9,473 research outputs found
Efficient Energy Distribution in a Smart Grid using Multi-Player Games
Algorithms and models based on game theory have nowadays become prominent
techniques for the design of digital controllers for critical systems. Indeed,
such techniques enable automatic synthesis: given a model of the environment
and a property that the controller must enforce, those techniques automatically
produce a correct controller, when it exists. In the present paper, we consider
a class of concurrent, weighted, multi-player games that are well-suited to
model and study the interactions of several agents who are competing for some
measurable resources like energy. We prove that a subclass of those games
always admit a Nash equilibrium, i.e. a situation in which all players play in
such a way that they have no incentive to deviate. Moreover, the strategies
yielding those Nash equilibria have a special structure: when one of the agents
deviate from the equilibrium, all the others form a coalition that will enforce
a retaliation mechanism that punishes the deviant agent. We apply those results
to a real-life case study in which several smart houses that produce their own
energy with solar panels, and can share this energy among them in micro-grid,
must distribute the use of this energy along the day in order to avoid
consuming electricity that must be bought from the global grid. We demonstrate
that our theory allows one to synthesise an efficient controller for these
houses: using penalties to be paid in the utility bill as an incentive, we
force the houses to follow a pre-computed schedule that maximises the
proportion of the locally produced energy that is consumed.Comment: In Proceedings Cassting'16/SynCoP'16, arXiv:1608.0017
A Distributed Demand-Side Management Framework for the Smart Grid
This paper proposes a fully distributed Demand-Side Management system for
Smart Grid infrastructures, especially tailored to reduce the peak demand of
residential users. In particular, we use a dynamic pricing strategy, where
energy tariffs are function of the overall power demand of customers. We
consider two practical cases: (1) a fully distributed approach, where each
appliance decides autonomously its own scheduling, and (2) a hybrid approach,
where each user must schedule all his appliances. We analyze numerically these
two approaches, showing that they are characterized practically by the same
performance level in all the considered grid scenarios. We model the proposed
system using a non-cooperative game theoretical approach, and demonstrate that
our game is a generalized ordinal potential one under general conditions.
Furthermore, we propose a simple yet effective best response strategy that is
proved to converge in a few steps to a pure Nash Equilibrium, thus
demonstrating the robustness of the power scheduling plan obtained without any
central coordination of the operator or the customers. Numerical results,
obtained using real load profiles and appliance models, show that the
system-wide peak absorption achieved in a completely distributed fashion can be
reduced up to 55%, thus decreasing the capital expenditure (CAPEX) necessary to
meet the growing energy demand
Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approaches
Peer-to-peer (P2P) energy trading has emerged as a next-generation energy
management mechanism for the smart grid that enables each prosumer of the
network to participate in energy trading with one another and the grid. This
poses a significant challenge in terms of modeling the decision-making process
of each participant with conflicting interest and motivating prosumers to
participate in energy trading and to cooperate, if necessary, for achieving
different energy management goals. Therefore, such decision-making process
needs to be built on solid mathematical and signal processing tools that can
ensure an efficient operation of the smart grid. This paper provides an
overview of the use of game theoretic approaches for P2P energy trading as a
feasible and effective means of energy management. As such, we discuss various
games and auction theoretic approaches by following a systematic classification
to provide information on the importance of game theory for smart energy
research. Then, the paper focuses on the P2P energy trading describing its key
features and giving an introduction to an existing P2P testbed. Further, the
paper zooms into the detail of some specific game and auction theoretic models
that have recently been used in P2P energy trading and discusses some important
finding of these schemes.Comment: 38 pages, single column, double spac
Managing Price Uncertainty in Prosumer-Centric Energy Trading: A Prospect-Theoretic Stackelberg Game Approach
In this paper, the problem of energy trading between smart grid prosumers,
who can simultaneously consume and produce energy, and a grid power company is
studied. The problem is formulated as a single-leader, multiple-follower
Stackelberg game between the power company and multiple prosumers. In this
game, the power company acts as a leader who determines the pricing strategy
that maximizes its profits, while the prosumers act as followers who react by
choosing the amount of energy to buy or sell so as to optimize their current
and future profits. The proposed game accounts for each prosumer's subjective
decision when faced with the uncertainty of profits, induced by the random
future price. In particular, the framing effect, from the framework of prospect
theory (PT), is used to account for each prosumer's valuation of its gains and
losses with respect to an individual utility reference point. The reference
point changes between prosumers and stems from their past experience and future
aspirations of profits. The followers' noncooperative game is shown to admit a
unique pure-strategy Nash equilibrium (NE) under classical game theory (CGT)
which is obtained using a fully distributed algorithm. The results are extended
to account for the case of PT using algorithmic solutions that can achieve an
NE under certain conditions. Simulation results show that the total grid load
varies significantly with the prosumers' reference point and their
loss-aversion level. In addition, it is shown that the power company's profits
considerably decrease when it fails to account for the prosumers' subjective
perceptions under PT
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