4,644 research outputs found
A Game-Theoretic Approach to Energy Trading in the Smart Grid
Electric storage units constitute a key element in the emerging smart grid
system. In this paper, the interactions and energy trading decisions of a
number of geographically distributed storage units are studied using a novel
framework based on game theory. In particular, a noncooperative game is
formulated between storage units, such as PHEVs, or an array of batteries that
are trading their stored energy. Here, each storage unit's owner can decide on
the maximum amount of energy to sell in a local market so as to maximize a
utility that reflects the tradeoff between the revenues from energy trading and
the accompanying costs. Then in this energy exchange market between the storage
units and the smart grid elements, the price at which energy is traded is
determined via an auction mechanism. The game is shown to admit at least one
Nash equilibrium and a novel proposed algorithm that is guaranteed to reach
such an equilibrium point is proposed. Simulation results show that the
proposed approach yields significant performance improvements, in terms of the
average utility per storage unit, reaching up to 130.2% compared to a
conventional greedy approach.Comment: 11 pages, 11 figures, journa
Applications of Repeated Games in Wireless Networks: A Survey
A repeated game is an effective tool to model interactions and conflicts for
players aiming to achieve their objectives in a long-term basis. Contrary to
static noncooperative games that model an interaction among players in only one
period, in repeated games, interactions of players repeat for multiple periods;
and thus the players become aware of other players' past behaviors and their
future benefits, and will adapt their behavior accordingly. In wireless
networks, conflicts among wireless nodes can lead to selfish behaviors,
resulting in poor network performances and detrimental individual payoffs. In
this paper, we survey the applications of repeated games in different wireless
networks. The main goal is to demonstrate the use of repeated games to
encourage wireless nodes to cooperate, thereby improving network performances
and avoiding network disruption due to selfish behaviors. Furthermore, various
problems in wireless networks and variations of repeated game models together
with the corresponding solutions are discussed in this survey. Finally, we
outline some open issues and future research directions.Comment: 32 pages, 15 figures, 5 tables, 168 reference
Distributed Task Management in Cyber-Physical Systems: How to Cooperate under Uncertainty?
We consider the problem of task allocation in a network of cyber-physical
systems (CPSs). The network can have different states, and the tasks are of
different types. The task arrival is stochastic and state-dependent. Every CPS
is capable of performing each type of task with some specific state-dependent
efficiency. The CPSs have to agree on task allocation prior to knowing about
the realized network's state and/or the arrived tasks. We model the problem as
a multi-state stochastic cooperative game with state uncertainty. We then use
the concept of deterministic equivalence and sequential core to solve the
problem. We establish the non-emptiness of the strong sequential core in our
designed task allocation game and investigate its characteristics including
uniqueness and optimality. Moreover, we prove that in the task allocation game,
the strong sequential core is equivalent to Walrasian equilibrium under state
uncertainty; consequently, it can be implemented by using the Walras'
tatonnement process
Dynamic Pricing Problems Arising in the Adoption of Renewable Energy
There are two problems at the interface of electrical power and economics that are examined
in this thesis. The first problem addresses the issue of optimally operating electric vehicle (EV)
charging stations, where price as well as scheduling of purchasing, storing, and charging play key
roles. The second problem addresses the challenge faced by electric power system operators who
have to balance power generation and demand at all times, and are faced with the task of maximizing
the social welfare of all affected entities comprised of producers, consumers and prosumers
(e.g., homes with solar panels who may be producers at some times and consumers at other times).
For the first problem, we have developed a layered decomposition approach that permits a
holistic solution to solving the scheduling, storage and pricing problems of charging stations. The
key idea is to decompose problems by time-scale.
For the second problem, we have shown that for the special case of LQG agents, by careful
construction of a sequence of layered VCG payments over time, the intertemporal effect of current
bids on future payoffs can be decoupled, and truth-telling of dynamic states is guaranteed if system
parameters are known and agents are rational. We have also shown that a modification of the VCG
payments, called scaled-VCG payments, achieves Budget Balance and Individual Rationality for a
range of scaling, under a certain identified Market Power Balance condition
Multi-agent network games with applications in smart electric mobility
The growing complexity and globalization of modern society brought to light novel problems and challenges for researchers that aim to model real-life phenomena. Nowadays communities and even single individuals cannot be considered as a closed system, since one's actions create a ripple effect that ends up influencing the action of others. Therefore, the study of decision-making processes over networks became a pivotal topic in the research community. The possible applications are virtually endless and span into many different fields. Two of the most relevant examples are smart mobility and energy management in highly populated cities, where a collection of (partially) noncooperative individuals interact over a network trying to reach an efficient equilibrium point, in the sense of Nash, and share limited resources due to the environment in which they operate. In this work, we approach these problems through the lens of game theory. We use different declinations of this powerful mathematical tool to study several aspects of these themes. We design decentralized iterative algorithms solving generalized network games that generate behavioral rules for the players that, if followed, ensure global convergence. Then, we question the classical assumption of perfect players’ rationality by introducing novel dynamics to model partial rationality and analyzing their properties. We conclude by focusing on the design of optimal policies to regulate smart mobility and energy management. In this case, we create a detailed and more realistic description of the problem and use a nudging mechanism, implemented by means of a semi-decentralized algorithm, to align the users' behavior with the one desired by the policymaker
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