Dynamic Coalition Formation among Rational Agents
IEEE Intelligent Systems
Efficient Public-Key Cryptosystems Provably Secure Against Active Adversaries
ASIACRYPT '99 Proceedings of the International Conference on the Theory and Applications of Cryptology and Information Security: Advances in Cryptology
Equivalence between Semantic Security and Indistinguishability against Chosen Ciphertext Attacks
PKC '03 Proceedings of the 6th International Workshop on Theory and Practice in Public Key Cryptography: Public Key Cryptography
Security and Privacy Challenges in the Smart Grid
IEEE Security and Privacy
Enabling applicability of energy saving applications on the appliances of the home environment
IEEE Network: The Magazine of Global Internetworking
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
Agent-based control for decentralised demand side management in the smart grid
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Deploying power grid-integrated electric vehicles as a multi-agent system
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Cooperatives of distributed energy resources for efficient virtual power plants
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Putting the 'smarts' into the smart grid: a grand challenge for artificial intelligence
Communications of the ACM
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In this work we focus on one particular area of the smart grid, namely, the challenges faced by distribution network operators in securing the balance between supply and demand in the intraday market, as a growing number of load controllable devices and small-scale, intermittent generators coming from renewables are expected to pervade the system. We introduce a multi-agent design to facilitate coordinating the various actors in the grid. The underpinning of our approach consists of an online cooperation scheme, eCOOP, where agents learn a prediction model regarding potential coalition partners and thus, can respond in an agile manner to situations that are occurring in the grid, by means of negotiating and formulating speculative solutions, with respect to the estimated behavior of the system. We provide a computational characterisation for our solution in terms of complexity, as well as an empirical analysis against the state-of-the-art mechanism, showing a performance improvement of about 14%.