Markov based estimation of energy storage requirements accounting for seasonal variations

The use of energy storage in conjunction with renewable energy sources such as wind and solar is receiving more attention to help mitigate the effects of the intermittent nature of these sources. One wishes to maximize the probability that there will be enough energy available to meet the residential load demand while minimizing the cost of both the renewable energy sources as well as the energy storage device(s). The use of a first order Markov Chain has been previously investigated as a means of estimating the amount of energy storage required at a particular off-grid residence with wind energy supply. In this paper, the resultant state transition matrix is biased to account for seasonal variations in the wind resource. Compared to the data generated using the unbiased state transition matrix (STM), the biased STM yields a better autocorrelation coefficient and generally results in a larger number of hours of insufficient supply to the load. This is despite the fact that the seasonal variation in the wind resource overall yields a higher production of energy when compared to no seasonal variation.