Application of ANFASO for optimal power flow management of MG‐connected system with energy storage

Abstract

The power flow management scheme for a microgrid (MG)-connected system utilizing a hybrid technique is suggested in this dissertation. An MG-connected system includes photovoltaic, wind turbine, micro turbine and battery storage.Due to the use of this resource, power production is intermittent and unpredictable, as well as unstable, which causes fluctuation of power in hybrid renewable energy system. To ensure the fluctuation of power, an optimal hybrid technique is suggested. The suggested hybrid technique is joint execution on ANFIS and ASOA. ANFIS stands for adaptive neuro fuzzy interference system, and ASOA stands for advanced salp swarm optimization algorithm, thus it is commonly known as the ANFASO method. In the established method, ANFIS is applied to continuously track the MG-connected system's required load. ASOA optimizes the perfect combination of MG in terms of predicted required load. The suggested methodology is used for optimal cost and to increase renewable energy sources (RESs). Constraints are RES accessibility, power demand and the storage elements. Using the MATLAB/Simulink work site, the ANFASO approach is executed and implemented compared with existing methods. The suggested method is compared with genetic algorithm (GA), BFA and the artificial bee colony algorithm (ABC), and the observed elapsed time of ABC is 37.11 seconds, BFA is 36.96 seconds and GA is 38.08 seconds. The elapsed time of the proposed technique was found to be lower (36.47 seconds) compared to existing techniques. Significant improvements regarding utilization of RES and total generation cost accuracy are attainable by utilizing the proposed approach. K E Y W O R D Sbattery storage, constraints, micro turbine, photovoltaic, power demand, power generations, wind turbine

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“…Subsequently, load prediction is determined effectively by the sandpiper optimization algorithm (SOA) and advanced Salp swarm optimization algorithm (ASOA). Moreover, GBDT‐SOA 14 and ANFASO 15 approaches through a hybrid method have focused on merely optimizing the production overheads by completely neglecting the user preferences and the corresponding system constraints. In a recent scenario, the CEM framework has adopted a robust two‐stage decision approach 16 for solving the unit commitment and power flow challenges in a standalone microgrid that includes renewable sources, storage elements, and interruptible demand.…”

Optimal power flow with stochastic solar power using barnacles mating optimizer

“…Instead of single algorithm, there were various hybrid algorithms that have been proposed to solve either single or multi‐objectives OPF such as Moth Swarm Algorithm with GSA (MSA‐GSA) as recommended in Reference 26, Fuzzy Based Hybrid Particle Swarm Optimization as proposed in Reference 27 and 28, Hybrid Particle Swarm and SalpSwarm Optimization as proposed in Reference 29, Hybrid of (PSOGSA) as proposed in Reference 30, Hybrid Modified Imperialist Competitive Algorithm and Sequential Quadratic Programming, (HMICA‐SQP), 31 Hybrid Fuzzy Particle Optimisation and Nelder‐Mead (NM) algorithm (HFPSO‐NM), 28 Hybrid of Adaptive Neuro Fuzzy Interference System (ANFIS) with Advanced SalpSwarm Optimization Algorithm called (ANFASO) 32 and Hybrid SalpSwarm Optimization Algorithm with Particle Swarm Optimization (PSO‐SSO) 29 . The analysis of the metaheuristic approaches into OPF problem has been discussed in Reference 33 where eight different optimization algorithms, that is, MFO, Grey Wolf Optimizer (GWO), Dragonfly Algorithm (DA), Sine‐Cosine Algorithm (SCA), Antlion Optimizer (ALO), Multi‐Verse Optimizer (MVO), Grasshopper Algorithm (GOA) and Ion Motion Algorithm (IMO) have been studied and analysed.…”

Improved off‐grid wind/photovoltaic/hybrid energy storage system based on new framework of Moth‐Flame optimization algorithm

“…Considering the optimization of the power flow of a connected micro-grid with energy storage, a new study of the Adaptive Neuro Fuzzy Interference System (ANFIS), and an Advanced Salp Swarm Optimization Algorithm (ASOA) were proposed. 31 In Reference 32, the authors introduced a dynamic optimization approach of solar-wind system with energy storage system. The main goal of this work is to minimize the Annual Total Cost (ATC) of two architectures of the system, when it contains a battery and when it uses a fuel cell.…”