Abstract
Power loss is one aspect of an electric power system performance indicator. Loss of power can have an impact on poor voltage performance at the receiving end. DG integration in the network has become one of the more powerful methods. To get the maximum benefit from synchronizing the system with DG, it is necessary to ascertain the size, location, and type of DG. This study aims to determine the capacity and location of DG connections for DG type I and type II. To address the aim of this paper, a metaheuristic solution based on a firefly algorithm is used. FA can cover up the lack of metaheuristic algorithms that require a long computational time. To ensure that the load bus location solution is selected as the best DG connection location, the input of the load bus candidate has been filtered based on stability sensitivity. The proposed method is tested on IEEE 30 buses. The optimization results show a decrease in power loss and an increase in bus voltage, which affects an increase in system stability by integrating three DG units. FA validation of the evolution-based algorithm shows a significant reduction in computational time.
Highlights
The use of distributed generators (DG) on a scale of capacity and various types of innovation is becoming increasingly prevalent used in electric power systems
This paper presents research results on the placement of DG-1 and DG-2 in varying units using firefly algorithm (FA) approximations
The FA optimization method succeeded in solving locating the location of the generator deployed faster than searching using an evolution-based algorithm
Summary
The use of distributed generators (DG) on a scale of capacity and various types of innovation is becoming increasingly prevalent used in electric power systems. The benefits obtained from DG integration have been mentioned in previous studies, including reducing power losses, increasing reliability, improving stability, and improving the voltage profile [1]-[4]. In line with research [7], reference [8] has proposed a method for determining the location and size of DG type combinations using the efficient analytical method (EA) integrated power flow study. There is a point where the trend reverses to increase again This indicates the need for proper techniques to obtain the optimal level of DG capacity. This paper exists to address the shortcomings of using metaheuristic algorithms that require a long time in the execution process [9], [10] Due to this reason, the FA election to resolve the DG case in this study.
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