Due to increase, the electricity demands and the cost of energy generation to cover the living requirement of modern societies, micro-grid (MG) has been established and played an important role to solve the problems of intermittent. This study focused on implementing the analytical algorithm for calculating the difference between generated units and consumed units under the control of the State of Charge (SOC) value and applied the Particle Swarm Optimization (ANALYTIC-PSO) algorithm to optimize the value resulting from the difference between generated units and consumed units. The simulation was performed using the python environment and the PSO algorithm converges to final state approximately after the 10th iterations. The results showed that the proposed approach is efficient compared to Grey Wolf Optimization (GWO) algorithm.
The state of charge (SOC) estimation plays important role in the battery energy storage system (BESS).Nowadays many semiconductor companies are paying more and more attention and investment to support many researchers to implement the state of charge for the batteries storage. the key to optimize the batteries storage is determine SOC value based on accuracy methods. a number of brief methods for SOC determination have been studied and compared with traditional methods the adaptive methods shown precise result because didn't consider the dynamic effect of the batteries. In this paper, we use combination methods to estimate the SOC for lead-acid battery storage under two charge techniques namely Maximum Power Point Tracking -Plus Width Module (MPPT-PWM) when considering the effect of voltage drops on the estimation of SOC. The model uses the coulomb counting as an algorithm to determine the SOC and set it as a target in the backpropagation function in artificial neural network in MATLAB program (R2016a 64-bit (win64)). The simulation results show that the model is very precise to estimate the SOC in realistic operation.
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