Abstract
This paper describes an approach to model and to solve the Generation Expansion Planning Problem, GEP, using Genetic Algorithms. This approach was developed in order to help investors in new generation capacity to take decisions regarding new investments. This approach was developed in the scope of the implementation of electricity markets given that they eliminated the traditional centralized planning activities leading to the creation of several generation companies competing to supply the demand. As a result, the generation activity is more risky than in the past and so it becomes important to develop new tools to help decision makers to analyze the investment alternatives, having in mind the possible behavior of the competitors. The developed model aims at maximizing the expected profits that will be obtained by an investor, while it evaluates the reliability and the security of supply and it incorporates uncertainties related with the volatility of electricity prices, with the reliability of generation groups, with the evolution of the demand, and with the operation and investment costs The developed model and the implemented solution algorithm will be applied to a Case Study to illustrate the use of the developed approach to build the expansion plans. © 2013, Springer Science+Business Media Dordrecht.

Abstract
This paper results from the research conducted by the first author during the preparation of his MSc Thesis. This research aimed at investigating the impact on the market prices of the Iberian Electricity Market, MIBEL, due to increases of the feed-in generation, as such an increase is expected to occur in the next few years, namely for PV systems. This research was conducted using real market data publicly available in the web site of the Iberian Market Operator for 2016. To estimate this impact, for each trading hour of 2016 we considered new segments at price 0,0 (sic)/MWh to translate the priority given to this type of generation. These segments representing the new feed-in generation were then used together with the selling bids submitted by market agents to build the new aggregated selling curve. The new market price was finally obtained as the intersection of the new selling curve with the original buying curve, that was assumed unchanged. The global result indicates that if the feed-in generation increases by 25% regarding the values of 2016, then the average annual market price decreases by 6,57 % regarding the original value of 39,42 (sic)/MWh.

Abstract
Most generation expansion planning (GEP) methodologies developed under the monopolistic power industry framework were based on optimization models considering static and detailed descriptions of the power system's equipment and operating conditions. Many of these techniques are still in use in the current market framework, although planning in a liberalized environment is affected by a set of uncertainties and dynamics that traditional models are not designed to capture. In this setting, this research describes a GEP approach that uses System Dynamics (SD) to construct a simulation tool to provide planners, regulators, policy and decision makers with strategic and broader insights regarding policies to apply to power systems. The developed tool models the four Brazilian electricity submarkets, providing electricity prices and expansion scenarios in each region, given that policies recently applied at a country level are leading to different outcomes in each of these submarkets.

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
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.