A Comparative Study of Data Storage and Processing Architectures for the Smart Grid
- 1 October 2010
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
A number of governments and organizations around the world agree that the first step to address national and international problems such as energy independence, global warming or emergency resilience, is the redesign of electricity networks, known as Smart Grids. Typically, power grids have "broadcasted" power from generation plants to large population of consumers on a suboptimal way. Nevertheless, the fusion of energy delivery networks and digital information networks, along with the introduction of intelligent monitoring systems (Smart Meters) and renewable energies, would enable two- way electricity trading relationships between electricity suppliers and electricity consumers. The availability of real-time information on electricity demand and pricing, would enable suppliers optimizing their delivery systems, while consumers would have the means to minimize their bill by turning on appliances at off-peak hours. The construction of the Smart Grid entails the design and deployment of information networks and systems of unprecedented requirements on storage, real-time event processing and availability. In this paper, a series of system architectures to store and process Smart Meter reading data are explored and compared aiming to establish a solid foundation in which future intelligent systems could be supported.Keywords
This publication has 6 references indexed in Scilit:
- Reinventing the AutomobilePublished by MIT Press ,2010
- Hadoop high availability through metadata replicationPublished by Association for Computing Machinery (ACM) ,2009
- Using AMI to realize the Smart GridPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2008
- MapReduceCommunications of the ACM, 2008
- DynamoACM SIGOPS Operating Systems Review, 2007
- Anatomy of the ADO.NET entity frameworkPublished by Association for Computing Machinery (ACM) ,2007