This work addresses a new problem of dynamic voltage scaling (DVS) in multicore platforms. We solve the multicore DVS problem, i.e., simultaneously scheduling execution of tasks assigned to cores and determining dynamically-varying voltage levels, with the objective of minimizing total energy consumption of the cores and voltage regulators (VRs) in the reconfigurable VR-to-core power distribution network (PDN) of platform while meeting the arrival/deadline constraint of tasks. Here, the key factors to be exploited for energy saving are (1) available voltage levels, (2) power conversion efficiency curve of VRs, and (3) turning on/off VRs. Specifically, we formulate the problem of task scheduling with the relation between factors 1, 2, and 3 into a linear programming problem and solve optimally in polynomial time. In addition, we provide a theoretical bound on the number of voltage transitions and show that the number of transitions is very low in practice. Through a set of experiments it is shown that our proposed DVS solution is able to reduce the energy consumption by 3.8~6.1% over that of the conventional state-of-the-art multicore DVS method, which also uses the multicore platforms with reconfigurable PDN.