Day-ahead Generation Scheduling with Information Gap Decision Models

Abstract

Recently, because of global warming countermeasures, interest in renewable energy has increased, particularly photovoltaic power generation (PV). On the other hand, when PV, whose output varies greatly depending on weather conditions, is introduced into the power systems in a large amount, several issues and problems are raised. It becomes difficult to adjust the voltage of the distribution systems and secure adjustment power to absorb PV output fluctuations, which affects the system frequency. In contrast, the authors have proposed the concept of robust power system security, which can apply the conventional N-1 reliability to uncertain environments. Furthermore, we formulated and developed a robust dynamic feasible (RDF) region and proposed a computation method for the evaluation index that indicates the size of the RDF region. However, executing this index calculation in a large-scale system can take a huge amount of time. Moreover, the calculation is expected to become complicated depending on the future development of a robust power system security index. In this paper, we formulate a novel method for assessing uncertainty using the Information Gap Decision Theory (IGDT). Based on the concept of the robust confidence region developed in our previous research, the authors have proposed an uncertainty model and are studying the application of IGDT theory for robust confidence evaluation using the proposed model. The simulation results confirm and validate the effectiveness of this proposed model.