Neural Network-Based Control for Hybrid PV and Ternary Pumped-Storage Hydro Plants

The growth in renewable energy integration over the past few years, primarily fueled by the drop in capital cost, has revealed the requirement for more sustainable methods of integration. This paper presents a collocated hybrid plant consisting of solar photovoltaic (PV) and Ternary pumped-storage hydro (TPSH) and designs controls that integrate the PV plant such that the behavior and the controllability of the hybrid plant are similar to those of a conventional plant within operational constraints. The PV array control and hybrid plant control implement a neural-network-based framework to coordinate the response, de-loading, and curtailment of multiple arrays with the response of the TPSH. With the help of the designed controls, a symbiotic relationship is developed between the two energy resources, where the PV compensates for the TPSH nonlinearities and provides required speed of response, while the TPSH firms the PV system and allows the PV to be integrated using its existing infrastructure. Simulations demonstrate that the designed controls enable the PV system to track references, while the TPSH's firming and shifting transforms the PV system into a base load plant for most of the day and extends its hours of operation.

Automated summary

This paper introduces a hybrid plant consisting of solar photovoltaic (PV) and Ternary pumped-storage hydro (TPSH) that utilizes neural-network-based controls to coordinate the response of multiple arrays with the TPSH. The designed controls enable the PV system to track references, while the TPSH's firming and shifting transforms the PV system into a base load plant for most of the day. Through symbiotic relationship, the PV compensates for TPSH nonlinearities and provides required speed of response.