In a distribution network, timely varying load demand makes the operation and control more complex mostly in the high load density zones. The power loss of a distribution network will not be minimum with varying load demand for a fixed configuration of the network. Hence, timely reconfiguration of the network is required. Network reconfiguration is the process of varying the topological structure of feeders by altering the open/closed status of sectionalizing and tie switches. In general, reconfiguration of the network is preferred to relive the overloaded feeders and to minimize real power loss in the distribution system. Since reconfiguration is a complex nonlinear optimization problem.
    Recent studies show that the majority of the researchers have focused on either Distributed Generation (DG) allocation or network reconfiguration for enhancing distribution network performance. However, simultaneous network reconfiguration and DG allocation in the distribution network have been done by very few researchers. The design compares the performance two optimization algorithm that uses a Genetic Algorithm (GA) and Salp Swarm Algorithm (SSA) for solving network reconfiguration and DG allocation problem. The major objective of this design is the minimization of power loss and voltage deviation. To assess the performance three different scenarios considered are:    
1.    Reconfiguration of 33 bus distribution system, considering No of DG:3
2.    Optimal DG and sizing of 33 bus distribution system, considering No of DG:3
3.    Simultaneous reconfiguration and DG installation of 33 bus distribution system, considering No of DG:3
The proposed Genetic Algorithm and Slap Swam Algorithm are tested on 33 distribution systems. The simulated results illustrate the efficiency of the proposed two algorithms.

Reference Paper: Optimal Reconfiguration and Renewable Distributed Generation Allocation in Electric Distribution Systems
Author’s Name: Kola Sampangi Sambaiah and T. Jayabarathi
Source: International Journal of Ambient Energy

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