A dynamic foundational architecture for semantic web services
Distributed and Parallel Databases
Encyclopedia of Cryptography and Security
Encyclopedia of Cryptography and Security
Unifying Human and Software Services in Web-Scale Collaborations
IEEE Internet Computing
The convergence of social and technological networks
Communications of the ACM - Remembering Jim Gray
Web Services: Concepts, Architectures and Applications
Web Services: Concepts, Architectures and Applications
A human-centric runtime framework for mixed service-oriented systems
Distributed and Parallel Databases
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The smart grid promises to improve the efficiency and reliability of tomorrow's energy supply. One of the biggest achievements of future smart grids will be their distributed mode of operation which effectively eliminates vulnerable nodes causing single points of failures in the grid. However, due to the lack of centralized energy production and control, the coordination of energy consumption becomes first priority. Because there do not exist technologies to store energy at large-scale yet, all energy that is required must be produced at the same time. The biggest challenge of energy producers is therefore to reliably predict and provide the right amount of required energy to avoid shortages and breakdowns. In this paper, we propose a novel way to let smart grid stakeholders, i.e., energy producers and consumers, coordinate their energy demands themselves. For that purpose we combine traditional social network models and service-oriented computing concepts with the smart grid to allow consumers to form communities according to their energy consumption behavior. These communities enable them to interact with other grid stakeholders to coordinate energy consumption plans and set up private energy sharing alliances. This way, the utility provider and industrial energy producers can rely on a better predictable and a smoother energy demand of customers. We introduce a software framework, making use of widely adopted standards, demonstrate its feasibility with an agent-based simulation, and discuss its overall applicability.