DEEP LEARNING BASED INTELLIGENT METHODOLOGY FOR PHOTOVOLTAIC POWER QUALITY CONTROL WITH ENERGY STORAGE

Power imbalances instigated from Photovoltaics (PV) avert the incorporation of large-scale PV power into the grid and makes it a challenging resource as the intermittencies caused result in frequency variations and voltage interruptions which eventually engenders electricity blackouts. Furthermore, it compromises the associated battery's operation as it results in irregular battery charging/discharging. The fluctuating power also leads to the requirement of larger and impractical storage systems. Therefore, the power quality from the PV needs to be controlled to allow a stable output power supply to the load and also to enhance the operation of the connected Battery Energy Storage System (BESS). This paper presents a novel neural network (NN) model predictive control (MPC) method for PV power control and firming with BESS. Instead of creating a mathematical model of the plant for the prediction optimization step in an MPC, the proposed controller generates a NN plant model. Due to its innate characteristic, a NN model better captures the dynamics of the plant, delivers highly accurate predictions, and resolves the mathematical model complexity issues that occur in highly complicated plants. The proposed controller effectively firms the varying solar power and stabilizes the battery state of charge.