Abstract:
An accurate estimation of photovoltaic (PV) power production is crucial for organizing and regulating solar
PV power plants. The suitable prediction is often affected by the variable nature of solar resources, system
location and some internal/external disturbances, such as system effectiveness, climatic factors, etc. This paper
develops a novel strategy for applying a predictive control technique to PV power forecasting applications in
a smart grid environment. The strategy develops the model predictive control (MPC) under demand response
(DR) and some data-driven methods. It has been found that it is challenging to model an MPC for solar
power forecasting regardless of its robustness and ability to handle constraints and disturbance. Thus, an
optimal quadratic performance index-based MPC scheme is formulated to model a forecasting method for a
PV power prediction. This strategy is then compared with some machine learning approaches. The developed
strategies solve the problem of accurately estimating the direct current (DC) power yielded from the PV plant
in a real-world implementation. The study also considers external disturbances to evaluate the significance of
the developed methods for a suitable forecast. Therefore, this study optimally demonstrates that an accurate
solar PV DC power prediction can relatively be estimated with an appropriate strategy, such as MPC and
MLs, considering the system disturbances. This study also offers promising results for intelligent and real-time
energy resource estimation that assist in developing the solar power sector.
