Intelligent Renewable Energy Systems

The proposed method has been coded in MATLAB software and compared with four different optimization methods, such as PSO, TLBO, CS and SOS, available in the literature. For the comparison purpose, the CEC-2005 benchmark functions have been considered, and the comparison has been made from the point of view of best FF, worst FF, mean, standard deviation and rank. For almost all benchmark functions, the proposed SPBO method has been provided the better results. Therefore, it can be concluded that the proposed method is superior than the other four methods considered in this book chapter.

The multi-objective RDGs and capacitors sizing and placement study has been carried out considering the variable load demand of a day. After the placement of biomass DG, solar PV, and shunt capacitors to the 33-bus and 69-bus distribution network using mixed discrete SPBO algorithm, it may be noticed that the active power loss reduction for all the load demands of the day and for both the considered distribution networks is quite significant. Significant reduction of VDI may also be noticed after the placement of DGs to both the distribution networks and for all the different load hours. The effective annual installation cost is also found to be less for both the distribution networks.

From the study, it may be concluded that the proper placement of biomass DG, solar PV, and shunt capacitors may lead to a significant reduction in active power loss, VDI, as well as effective annual installation cost, for both the considered 33-bus and 69-bus distribution networks. It is also worth mentioning that the mixed discrete version of SPBO is capable to optimize the sizes and locations of the DGs simultaneously in order to achieve the desired objective. It can also be concluded that the mixed discrete SPBO is very much capable to optimize both continuous and discrete variables simultaneously.

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