Compute clusters are major power consumers in Cloud and Edge data centers, making it critical to reduce power usage and costs without compromising service levels objectives. Energy Performance Preference (EPP) settings and CPU frequency scaling can lower power but typically at the cost of reduced performance. When considering clusters with heterogeneous power profiles, it is essential to map workloads to the most suitable profile based on their quality-of-service constraints. Current orchestrators overlook power-profile heterogeneity; this is a particular concern at the Edge, where otherwise identical hardware may range from power-optimized to performance-oriented yet remain indistinguishable to schedulers. We present a taxonomy of power-aware orchestration, and extend the default Kubernetes scheduler with power-profile awareness. We evaluate the feasibility of this extended scheduler by comparing three power profile-aware scheduling strategies on a testbed running a microservices benchmark, with results showing that average power use can be reduced by up to 12% while maintaining application performance. We conclude with key challenges and future research directions.