A novel approach for the reconfiguration of distribution systems considering the voltage stability margin

摘要

In recent years, the problem of optimum reconfiguration indistribution systems (DSs) has been a task that must be solved in anoptimal manner. This paper presents a new approach for the optimalreconfiguration of DSs based on a hierarchical 2-stage optimizationproblem to improve the power system voltage stability margin andreduce losses incorporating the constraints. The mentioned problemhas been modeled as a nonlinear and multiobjective optimizationproblem. It uses the ability of the developed harmony searchalgorithm (HSA) as the first stage of the proposed optimizationproblem to reach the best network configuration. This reconfigurationalgorithm starts with a radial topology by a theoretical approach thatis based on the graph concept and matroid theory. These concepts areused in order to propose new intelligent HSAs to form a new harmonyvector that is well dedicated to the DS reconfiguration problem.Thus, all of the resulting individuals after forming a new harmonyvector are claimed to be feasible configurations. Moreover, thepresented approach is valid and avoids tedious mesh checks for thetopology constraint validation.In the second stage of the proposed approach, the voltage stabilityindex is calculated to evaluate the static voltage stability securitymargin for each reconfiguration pattern. Hence, a toolbox has beendeveloped to recognize the loadability limit of DSs based on theLagrangian optimization method.Finally, the proposed method establishes a tradeoff between thesecurity index and power losses to reach a coordinated reconfigurationpattern. To demonstrate the validity of the proposed method, thesimulations are carried out on 33- and 69-bus IEEE DSs. The proposedmethod is finally compared to some previous techniques used by otherauthors. The results show a good enhancement in the security marginand smaller power losses with considerably less computation effort.To validate the proposed method, the results that were obtained fromthe HSA are compared with the particle swarm optimization algorithm toascertain its effectiveness.