Multi-Type Distributed Generation Installation under Load Variability for Loss Minimization in Distribution System

Abstract

The increasing load demand in a power distribution network can lead to power loss increase, requiring the need for increased generation capacity. Therefore, it is necessary to take measures towards this situation, which can be referred to as a compensation process. An optimization process will be required to determine the optimal location and sizing of the compensation devices. The optimal sizing and location of the compensation devices will require an optimization process to ensure efficient, dependable and cost-effective operation. However, certain optimization techniques are prone to getting trapped in local optima and lack of exploration. This paper presents a new optimization technique to address multi-type distributed generation installation under load variability for loss minimization in distribution system. A strategy has been proposed to integrate the implementation of integrated strategy confining the standalone evolutionary programming (EP) and the proposed integrated immune moth flame evolutionary programming (IIMFEP) in one integrated algorithm. A common initialization process was adopted in both techniques, making it robust and intelligent in addressing the loss minimization issue in distribution system. Installations of DG Type-I and DG Type-III for loss minimization in distribution system under light load, full load and heavy load implemented in IEEE 69-Bus Radial Distribution System was considered in this study. The proposed IIMFEP managed to achieve 67.39% loss reduction for 3 units of DG Type-I installation and 97.62% loss reduction for 3 units of DG Type-III installation under light load condition. On the other hand, it achieves 69.14% loss reduction for 3 units of DG Type-I installation and 97.78% loss reduction for 3 units DG Type-III installation under full load condition. For the case of heavy load condition, the proposed IIMFEP managed to achieve 72.14% loss reduction for 3 units of DG Type-I installation and 98.03% loss reduction for 3 units DG Type-III installation. Obviously, the installation of 3 units of DG Type-III is superior to DG Type-I installation to achieve comparatively higher loss reduction. Comparative studies over the independent evolutionary programming (EP) and standalone moth flame optimization (MFO) technique revealed the merit of the proposed IIMFEP over EP and MFO.