Continuous–Discrete Student Psychology-Based Optimization Algorithm for Optimal Planning of Distributed Generators and Distribution Static Compensators in Power Distribution Network

Abstract

In this study, a novel method called the Continuous–Discrete Student Psychology-Based Optimization (CD-SPBO) is introduced. The technique is designed to address the simultaneous allocation of renewable distributed generators (DGs) and distribution static compensators (D-STATCOMs). A novel multi-objective function is formed by combining indices concerning the active power loss, voltage profile, voltage stability and cost consisting of investment, penalty for violation of environmental limits, and energy loss, which are combined through analytic hierarchical process. The CD-SPBO algorithm is used for solving the multi-objective allocation problem for non-dispatchable solar PV-DGs, dispatchable biomass DGs, D-STATCOMs, and their combinations. The superiority of the CD-SPBO algorithm was both numerically and statistically established by comparing its results with other state of the art methods such as Gorilla Troop Optimizer, Artificial Humming Bird Optimizer, and Harris Hawk Optimizer for simultaneous allocation of one, two, and three pairs of DGs and D-STATCOMs on 33-bus and 118-bus test systems. Further, the simulation findings involving seven distinct planning scenarios for the allocation of DGs and D-STATCOMs supported the CD-SPBO algorithm’s effective execution for simultaneous optimal allocation of dispatchable DGs, non-dispatchable DGs, and D-STATCOMS. The suitability of different planning scenarios for improving the overall performance of the distribution system are analyzed in detail. The research insights may prove beneficial for network planners in determining the best combination of devices to meet their needs.