Optimal integration of DGs into radial distribution network in the presence of plug-in electric vehicles to minimize daily active power losses and to improve the voltage profile of the system using bio-inspired optimization algorithms

Satish Kumar Injeti,Vinod Kumar Thunuguntla

doi:10.1186/s41601-019-0149-x

Abstract

PurposeThe increase in plug-in electric vehicles (PEVs) is likely to see a noteworthy impact on the distribution system due to high electric power consumption during charging and uncertainty in charging behavior. To address this problem, the present work mainly focuses on optimal integration of distributed generators (DG) into radial distribution systems in the presence of PEV loads with their charging behavior under daily load pattern including load models by considering the daily (24 h) power loss and voltage improvement of the system as objectives for better system performance.Design/methodology/approachTo achieve the desired outcomes, an efficient weighted factor multi-objective function is modeled. Particle Swarm Optimization (PSO) and Butterfly Optimization (BO) algorithms are selected and implemented to minimize the objectives of the system. A repetitive backward-forward sweep-based load flow has been introduced to calculate the daily power loss and bus voltages of the radial distribution system. The simulations are carried out using MATLAB software.FindingsThe simulation outcomes reveal that the proposed approach definitely improved the system performance in all aspects. Among PSO and BO, BO is comparatively successful in achieving the desired objectives.Originality/valueThe main contribution of this paper is the formulation of the multi-objective function that can address daily active power loss and voltage deviation under 24-h load pattern including grouping of residential, industrial and commercial loads. Introduction of repetitive backward-forward sweep-based load flow and the modeling of PEV load with two different charging scenarios.

Highlights

distributed generators (DG) (Distributed Generator) is characterized as a local power source with a constrained size associated with the distribution network
Active and reactive power demands for a day for each bus is obtained from typical daily load pattern of different types of buses in p.u with respect to peak demand 1 p.u is shown in Fig. 3 [27]
Two charging scenarios peak charging scenario (PCS) and off-peak charging scenario (OPCS) had taken for the inclusion of plug-in electric vehicles (PEVs) load demand on the system

Summary

Introduction

DG (Distributed Generator) is characterized as a local power source with a constrained size associated with the distribution network. Aggregated weight adaptive objective function is utilized to reduce the system’s active power losses and upgrade the voltage profile [9].S K Injeti, presented a Pareto optimizationbased improved differential search algorithm for optimal allocation of DGs in radial distribution networks to minimize total operating cost, bus voltage deviation, and active power losses simultaneously [6] In these papers, the authors considered the optimal allocation of DGs under peak load condition only. Neeraj Kanwar et al proposed a new methodology to provide an integrated solution for the optimal allocation of distributed generators and network reconfiguration considering load patterns of customers for the maximization of annual energy loss reduction [5] In these papers, researchers have concentrated on the optimal placement of DGs under few load levels or linear load variations from 50% to 150% of base load.

Modeling of DGs

Constraints ð14Þ

Particle swarm optimization

Implementation of PSO and BO algorithms to a proposed problem

Results and discussion

Conclusions