Affiliations: [a] Department of Control Engineering, Jilin Institute of Chemical Technology, Jilin, China
| [b] Calcium Carbide Factory of Jilin Petrochemical Company, Jilin, China
| [c] Acrylonitrile Plant, Jilin Petrochemical Company, Jilin, China
Correspondence:
[*]
Corresponding author. Yongguo Wang, Department of Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, Jilin, China. E-mail: a448535194@163.com.
Abstract: The high power generation growth by photovoltaic systems needs to forecast the power generation profile during a day. It is also required to evolve the high-efficient and optimal on-grid/off-grid photovoltaic power generation units. Furthermore, some advantages can be achieved by integrating photovoltaic systems with storage devices such as battery energy storage systems. Thus, optimizing the hybrid systems comprising photovoltaic and battery energy storage systems is needed to evaluate the best capacity. In the present work, a novel control and sizing scheme is proposed for the battery energy storage system in a photovoltaic power generation plant in one-hour ahead and one-day ahead during the dispatching phase. Then, the proposed prediction strategy is recommended for solar irradiation and power utilization. The control approach comprises a predictive control method concerning a Radial Basis Function network optimized by Levenberg-Marquardt back-propagation learning algorithm. Using the RBF network for simulation leads to a WAPE% =1.68 %.
Keywords: Photovoltaic systems, battery energy storage system, control method, prediction method, RBF neural network, experimental dataset
