  L\'evy-driven Ornstein-Uhlenbeck (OU) processes represent an intriguing class
of stochastic processes that have garnered interest in the energy sector for
their ability to capture typical features of market dynamics. However, in the
current state-of-the-art, Monte Carlo simulations of these processes are not
straightforward for two main reasons: i) algorithms are available only for some
particular processes within this class; ii) they are often computationally
expensive. In this paper, we introduce a new simulation technique designed to
address both challenges. It relies on the numerical inversion of the
characteristic function, offering a general methodology applicable to all
L\'evy-driven OU processes. Moreover, leveraging FFT, the proposed methodology
ensures fast and accurate simulations, providing a solid basis for the
widespread adoption of these processes in the energy sector. Lastly, the
algorithm allows an optimal control of the numerical error. We apply the
technique to the pricing of energy derivatives, comparing the results with
existing benchmarks. Our findings indicate that the proposed methodology is at
least one order of magnitude faster than existing algorithms, all while
maintaining an equivalent level of accuracy.
