In this paper, we describe an ongoing research on an energy efficient key/value store. The focus of the paper is to provide dynamic adaptability to PowerCass which is an energy invariant of Apache Cassandra — a highly scalable distributed data store. The paper addressed the challenge of improving performance through dynamic data relocation while preserving energy efficiency and the fast zero-hop (constant time routing) data lookup attained through consistent hashing in PowerCass. In PowerCass energy efficiency is addressed through 3-way gear leveling in which nodes are classified into three groups: active, dormant, and sleepy and handle high, medium and low load by leveraging usage patterns in data accesses. The energy efficiency results show that the energy consumption of dyn-PowerCass is comparable to the static PowerCass which reduced energy consumption by up to 66PowerCass improved by at least 10% when compared to the static PowerCass and the latency also reduced by the same magnitude.