A Novel Integration Technique for Optimal Location & Sizing of DG Units With Reconfiguration in Radial Distribution Networks Considering Reliability

This paper introduces an advanced approach for optimizing the distribution network reconfiguration (DNR) with the placement and sizing of multiple types of distributed generators (DGs). The method employs the ant colony optimization algorithm (ACOA), which is an innovative adaptive optimization algorithm, while also considering the system’s reliability. The primary objectives of the optimization problem are to minimize active power loss (APL), reduce voltage drop (VD) on buses, enhance system stability (SS), and improve overall reliability by reducing energy not supplied (ENS) to end-users. The optimization process involves determining the optimal location and size of DGs in the radial distribution network (RDN) using the ACOA metaheuristic. The method maintains the radial structure of the system by
selectively opening lines during the DNR process. The proposed technique is evaluated through simulations carried out on the IEEE-33 & -69 bus RDNs under various scenarios. The optimal solution is achieved by combining DG Type-1 with integration of DNR to reduce the APL and amplify the Vp of buses in both RDNs. In this scenario APL is reduced to 87.97 % (IEEE-33) and 92.83 % (IEEE-69), respectively. Similarly, the Vp of the buses significantly improved to 0.9776 p.u. (IEEE-33) and 0.9888 p.u. (IEEE-69), respectively. The results demonstrate the superiority of the presented ACOA-based approach over other techniques, such as fireworks algorithm (FWA) and adaptive shuffled frogs leaping algorithm (ASFLA). Combining the DNR and DGs placement in a simultaneous manner yields the best performance for the distribution network, resulting in lower APL, reduced VD, improved SS, and enhanced reliability. Furthermore, considering reliability in the optimization process significantly reduces ENS for customers and enables meeting their maximum load demand. Overall, the concurrent consideration of DNR and DGs placement using ACOA
proves to be more effective than alternative algorithms.