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.
Highlights
The increasing electricity demand drives the Power System Expansion (PSE) so that the load is properly supplied
This paper presents a study of the major Constructive Heuristic Algorithms (CHA) used in the Transmission Expansion Planning (TEP) problem and has the main objective of analyzing the quality of the solution presented by each CHA in order to verify if the optimal solutions reported in the literature are not excluded from the reduced search space
The CHA techniques presented in the previous chapter were simulated using the Garver 6Bus academic system with and without generation rescheduling
Summary
The increasing electricity demand drives the Power System Expansion (PSE) so that the load is properly supplied. Some characteristics of the TEP problem make it difficult to solve it, becoming a real challenge in the power systems area Among these features are (de Mendonça, Junior, and Marcato 2014): − Non convex search space; − Isolated buses; − Integer nature of the problem; − Several investment options. It is expected that this search space reduction is made so as to keep the main expansion routes, that is, to maintain optimal and sub-optimal solutions and only discarding worse quality solutions In this context, this paper presents a study of the major CHAs used in the TEP problem and has the main objective of analyzing the quality of the solution presented by each CHA in order to verify if the optimal solutions reported in the literature are not excluded from the reduced search space.
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