Renewable Energy Storage Systems with Grid Connected Solar Using Multi-Objective Optimization Technique

For renewable energy sources that are linked to a grid, the dynamic mode has been identified. To ensure dependable mode selection, the criticality of the load is taken into account. To maximize battery life, the Z-Soft Fuzzy Intelligence (ZS-Fuzzy) algorithm decides whether or not to equalize the battery. Battery energy power, net present cost, LPSP, prediction error, cost of energy, and variation rate are some of the key performance metrics used to evaluate the suggested model in comparison to the existing systems. The photovoltaic solar (PV) and wind hybrid RES, as well as the PI controllers in a storage solution utilized to smooth the output changes of those RES. How well the suggested model does in contrast to two widely used AI methods, the particle swarm optimization (PSO) and the genetic algorithm (GA). Thus, a comparative approach is used here. The suggested approach has been shown to be superior to both the PSO and GA algorithms in terms of convergence to the minimum value, resilience under stress, and tracking speed. In order to verify the effectiveness of the proposed techniques, a MATLAB/Simulink model was developed and optimization and simulation were performed on the proposed system. The optimization and simulation results demonstrated that the proposed approach is a viable one for addressing a wide range of technical issues.