Hybrid Elephant Herding and Particle Swarm Optimizations for Optimal DG Integration in Distribution Networks

Abstract

In this article, the amalgamation of two well-established meta-heuristic optimization methods is presented to solve the multi-objective distributed generation (DG) allocation problem of distribution systems. To overcome some of the shortcomings of newly developed elephant herding optimization (EHO), an improvement is suggested and then, a prominent feature of particle swarm optimization is introduced to the modified version of EHO. The suggested modifications are validated by solving a single objective DG integration problem where various performance parameters of the proposed hybrid method are compared with their individual standard variants. After validation, the proposed technique is exploited to solve a multi-objective DG allocation problem of distribution systems, aiming to minimize power loss and node voltage deviation while simultaneously maximizing the voltage stability index of three benchmark distribution systems namely, 33-bus, 69-bus and 118-bus. The obtained simulation results are further compared with that of the same available in the existing literature. This comparison reveals that the proposed hybrid approach is promising to solve the multi-objective DG integration problem of distribution systems as compared to many existing methods. Nomenclature NTotal number of buses in the system.x jk Reactance of branch between node j & k (Ω).L max , L min Max and min limits of clan. S M ax DGMaximum DG capacity allowed on a bus (kVA).Velocity of elephant i in iteration k. S DGjSuggested capacity of DG at bus j (kVA).Position of particle i in iteration k.Y jk Element of Y-bus matrix (Ω −1 ).c 1 , c 2 The acceleration coefficients.

Performance Enhancement of Renewable ADN by Strategical Placement of Dispatchable DER

“…To exemplify the potential of MABO in the broach model for optimal integration of various DERs, the model is executed on a 33-bus radial test network [22]. Further, the factor for wind speed and solar radiation for the study case is referred from [5], which includes the market available sizes of WTs. For the studied case, it is considered that the geographical scenario for all the nodes is the same.…”

“…DER is a source of power generation in the vicinity of load and is deployed in the PDN [4]. The advanced DER technologies have enhanced the PDN in terms of reliability, security and robustness [5][6][7][8][9]. Further, the alleviation in hydrocarbon reserves and increase in greenhouse gases have attracted the attention of the ascendancy institute towards the renewable active distribution network (ADN).…”

“…The literature has various AI techniques to solve this problem like Genetic Algorithm (GA) [15], Elephant Herd Optimization (EHO) [5], Partial Swarm Optimization (PSO) [15], Moth Fly optimization (MSO) [18], African Buffalo Optimization [19], etc. [3].…”

Network Reconfiguration and Distributed Generation Placement for Multi-Goal Function Based on Improved Moth Swarm Algorithm

“…In [7], the NR problem is solved to satisfy the goals of power loss reduction and reliability enhancing relied on epsilon method. Meanwhile, there are also many studies related to the DGP problem on the DS such as [8][9][10][11][12]. In [8], the DGP problem is considered to reduce power loss, voltage deviation and enhance the voltage stability of the DS using hybrid algorithm between elephant herding optimization (EHO) and PSO (EHOPSO).…”

“…Meanwhile, there are also many studies related to the DGP problem on the DS such as [8][9][10][11][12]. In [8], the DGP problem is considered to reduce power loss, voltage deviation and enhance the voltage stability of the DS using hybrid algorithm between elephant herding optimization (EHO) and PSO (EHOPSO). e noted point of this work is that the authors have shown the advantages of EHOPSO compared to EHO as well as PSO.…”