MATEC Web of Conferences
Volume 150, 2018Malaysia Technical Universities Conference on Engineering and Technology (MUCET 2017)
|Number of page(s)||6|
|Section||Electrical & Electronic|
|Published online||23 February 2018|
Effect of Load Model Using Ranking Identification Technique for Multi Type DG Incorporating Embedded Meta EP-Firefly Algorithm
Center of Excellence for Renewable Energy (CERE), UniMAP, Universiti Malaysia Perlis, 02600, Arau, Perlis, Malaysia.
2 Center for Electrical Power Engineering Studies, Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Malaysia
3 School of Electrical System Engineering, Universiti Malaysia Perlis, 02600, Arau, Perlis, Malaysia
* Corresponding author: email@example.com
This paper presents the effect of load model prior to the distributed generation (DG) planning in distribution system. In achieving optimal allocation and placement of DG, a ranking identification technique was proposed in order to study the DG planning using pre-developed Embedded Meta Evolutionary Programming–Firefly Algorithm. The aim of this study is to analyze the effect of different type of DG in order to reduce the total losses considering load factor. To realize the effectiveness of the proposed technique, the IEEE 33 bus test systems was utilized as the test specimen. In this study, the proposed techniques were used to determine the DG sizing and the suitable location for DG planning. The results produced are utilized for the optimization process of DG for the benefit of power system operators and planners in the utility. The power system planner can choose the suitable size and location from the result obtained in this study with the appropriate company’s budget. The modeling of voltage dependent loads has been presented and the results show the voltage dependent load models have a significant effect on total losses of a distribution system for different DG type.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
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