Data-Driven Techniques for Optimizing the Renewable Energy Systems Operations

Emission-free and sustainable characteristics of renewable energy resources have led to significant deployment of these sources in power systems to mitigate the adverse impacts of fossil fuels on the environment. However, the stochastic nature of renewable energies can jeopardize power systems reliability. Therefore, optimizing renewable energy operation is vital to have a secure and reliable power system. Early and accurate fault detection in renewable energy systems is critical for these systems’ optimal performance. Employing robust fault diagnosis methods is necessary for having an efficient power system, continuous power generation, and reducing maintenance costs. Another crucial factor for optimum operation of renewable energy systems is precise forecasting of renewable energy power to cope with challenges due to the intermittent nature of sustainable energies. Determining the optimal location of renewable energy systems and optimum sizing are other significant factors optimizing their performance. Choosing the best location for installing these systems and proper sizing can maximize produced power. In the big data era, data-driven techniques are suitable tools for this purpose because they do not need mathematical modeling. They only require data measured by installed sensors in renewable energy systems. This feature makes these methods effective for large-scale renewable energy systems, mainly when an accurate system model is not available.