Efficient Operation of Distribution System using Optimal DG Placement

Kalyani, R.P.; Damodaran, Suresh K.

doi:10.1109/iccs45141.2019.9065766

Cited by 1 publication

(1 citation statement)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In another research, the fuzzy logic technique is used for placement of DGs to improve the voltage profile and minimize losses in the network [15]. In [16], the multi-objective genetic optimization algorithm (NSGA-II) method is used for optimal placement and sizing of a DG to reduce losses and improve the voltage profile. In [17], in order to achieve the goals of reduction of losses and improvement of the voltage profile in a microgrid, the optimal allocation and sizing of DGs have been considered through using a proposed genetic algorithm (GA).…”

Section: Introductionmentioning

confidence: 99%

Archives of Electrical Engineering

2022

View full text Add to dashboard Cite

One of the most important aims of the sizing and allocation of distributed generators (DGs) in power systems is to achieve the highest feasible efficiency and performance by using the least number of DGs. Considering the use of two DGs in comparison to a single DG significantly increases the degree of freedom in designing the power system. In this paper, the optimal placement and sizing of two DGs in the standard IEEE 33-bus network have been investigated with three objective functions which are the reduction of network losses, the improvement of voltage profiles, and cost reduction. In this way, by using the backward-forward load distribution, the load distribution is performed on the 33-bus network with the power summation method to obtain the total system losses and the average bus voltage. Then, using the iterative search algorithm and considering problem constraints, placement and sizing are done for two DGs to obtain all the possible answers and next, among these answers three answers are extracted as the best answers through three methods of fuzzy logic, the weighted sum, and the shortest distance from the origin. Also, using the multi-objective non-dominated sorting genetic algorithm II (NSGA-II) and setting the algorithm parameters, thirty-six Pareto fronts are obtained and from each Pareto front, with the help of three methods of fuzzy logic, weighted sum, and the shortest distance from the origin, three answers are extracted as the best answers. Finally, the answer which shows the least difference among the responses of the iterative search algorithm is selected as the best answer. The simulation results verify the performance and efficiency of the proposed method.

show abstract

Section: Introductionmentioning

confidence: 99%

Archives of Electrical Engineering

2022

View full text Add to dashboard Cite

show abstract

Efficient Operation of Distribution System using Optimal DG Placement

Cited by 1 publication

References 8 publications

Archives of Electrical Engineering

Archives of Electrical Engineering

Contact Info

Product

Resources

About