Providing flexible software as a service for smart grid by relying on big data platforms

Abstract

When migrating existing software solutions to cloud platforms, the design should be modified in order to fully utilize the elasticity that cloud platforms provide. Emerging Internet of Things (IoT) technologies will intersect with the smart grid of the future, while big data platforms are a perfect match for storing massive amounts of data in cloud environments. Cloud based services are yet to be adopted in the smart grid, but features that require big data solutions like demand response (DR) will accelerate the shift to cloud services. Among the competition, only effective services will prevail. Consequently, the purpose of this study is to examine approaches to using the elasticity of the cloud in order to create a flexible service, meaning the service consumers can choose between cost and comfort. Hence, the paper proposes a general approach to service design considerations based on big data platforms. The proposed method is applied to the case of demand response feature along with distributed management system (DMS) applications for managing smart grids and is implemented using Apache HBase, the Hadoop database. Since the experiments were set up to give necessary inputs for a design discussion, we tested the solution in the cloud environment. The results were able to match requirements, but more importantly we could draw conclusions as to how we could design the proposed service. Moreover, the same principles could be applied to any service that relies on a big data platform.