Neural Network-Based Control for Hybrid PV and Adjustable Speed Pumped-Storage Hydropower Plant

Abstract

The penetration of renewable energy sources into the grid has been on a constant increase in recent years. These sources are characterized by their intermittent nature which poses challenges such as reliability and resiliency to the electric grid. To help mitigate these challenges, large-scale energy storage devices and appropriate control strategies are required. In this paper, a parallel hybrid plant comprising a solar photovoltaic (PV) unit and an adjustable speed pumped-storage hydropower (ASPSH) unit implements neural network (NN) estimators to estimate the maximum power point and the terminal voltage of the PV module. These estimates were utilized by the designed hybrid plant and PV array control to successfully synchronize the PV and ASPSH responses to achieve a synergetic relationship between them.