Multi‐objective Taguchi approach for optimal DG integration in distribution systems

A novel multiobjective chaotic symbiotic organisms search algorithm to solve optimalDGallocation problem in radial distribution system

“…The simulation results yielded by MOCSOS are compared with other existing methods such as GA, 19 PSO, 19 GA/PSO, 19 TLBO, 25 QOTLBO, 25 Taguchi method (TM), 31 MOTA, 31 NSGA-II, and MOPSO and are listed in Table 3. The simulation results yielded by MOCSOS are compared with other existing methods such as GA, 19 PSO, 19 GA/PSO, 19 TLBO, 25 QOTLBO, 25 Taguchi method (TM), 31 MOTA, 31 NSGA-II, and MOPSO and are listed in Table 3.…”

“…Three UPF DGs are considered to be integrated into the RDS in this case. The simulation results yielded by MOCSOS are compared with other existing methods such as GA, 19 PSO, 19 GA/PSO, 19 TLBO, 25 QOTLBO, 25 Taguchi method (TM), 31 MOTA, 31 NSGA-II, and MOPSO and are listed in Table 3. The simulation results suggest that the proposed MOCSOS gives minimum power loss and maximum VSI min among all the methods.…”

“…Some of the Paretobased MOO algorithms that were utilized in solving the DG allocation problem include improved multiobjective HSA, 27 multiobjective shuffled bat algorithm, 28 improved differential search algorithm, 29 improved nondominated sorting GA-II (NSGA-II), 30 multiobjective Taguchi approach (MOTA), 31 Pareto frontier-based DE, 32 and nondominated sorting TLBO. Hence, the problem of allocating DGs in RDS becomes a complex multiobjective optimization (MOO) problem since it is quite hard to simultaneously optimize multiple conflicting objectives.…”

“…In view of the considered DG allocation problem, the Pareto approach is more suitable than the conventional weighted sum approach because, in Pareto approach, the trade-off nondominated solutions are obtained in just a single run whereas the weighted sum approach requires multiple runs by varying the weights (usually, in steps) to find the trade-off nondominated solutions. Some of the Paretobased MOO algorithms that were utilized in solving the DG allocation problem include improved multiobjective HSA, 27 multiobjective shuffled bat algorithm, 28 improved differential search algorithm, 29 improved nondominated sorting GA-II (NSGA-II), 30 multiobjective Taguchi approach (MOTA), 31 Pareto frontier-based DE, 32 and nondominated sorting TLBO. 33 It may be observed from literature survey that only a few number of studies are available that have utilized the Pareto-based MOO methods 17,[27][28][29][30][31][32][33] as compared with the conventional weighted sum method.…”

“…Some of the Paretobased MOO algorithms that were utilized in solving the DG allocation problem include improved multiobjective HSA, 27 multiobjective shuffled bat algorithm, 28 improved differential search algorithm, 29 improved nondominated sorting GA-II (NSGA-II), 30 multiobjective Taguchi approach (MOTA), 31 Pareto frontier-based DE, 32 and nondominated sorting TLBO. 33 It may be observed from literature survey that only a few number of studies are available that have utilized the Pareto-based MOO methods 17,[27][28][29][30][31][32][33] as compared with the conventional weighted sum method. [9][10][11][12][13][14][15][16][18][19][20][21][22][23][24][25][26] Moreover, most of the studies that apply Pareto-based methods have not taken reactive power loss in consideration.…”