Hybrid Genetic Algorithm for multi-objective Transmission Expansion Planning

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 CO 2 emissions, i.e. two conflicting objectives.

Multiyear and multi-criteria AC Transmission Expansion Planning model considering reliability and investment costs

“…The NDCCGA used 50 individuals in the population and the parent circle radius was set at 4 10 . The parameters for the CHA and the Hill Climbing methods are the same as used in [5]. The simulation involved solving about 1200000 AC-OPFs in about 100 hours.…”

“…CONCLUSIONS This paper presents a dynamic approach of the Transmission Expansion Planning problem using a multicriteria analysis that considers the total investment cost and the Expected Energy Not Supplied as the objectives. The problem was solved using the Non-Dominative CHAClimbing Genetic Algorithm tool developed by Vilaça and Saraiva in [5]. A Fuzzy decision making process is then used to select the final expansion plan among the solutions in the Pareto Front.…”

“…If not, that equipment is included back in the individual. These algorithms are detailed in [5] and they are used as a way to accelerate the convergence of the genetic algorithm reducing the investment cost or the EENS index of some solutions.…”

Evaluation of the performance of space reduction technique using AC and DC models in Transmission Expansion problems

“…The CHA and the DEPSO tools were applied to 3 different test systems indicated below. Case 6 and Case 30 Bus test systems are available in version 5.1 of the MATPOWER tool described in [5] Regarding the power flow solution, the maximum allowed flow was considered in emergency condition for all branches and the loads are modeled as dispatchable loads, that is, as negative real power injections with associated negative costs as detailed in [6].…”

A Meta-Heuristic Based on Genetic Algorithm for Selecting Bailiffs by Districts

“…At the best of our knowledge, there is no other way to solve this problem in the literature neither using a meta-heuristic nor a simplified enumerative algorithm. In fact, the works [2,5,8,12,16,18,19,20] solve problems in the legal area, but they are entirely different problems. In other words, there are no applications related to our problem.…”