Markovian approach applied to reliability modeling of a wind farm

The exploitation of wind energy as a resource for generating electricity is going to make a leap forward. Large-scale wind farms (WFs) will be engaged with power systems in the near future and this will cause changes in the supply pattern. Wind speed has an intermittent behavior and the output power of a WF is highly variable, consistent with this fact. The Markov process could consider this characteristic of wind speed using the state transition rate probability matrix, an issue that is neglected when a probability distribution function is established to model the wind speed data. The adequacy indices of a power system describe the ability of the generation units to meet the load demand. When a WF is going to be added to a generation system or an existing WF is subjected to expansion study, in addition to the probability of occurrence for the wind speed states, which is considered in the probability distribution function of the wind speed, temporal relations between the wind speed states may affect the adequacy indices. In this paper, the effect of considering transition rates on adequacy indices is studied using a Markovian approach. A WF is modeled with real wind speed data, and then it is added to the generation system of Roy Billinton test system. The transition rate matrix of the WF is modified by definition of the scenarios and the effect of this modification on the loss of load expectation (LOLE) and loss of load frequency (LOLF) is studied. Next, the LOLF index, in comparison with the LOLE, is assessed in expansion strategies and the results enhance the importance of using a Markovian approach and considering the temporal relation of wind speed states in adequacy studies.