Neural Network-Based Demand-Side Management in a Stand-Alone Solar PV-Battery Microgrid Using Load-Shifting and Peak-Clipping

Due to failures or even the absence of an electricity grid, microgrid systems are becoming popular solutions for electrifying African rural communities. However, they heavily stressed and complex control due their intermittency demand growth. Demand side management (DSM) serves as option increase level flexibility on by scheduling users’ consumption patterns profiles in response supply. This paper proposes a demand-side strategy based load shifting peak clipping. The proposed approach was modelled MATLAB/Simulink R2021a environment optimized using artificial neural network (ANN) algorithm. Simulations were carried out test model’s efficacy stand-alone PV-battery East Africa. algorithm reduces demand, smoothing profile desired level, improves system’s average ratio (PAR). presence deferrable loads has been considered bring more flexible management. Results promise decreases about 31.2% 7.5% through In addition, promises customers.