Power System Operation and Control: A Data-Driven Approach

Abstract

Renewable energy is becoming more popular around the world, especially wind power and solar photovoltaic (SPV) systems with data interfaces and IoT sensors that generate significant volumes of data. In addition to serving as a monitoring device, the data provided by such devices can be used to improve system reliability and efficiency by providing real-time data. When compared to traditional model-based operation, data-driven based optimal renewable power operation is an emerging method for ensuring trouble-free power system operation. The data-driven method is effective for studying the impact of rapid distributed generation systems integration on utility power system functioning. In data-driven approach, Machine-learning (ML) is an emerging technology for addressing the optimal functioning power system networks. Data-driven operated distributed energy resources (DER) provide real-time management of our dependable power supply through suitable forecasting methods and hence give rise to the smart grid idea. In this proposed work, our objective is to apply a data-driven based strategy to the smart grid in order to ensure the smooth operation and control of both utility and renewable-rich power system.