertainty of these consumers is considered through a range of load profiles by segmenting the energy consumption according to the different energy uses. Then, consumption adjustments are simulated using a nonhomogenous Poisson process based on the energy usage preferences and the financial gains according to the tariff scheme. A case study based on the modified IEEE 33-Bus test system with real data collected from a Brazilian distribution company is performed in order to analyze the impact of the load profiles in scenarios with high penetration of renewable distributed generation (DG). The experiments carried out reveal that considerable monetary savings in the expansion of the distribution grid can be achieved for this case study (up to 37%) as compared with the alternative with no active demand (AD) by exploiting the flexibility associated with the active behavior of prosumers as a response to price signals and/or by permitting adequate levels for the integration of DG into the distribution grid.

Abstract
Security and quality of supply continue to be major concern of power system operators. Thus, the expansion of transmission grids is certainly one of the major drivers to achieve this goal. In this scope, this paper presents a three-stage approach to solve the multi-year Transmission Expansion Planning (TEP) problem. This approach uses heuristic algorithms coupled with the Harmony Search (HS) metaheuristic and the Branch & Bound (B&B) algorithm. This hybrid method (HS-B&B) aims at finding the optimal multi-stage investment plan avoiding load shedding over the planning horizon. In this work, the AC-Optimal Power Flow (AC-OPF) is used to model the network as a way to consider the real operation conditions of the system. The method was validated using the Garver and the IEEE RTS 24 bus systems. Results demonstrate the reduction of computational effort without compromising the quality of the TEP.