Population-based intelligent search in reliability evaluation of generation systems with wind power penetration

Adequacy assessment of power-generating systems provides a mechanism to ensure proper system operations in the face of various uncertainties including equipment failures. The integration of time-dependent sources such as wind turbine generators (WTGs) makes the reliability evaluation process more challenging. Due to the large number of system states involved in system operations, it is normally not feasible to enumerate all possible failure states to calculate the reliability indices. Monte Carlo simulation can be used for this purpose through iterative selection and evaluation of system states. However, due to its dependence on proportionate sampling, its efficiency in locating failure states may be low. The simulation may thus be time-consuming and take a long time to converge in some evaluation scenarios. In this paper, as an alternative option, four representative population-based intelligent search (PIS) procedures including genetic algorithm (GA), particle swarm optimization (PSO), artificial immune system (AIS), and ant colony system (ACS) are adopted to search the meaningful system states through their inherent convergence mechanisms. These most probable failure states contribute most significantly to the adequacy indices including loss of load expectation (LOLE), loss of load frequency (LOLF), and expected energy not supplied (EENS). The proposed solution methodology is also compared with the Monte Carlo simulation through conceptual analyses and numerical simulations. In this way, some qualitative and quantitative comparisons are conducted. A modified IEEE Reliability Test System (IEEE-RTS) is used in this investigation.