Smallcells have recently emerged as a potential approach for local area deployments that can satisfy high data rate requirements, reduce energy consumption and enhance network coverage. In this paper, we work on maximizing the weighted sum energy efficiency (WS-EE) for densely deployed smallcell networks. Due to the combinatorial and the general fractional program nature of the resource allocation problem, WS-EE maximization is non-convex and the optimal joint resource blocks (RBs) and power allocation is NP-hard. To solve this complex problem, we propose to decompose the primal problem into two subproblems (referred as RBs allocation and power control) and solve the subproblems sequentially. For the RBs allocation subproblem given any feasible network power profile, the optimal solution can be solved by maximizing throughput locally. For the power control subproblem, we propose to solve it locally based on a new defined pricing factor. Then, a distributed power control algorithm with guaranteed convergence is designed to achieve a Karush-Kuhn-Tucker (KKT) point of the primal problem. Simulation results verify the performance improvement of our proposed resource allocation scheme in terms of WS-EE. Besides, the performance evaluation shows the tradeoff between the WS-EE and the sum rate of the smallcell networks.