This article presents a two-layer optimization scheme for simultaneous optimal allocation of wind turbines (WTs) and battery energy storage systems (BESSs) in power distribution networks. The prime objective of this formulation is to maximize the renewable hosting capacity of the system. For outerlayer, a new objective function is developed by combining multiple objectives such as annual energy loss in feeders, back-feed power, BESSs conversion losses, node voltage deviation, and demand fluctuations caused by renewables subject to various system security and reliability constraints. Furthermore, a modified variant of African buffalo optimization (ABO) introduced to overcome some of the limitations observed in its standard variant. The proposed modifications are first validated and then introduced for simultaneous optimal integration of multiple distributed energy resources in distribution systems. The proposed modified ABO is employed to determine the optimization variables of outer-layer. Whereas, a heuristic is proposed to solve the inner-layer optimization problem aiming to determine the optimal dispatch of BESSs suggested by outer-layer optimization. By considering the high investment and operating cost of BESSs, minimum energy storage capacity has been ensured during the planning stage. To present the efficacy of developed model, it is implemented on a 33-bus, benchmark test distribution system for various test cases. The comparative simulation results show that the proposed optimization model and modified ABO is very promising to improve the performance of active distribution systems.

In this paper, a newly developed moth search optimization (MSO) technique is introduced to solve the complex distributed energy resources (DER) integration problems of distribution systems. In order to overcome some of the limitations observed in the standard variant of MSO, minor corrections are also suggested. On the other hand, a new optimization problem is formulated for optimal deployment of dispatchable distributed generations and shunt capacitors while simultaneously optimizing the tap positions of on-load tap-changing transformers, already deployed in grid substations. The objective of this work is to minimize the cost of annual energy loss and node voltage deviations over multiple load levels. The proposed model is implemented and solved for two benchmark test distribution networks of 33 and 118 buses. The suggested corrections are also validated by comparing the performance of the proposed approach with standard MSO and other available optimization methods. The simulation results show that the developed model optimally utilizes the existing distribution system resources and generates higher deployment benefits at lesser DER penetration as compared to the planning model which ignores these resources.

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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.