In this chapter we investigate the problem of energy optimization of a Multisource Elevator. We propose a large framework including a strategic optimizer and a local controller. The roles and influences of these systems depend on the time horizon fixed. The first one proposes the strategy for the next hour, where the second tries to re-adapt the long term strategy to the real-time constraints. We prove the adaptability of this approach to real-time conditions (e.g.: disruptions of the forecasted elevator travels). In such a context, a linear programming formulation has been developed, coupled with a low level rule-based controller. We propose three main objective functions for the sourcing problem, that can be studied together or one after each other. Then, we tackle a specialized sourcing problem: the Multisource Elevator problem in which the three objectives have to be achieved simultaneously. We show in the experiments, based on real data sets, that a compromise between reducing consumption peaks and minimizing the energy bill has to be reached. Using storage units to reduce consumption peaks does not induce additional costs, due to the benefit obtained from the electricity peak/off-peak tariff.