Power-aware scheduling has become a critical research thrust for deploying exascale High Performance Computing (HPC) systems with limited power budget. Time-varying pricing of electricity with respect to the market demand and dynamic HPC workloads can lead to unpredictable operational cost, which complicates the scheduling decisions further. For an oversubscribed HPC system, value based scheduling heuristics have been shown to be a more productive option for scheduling time-constrained tasks over priority and deadline based heuristics. However, oversubscribed HPC systems have higher probability of exceeding the power constraints. Earlier studies on value based heuristics do not take power constraints into account during scheduling decisions. In this study, we propose a methodology for deriving task-specific power-execution time models. These models are derived by interpolating the execution time and power consumption measurements over a configuration space parameterized with pairs of dynamic voltage frequency scaling and forced idleness values. We then propose two power-aware value based heuristics, which utilize those models for power capping the nodes and making resource allocation decisions in an oversubscribed homogeneous HPC system. We compare their performance with traditional value based heuristics under a defined power constraint on a real system using different synthetic traces of scientific computing routines. We show that, as power constraints become tighter, the proposed heuristics significantly outperform earlier heuristics in terms of value earning of the HPC system. We also compare the task completion percentage of proposed heuristics and relate the completion percentage with value earnings of the heuristics.