In modeling of distributed systems with distributed sources large networks with RLC-elements and independent sources arise. This high complexity leads to a high effort in simulations. Therefore model reduction can be used to reduce these networks, preserving the behavior at the observed nodes in the networks. For the reduction of networks with a large number of independent sources only a weak reduction is enabled with standard model reduction techniques. In this paper an efficient reduction of networks with a large number of sources with piece-wise-linear waveforms is presented, using the decomposition of piece-wise-linear functions. With the proposed method a higher reduction of the network and/or a higher accuracy can be achieved with model reduction. The validity and efficiency of the proposed method is shown by reducing a RCI-Grid model.