A new IGDT-based robust model for day-ahead scheduling of smart power system integrated with compressed air energy storage and dynamic rating of transformers and lines

Abstract

Growing concerns about climate change have driven power system operators worldwide to utilize wind energy as clean and affordable energy. High penetration of wind energy along with high power consumption of consumers can cause congestion in the transmission network which in turn cause wind spillage, load shedding and high operation cost. Motivated by this challenge, compressed air energy storage (CAES), dynamic transformer rating (DTR) and dynamic line rating (DLR) are three smart technologies that are considered as ways to increase the flexibility of the electrical network and decrease wind spillage and load shedding. With DTR and DLR technologies, the real capacity of transformers and lines is determined which is dependent on weather parameters. Hence, this study proposes a day-ahead scheduling based on the AC power flow model for smart power system taking CAES, DLR and DTR into account. The aim of this model is to minimize load shedding, wind spillage, total cost and emissions. Uncertainties of wind energy (which has a great impact on day-ahead scheduling and capacity of lines with DLR) and electrical load, are handled through an improved form of the information gap decision theory (IGDT), hereafter called weighted IGDT (WIGDT)-based robust model. The effectiveness of the introduced method is evaluated by testing on IEEE 24-bus system. According to obtained results, simultaneous used of CAES, DTR and DLR can reduce wind spillage, load shedding, emission and operation cost and also improve the voltage profile.