The Structure and Complexity of Nash Equilibria for a Selfish Routing Game
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Scheduling Resources in Multi-User, Heterogeneous, Computing Environments with SmartNet
HCW '98 Proceedings of the Seventh Heterogeneous Computing Workshop
Dynamic Matching and Scheduling of a Class of Independent Tasks onto Heterogeneous Computing Systems
HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
A deployed multi-agent framework for distributed energy applications
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
A Service-Oriented System for Optimizing Residential Energy Use
ICWS '09 Proceedings of the 2009 IEEE International Conference on Web Services
Convergence time to Nash equilibria
ICALP'03 Proceedings of the 30th international conference on Automata, languages and programming
User-sensitive scheduling of home appliances
Proceedings of the 2nd ACM SIGCOMM workshop on Green networking
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Demand Response(DR) is a method for achieving energy efficiency through stimulating end-users to adjust their demand to respond to change in electricity markets. Significant consumption and cost savings can potentially be made via DR strategies. However, the lack of knowledge of how to develop and apply these strategies is a barrier for small businesses and residential users taking advantage of this method. With the emerging Smart Grid technologies, it is now possible to build a cost-effective computing infrastructure by combining Web services and off-the-shelf home automation equipment to address this problem. As Smart Grids roll out, a significant challenge is effective management of automated DR in a large scale. For example, the lack of coordination among DR participants may diminish the energy saving effort of an individual. Also, balancing energy efficiency and user satisfaction is another unresolved DR challenge for Smart Grids. In this paper, we propose a method to aggregate and manage end-users' preferences to maximize both energy efficiency and user satisfaction. We also give an algorithm for multiple such service providers to optimize outcomes through selfish load-balancing. Our method can be implemented with emerging open standard for Automated DR. Our extensive simulation shows the effectiveness of the proposed method.