Distributed Algorithms
Fast load balancing via bounded best response
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
Game Theoretic Problems in Network Economics and Mechanism Design Solutions
Game Theoretic Problems in Network Economics and Mechanism Design Solutions
Agent-based micro-storage management for the Smart Grid
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
How internet concepts and technologies can help green and smarten the electrical grid
ACM SIGCOMM Computer Communication Review
Optimal power cost management using stored energy in data centers
Proceedings of the ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Agent-based control for decentralised demand side management in the smart grid
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
The impact of electricity pricing schemes on storage adoption in Ontario
Proceedings of the 3rd International Conference on Future Energy Systems: Where Energy, Computing and Communication Meet
RealTime distributed congestion control for electrical vehicle charging
ACM SIGMETRICS Performance Evaluation Review
The case for efficient renewable energy management in smart homes
Proceedings of the Third ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
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Growing fuel costs, environmental awareness, government directives, an aggressive push to deploy Electric Vehicles (EVs) (a single EV consumes the equivalent of 3 to 10 homes) have led to a severe strain on a grid already on the brink. Maintaining the stability of the grid requires automatic agent based control of these loads and rapid coordination between them. In the literature, a number of iterative pricing, signaling and tâtonnement (or bargaining) approaches have been proposed to allow smart homes, storage devices and the autonomous agents that control them to be responsive to the state of the grid in a distributed manner. These existing approaches are not scalable due to slow convergence and moreover the approaches are not incentive compatible. In this paper, we present a tâtonnement framework for resource allocation among intelligent agents in the smart grid, that non-trivially generalizes past work in this area. Our approach based on the work in server load balancing involves communicating carefully chosen, centrally verifiable constraints on the set of actions available to agents and cost functions, leading to distributed, incentive compatible protocols that converge in a constant number of iterations, independent of the number of users. These protocols can work on the top of prior approaches and result in a substantial speed-up, while ensuring that it is in the best interests of the agents to be truthful. We demonstrate this theoretically and through extensive simulations for three important scenarios that have been discussed in the literature. We extend the techniques to account for capacity limits in each time slot, the EV charging problem and the distributed storage control problem. We establish the generality and usefulness of this technique and making the case that it should be incorporated into future smart grid protocols.