  We develop novel statistical models of the harvested energy from renewable
energy sources (such as solar and wind energy) considering
harvest-store-consume (HSC) architecture. We consider three renewable energy
harvesting scenarios, i.e. (i) harvesting from the solar power, (ii) harvesting
from the wind power, and (iii) hybrid solar and wind power. In this context, we
first derive the closed-form expressions for the probability density function
(PDF) and cumulative density function (CDF) of the harvested power from the
solar and wind energy sources. Based on the derived expressions, we calculate
the probability of energy outage at UAVs and signal-to-noise ratio (SNR) outage
at ground cellular users. We derive novel closed-form expressions for the
moment generating function (MGF) of the harvested solar power and wind power.
Then, we apply Gil-Pelaez inversion to evaluate the energy outage at the UAV
and signal-to-noise-ratio (SNR) outage at the ground users. We formulate the
SNR outage minimization problem and obtain closed-form solutions for the
transmit power and flight time of the UAV. In addition, we derive novel
closed-form expressions for the moments of the solar power and wind power and
demonstrate their applications in computing novel performance metrics
considering the stochastic nature of the amount of harvested energy as well as
energy arrival time. These performance metrics include the probability of
charging the UAV battery within the flight time, average UAV battery charging
time, probability of energy outage at UAVs, and the probability of eventual
energy outage (i.e. the probability of energy outage in a finite duration of
time) at UAVs.
