Stochastic game

About: Stochastic game is a research topic. Over the lifetime, 9493 publications have been published within this topic receiving 202664 citations.

Stochastic Games for the Smart Grid Energy Management With Prospect Prosumers

Abstract: In this paper, the problem of the smart grid energy management under stochastic dynamics is investigated. In the considered model, at the demand side, it is assumed that customers can act as prosumers who own renewable energy sources and can both produce and consume energy. Due to the coupling between the prosumers’ decisions and the stochastic nature of renewable energy, the interaction among prosumers is formulated as a stochastic game, in which each prosumer seeks to maximize its payoff, in terms of revenues, by controlling its energy consumption and demand. In particular, the subjective behavior of prosumers is explicitly reflected into their payoff functions using the prospect theory, a powerful framework that allows modeling real-life human choices, rather than objective, user-agnostic decisions, as normative models do. For this prospect-based stochastic game, it is shown that there always exists a stationary Nash equilibrium where the prosumers’ trading policies in the equilibrium are independent of the time and their histories of the play. Moreover, to obtain one of such equilibrium policies, a novel distributed algorithm with no information sharing among prosumers is proposed and shown to converge to an $\epsilon$ -Nash equilibrium in which each prosumer is able to achieve its optimal payoff in an equilibrium up to a small additive error $\epsilon$ . On the other hand, at the supply side, the interaction between the utility company and the prosumers is formulated as an online optimization problem in which the utility company's goal is to learn its optimal energy allocation rules. For this case, it is shown that such an optimization problem admits a no-regret algorithm meaning that regardless of the actual outcome of the game among the prosumers, the utility company can follow a strategy that mitigates its allocation costs as if it knew the entire demand market a priori . Simulation results justify the convergence of the proposed algorithms and present new insights toward more efficient energy management in the smart grids.

Volatile decision dynamics: experiments, stochastic description, intermittency control and traffic optimization

Abstract: The coordinated and efficient distribution of limited resources by individual decisions is a fundamental, unsolved problem. When individuals compete for road capacities, time, space, money, goods, etc, they normally make decisions based on aggregate rather than complete information, such as TV news or stock market indices. In related experiments, we have observed a volatile decision dynamics and far-from-optimal payoff distributions. We have also identified methods of information presentation that can considerably improve the overall performance of the system. In order to determine optimal strategies of decision guidance by means of user-specific recommendations, a stochastic behavioural description is developed. These strategies manage to increase the adaptibility to changing conditions and to reduce the deviation from the time-dependent user equilibrium, thereby enhancing the average and individual payoffs. Hence, our guidance strategies can increase the performance of all users by reducing overreaction and stabilizing the decision dynamics. These results are highly significant for predicting decision behaviour, for reaching optimal behavioural distributions by decision support systems and for information service providers. One of the promising fields of application is traffic optimization.

Surveying Game Theoretic Approaches for Wind Farm Optimization

Abstract: This paper surveys recent results in game theory and cooperative control and highlights their implications for the problem of optimizing energy production in wind farms. One such result is a simple payoff-based learning rule that is completely decentralized and leads to an efficient configuration of actions in virtually any distributed system. We demonstrate that this learning rule can be used to provably maximize energy production in wind farms without explicitly modeling the aerodynamic interaction amongst the turbines.

4G Converged Environment: Modeling Network Selection as a Game

Abstract: Network selection mechanism in 4G mobile networks is a newly introduced mechanism that handles the selection of the best network to satisfy a service request. Network selection needs to take part prior to admission of the service in a particular access network participating in the 4G system. This paper proposes to model this decision using a game theoretic approach; specifically defining a game between the access networks themselves, competing in a non-cooperative manner to maximize their payoff. The outcome of the game is a decision of which subset of the set of service requests made to the 4G converged system should be admitted by each access network.