An efficient generator maintenance scheduling model based on unit clustering and linear relaxation

Abstract

The formulation of periodic maintenance schedules for generators is crucial for the efficient and reliable operation of power systems. Traditional generator maintenance scheduling models often have low temporal resolution and ignore significant information. To solve this problem, this paper proposes a mid/ long-term generator maintenance scheduling model with an hourly time resolution. Given the challenges of large integer variable quantities and computational complexity in directly solving mid/ long-term maintenance schedules for power system generation units, an efficient solution method is proposed based on unit clustering and linear relaxation. Initially, a basic maintenance model is established based on the Unit Commitment (UC) model. Subsequently, the basic UC maintenance model is linearly relaxed to obtain an efficient approximate model, termed the Relaxed Clustered Unit Commitment (RCUC) maintenance model, which involves the operation of annual regulating hydropower stations, energy storage devices, pumped storage stations, wind power plants, and photovoltaic (PV) plants. Simulation results from an adapted IEEE RTS-79 system and a provincial scale system case study are provided to demonstrate the effectiveness of the proposed model in coping with mid/long-term maintenance scheduling and annual hydropower allocation problems of large-scale power systems.