AN APPLICATION OF REACTIVE TABU SEARCH FOR SERVICE RESTORATION IN DISTRIBUTION SYSTEMS AND ITS COMPARSION WITH GENETIC ALGORITHM AND PARALLEL SIMULATED ANNEALING

Abstract

Service restoration in distribution systems can be formulated as a combinatorial optimization problem. It is the problem to determine power sources for each load considering various operational constraints in distribution systems. Up to now, the problem has been dealt with using conventional methods such as branch and bounds method, expert system, neural networks, and fuzzy reasoning.
Recently, modern heuristic methods such as Genetic Algorithms (GA), Simulated Annealing (SA), and Tabu Search (TS) are noticed as efficient methods for solving large combinatorial optimization problems. Moreover, Reactive Tabu Search (RTS) can solve the parameter tuning problem, which is recognized as the essential problem of the TS. Therefore, RTS, GA, and SA can be efficient search method for service restoration in distribution systems.
This paper develops a reactive tabu search (RTS) for service restoration and compares RTS, GA, and PSA (Parallel SA) for the problem. The feasibility of the proposed methods is shown and compared on a typical distribution system model with promising results.