Differential evolution algorithm for static and multistage transmission expansion planning
ABSTRACT A novel differential evolution algorithm (DEA) is applied directly to the DC power flow-based model in order to efficiently solve the problems of static and multistage transmission expansion planning (TEP). The purpose of TEP is to minimise the transmission investment cost associated with the technical operation and economical constraints. Mathematically, long-term TEP using the DC model is a mixed integer nonlinear programming problem that is difficult to solve for large-scale real-world transmission networks. In addition, the static TEP problem is considered both with and without the resizing of power generation in this research. The efficiency of the proposed method is initially demonstrated via the analysis of low, medium and high complexity transmission network test cases. The analysis is performed within the mathematical programming environment of MATLAB using both DEA and conventional genetic algorithm and a detailed comparative study is presented.
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ABSTRACT: This paper proposes the application of ant colony optimization (ACO) to solve a static transmission expansion planning (STEP) problem based on a DC power flow model. The major objective is to minimize the investment cost of transmission lines added to an existing network in order to supply the forecasted load as economically as possible and subject to many system constraints i.e. the power balance, the generation requirements, line connections and thermal limits. The Garver's six-buses system, is analyzed to appraise the feasibility of the ACO. The experimental results obtained by ACO are compared to those obtained by the conventional approaches of the Genetic Algorithm (GA), and the Tabu Search (TS) algorithm. The results show that the ACO method outperforms other methods in convergence characteristic and computational efficiency.
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ABSTRACT: The optimization problem in transmission system expansion planning (TSEP) is a mixed-integer nonlinear programming problem of combinatorial nature that leads to an extremely large number of alternative solutions for medium and large size electric power systems. Due to its complex characteristics, heuristic optimization has become an effective solver. In this paper, a novel heuristic optimization algorithm, namely the Mean-Variance Mapping Optimization (MVMO), is adapted to handle the TSEP. Additionally, a variant of MVMO termed as collaborative MVMO (CMVMO) is introduced. CMVMO exploits multicore technology of modern computers as well as distributed computing to enhance the performance of the former MVMO. Several tests were performed on three benchmark systems with different mesh complexity in their topologies in order to compare the performance of both, MVMO and CMVMO, with other evolutionary algorithms. Simulation results show that CMVMO constitutes a powerful algorithm and should earn more attention.Transmission and Distribution: Latin America Conference and Exposition (T&D-LA), 2012 Sixth IEEE/PES; 01/2012