With the end of Denard scaling, future systems will be constrained by power and energy. This will impact application developers by forcing them to restructure and optimize their algorithms in terms of these resources. In this paper, we analyze the impact of different code optimizations on power, energy, and execution time. Our optimizations include loop fusion, data structure transformations, global allocation, and compiler selection. We analyze the static and dynamic components of power and energy as applied to the processor chip and memory domains within a system. In addition, our analysis correlates energy and power changes with performance events and shows that data motion is highly correlated with memory power and energy usage and executed instructions are partially correlated with processor power and energy. Our results demonstrate key tradeoffs among power, energy, and execution time for explicit hydrodynamics via a representative kernel. In particular, we observe that loop fusion and compiler selection improve all objectives, while global allocation and data layout transformations present tradeoffs that are objective-dependent.