Neural network predictive control for smoothing of solar power fluctuations with battery energy storage

Abstract Power fluctuations caused by Photovoltaics (PV) prevent the penetration of large-scale PV power into the grid as it causes multiple instabilities such as frequency deviations, voltage fluctuations, and decreased output power quality. Additionally, it severely compromises the associated battery’s performance and reduces its operational life span that can lead to the requirement of larger batteries thereby increasing the overall system cost. In this paper, a novel neural network model predictive control (MPC) approach for photovoltaic power smoothing with battery energy storage system is proposed. As opposed to the conventionally used MPC that utilizes the mathematical model of the plant for its predictive optimization, the proposed controller generates a Neural Network (NN) model of the plant. In comparison to the mathematical model, a NN better encapsulates the dynamics of the plant and can also provide higher accuracy predictions. Furthermore, the precision of the NN plant model is further increased as the collected input–output plant data increases. The NN model also solves the issues related to mathematical complexity of the MPC model that arises due to the increasing complications in the plant. Whereas the inherent characteristics of a NN allows it to model highly complex plants with a relatively simpler approach. The proposed controller is capable of firming the solar power by employing the inputs from our NN plant model and also optimizes the battery state of charge under a variety of practical constraints which consequently promotes enhanced battery life. Furthermore, this study also proposes a novel NN architecture for accurate PV power forecasting. In comparison to the popularly used fuzzy logic controller, the proposed controller manages to significantly reduce the battery charging levels and state of charge.