Tabu Search Based Algorithm for Multi-Objective Network Reconfiguration Problem

T. Thakur, Jaswanti Jaswanti

Abstract


Abstract: The electric power distribution usually operates in a radial configuration, with tie switches between circuits to provide alternate feeds. The losses would be minimized if all switches were closed, but this is not done because it complicates the system’s protection against over currents. Whenever components fail, some of the switches must be operated to restore power to as many customers as possible. As loads vary with time, switch operations may reduce losses in the system. All of these are applications for reconfiguration.
The reconfiguration problem is combinatorial problem, which precludes algorithms that guarantee a global optimum. Most existing reconfiguration algorithms fall into two categories. In the first, branch exchange, the system operates in a feasible radial configuration and the algorithm opens and closes candidate switches in pairs. In the second, loop cutting, the system is completely meshed and the algorithm opens candidate switches to reach a feasible radial configuration. Reconfiguration algorithms based on neural network, heuristics, genetic algorithms, and simulated annealing have also been reported, but not widely used.
The objective of the paper presented in this work is to make a Tabu Search (TS) based algorithm for multi-objective programming to solve the network reconfiguration problem in a radial distribution system. Here six objectives are considered in conjunction with network constraints. The main objective of research is allocation of optimal switches to reduce the power losses of the system. It is tested for 33 bus systems. Simulation results of the case studies demonstrate the effectiveness of the solution algorithm and proved that the TS is suitable to solve this kind of problems.
Key words: Combinatorial optimization; Distribution system; Energy Loss minimization; Genetic Algorithm; Simulating Annealing; Tabu search

Keywords


Combinatorial optimization; Distribution system; Energy Loss minimization; Genetic Algorithm; Simulating Annealing; Tabu search

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References


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DOI: http://dx.doi.org/10.3968%2Fj.est.1923847920110102.001

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