This chapter proposes a hierarchical optimization planning method for multiple electric vehicle (PEV) charging facilities and distributed renewable power sources (DGs) in active power distribution systems, explicitly considering the coupling between transportation and distribution networks. With the increasing integration of renewable energy and the growing demand for electric vehicle charging, the inherent uncertainty of DG output and the complex interdependencies between the two networks pose significant challenges to system planning and operation. To address these issues, the K-means clustering algorithm is first employed to analyze historical DG output data, extracting representative daily output curves that capture the stochastic characteristics of renewable generation. Subsequently, a multi-objective collaborative optimization model is established for conventional charging parking lots, fast charging stations, and DGs, incorporating various operational constraints from both networks. A hierarchical solution approach is adopted to sequentially determine the optimal siting and sizing of each facility: the locations of conventional parking lots are based on existing residential and commercial nodes; fast charging station locations are optimized to minimize total travel distance for PEV users; the capacity of fast charging stations is then determined using a queuing theory-based economic model to maximize annualized profit; finally, the allocation of DGs is optimized to minimize a composite index of active power loss, reactive power loss, and node voltage fluctuation in the distribution network. The proposed method is validated on IEEE 53-bus and 123-bus distribution systems coupled with a transportation network. Results demonstrate that the hierarchical planning significantly reduces network losses and voltage deviations compared to scenarios with only PEV charging facilities, effectively mitigating the adverse impacts of uncoordinated charging. Moreover, most DGs are optimally located near charging facilities to enhance renewable energy utilization and provide local power support. This approach simplifies the complex nonlinear planning problem, balances economic and operational objectives, and exhibits good scalability for application in larger, more complex networks.