Modified Taguchi-Based Approach for Optimal Distributed Generation Mix in Distribution Networks


In this paper, a new two-stage optimization framework is proposed to determine the optimal-mix integration of dispatchable Distributed Generation (DG), in power distribution networks, in order to maximize various techno-economic and social benefits simultaneously. The proposed framework incorporates some of the newly introduced regulatory policies to facilitate low carbon networks. A modified Taguchi Method (TM), in combination with a node priority list, is proposed to solve the problem in a minimum number of experiments. Nevertheless, the standard TM is computationally fast but has some inherent tendencies of local trapping and usually converges to suboptimal solutions. Therefore, two modifications are suggested. A roulette wheel selection criterion is applied on priority list to select the most promising DG nodes and then modified TM determines the optimal DG sizes at these nodes. The proposed approach is implemented on two standard test distribution systems of 33 and 118 buses. To validate the suggested improvements, various algorithm performance parameters such as convergence characteristic, best and worst fitness values, and standard deviation are compared with existing variants of TM, and improved genetic algorithm. The comparison shows that the suggested corrections significantly improve the robustness and global searching ability of TM, even compared to meta-heuristic methods.

Publication DOI:
Divisions: College of Engineering & Physical Sciences
Additional Information: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see Funding: : EP/R001456/1 and EP/S001778/1
Publication ISSN: 2169-3536
Last Modified: 27 Jun 2024 10:09
Date Deposited: 19 Sep 2019 12:25
Full Text Link:
Related URLs: https://ieeexpl ... ocument/8843953 (Publisher URL)
PURE Output Type: Article
Published Date: 2019-10-01
Published Online Date: 2019-09-18
Accepted Date: 2019-09-08
Authors: Meena, Nand K. (ORCID Profile 0000-0002-4092-3921)
Swarnkar, Anil
Yang, Jin (ORCID Profile 0000-0002-1026-8495)
Gupta, Nikhil
Niazi, K. R.

Export / Share Citation


Additional statistics for this record