
PeertoPeer Electricity Market Analysis: From Variational to Generalized Nash Equilibrium
We consider a network of prosumers involved in peertopeer energy excha...
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Price of Privacy in the Keynesian Beauty Contest
The Keynesian Beauty Contest is a classical game in which strategic agen...
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RiskAverse Equilibrium Analysis and Computation
We consider two market designs for a network of prosumers, trading energ...
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OperationallySafe PeertoPeer Energy Trading in Distribution Grids: A GameTheoretic MarketClearing Mechanism
In future distribution grids, prosumers (i.e., energy consumers with sto...
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Quadratic PrivacySignaling Games, Payoff Dominant Equilibria and the Information Bottleneck Problem
We introduce a privacysignaling game problem in which a transmitter wit...
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A rational decentralized generalized Nash equilibrium seeking for energy markets
We propose a method to design a decentralized energy market which guaran...
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Equilibrium Characterization for Data Acquisition Games
We study a game between two firms in which each provide a service based ...
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Privacy Impact on Generalized Nash Equilibrium in PeertoPeer Electricity Market
We consider a peertopeer electricity market, where agents hold private information that they might not want to share. The problem is modeled as a noncooperative communication game, which takes the form of a Generalized Nash Equilibrium Problem, where the agents determine their randomized reports to share with the other market players, while anticipating the form of the peertopeer market equilibrium. In the noncooperative game, each agent decides on the deterministic and random parts of the report, such that (a) the distance between the deterministic part of the report and the truthful private information is bounded and (b) the expectation of the privacy loss random variable is bounded. This allows each agent to change her privacy level. We characterize the equilibrium of the game, prove the uniqueness of the Variational Equilibria and provide a closed form expression of the privacy price. In addition, we provide a closed form expression to measure the impact of the privacy preservation caused by inclusion of random noise and deterministic deviation from agents' true values. Numerical illustrations are presented on the 14bus IEEE network.
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