Aquila Optimizer Based Optimal Allocation of Soft Open Points for MultiObjective Operation in Electric Vehicles Integrated Active Distribution Networks

Abstract

The appropriate position and sizing of soft open points (SOPs) for reducing the detrimental impact of electric vehicle (EV) load penetration and renewable energy (RE) variation on active distribution networks (ADNs) are provided in this study. Soft open points (SOPs) have been used to create a multi-objective framework that considers loss minimization and voltage profile enhancement. The non-linear multi-variable complicated SOP allocation problem is solved for the first time using a modern meta-heuristic Aquila optimizer (AO). The modified IEEE 33-bus benchmark and IEEE 69-bus ADNs are used in the simulations. Before SOPs, the average real power loss in IEEE 33-bus AND was 370.329 kW, but after SOPs, it was reduced to 259.356 kW (i.e., 29.96 percent reduction). Similarly, effective SOPs integration in the IEEE 69-bus resulted in a loss reduction of 81.07 percent. AO's computational efficiency is also compared to that of multiobjective particle swarm optimization (MOPSO), particle swarm optimization (PSO), and cuckoo search algorithm (CSA). The AO has produced better results in terms of lower losses, improved voltage profile despite variations in EV load penetration, and RE and load volatility in ADNs, according to the results.

Enhanced Jellyfish Search Optimizer for Collaborative Team Formation in Social Network

“…The CJSESOS method was programmed in MATLAB. The results were compared against the following algorithms (JSO [4], CJSO [5], PSO [9], GA [10], GWO [11], HBO [13], AO [14] and POA [15]) using a set benchmark function and different datasets. The general efficiency of our suggested method and its ability to converge to optimality are investigated using different experiments.…”

“…CJSESOS tested in solving the TF problem with varying number skills in different dataset. Furthermore, the proposed algorithms were compared to well-known optimization algorithms such as the jellyfish search optimizer (JSO), particle swarm optimization (PSO) [9], genetic algorithm (GA) [10], grey wolf optimization (GWO) [11,12], heap-based optimizer (HBO) [13], aquila optimizer (AO) [14], pelican optimization algorithm (POA) [15] and chaotic local jellyfish CJSO. using a set of mathematical benchmark functions to evaluate the CJSESOS algorithm and solve the team formation problem (TF) using a set of real datasets.…”

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“…Fig. 11 shows the convergence curve with the proposed and existing optimization algorithms such as Aquila optimization algorithm (AOA) [34], guided pelican algorithm (GPA) [35], stochastic komodo algorithm (SKA) [36], modified moth flame optimization algorithm (NMOA) [37], artificial rabbits optimization algorithm (AROA) [38], northern goshawk optimization (NGO) [39], enhanced whale optimization algorithm (EWOA) [40], fixed step average and subtraction (FSAS) [41] and puzzle optimization algorithm (POA) [42]. Fig.…”

A Secure and Energy Efficient Cluster Based Routing Using Energy and Trust Aware - Multi Objective African Vultures Optimization for MANET

“…The recent optimization techniques such as Aquila Optimizer (AO) [22], Guided pelican algorithm (GPA) [23] and Puzzle Optimization Algorithm (POA) [24]. AO is based on optimal allocation of soft open points for multi-objective operation in Electric Vehicles (EV) integrated active distribution networks.…”

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