Distributed Demand Management in Smart Grid with a Congestion Game

Abstract

In this paper we propose distributed load management in smart grid infrastructures to control the power demand at peak hours, by means of dynamic pricing strategies. The distributed solution that we propose is based on a network congestion game, which can be demonstrated to converge in a finite number of steps to a pure Nash equilibrium solution. We take advantage of the remarkable property of congestion games, according to which they are equivalent to potential games. We define a potential function characterized by a meaningful physical interpretation, so that we obtain the favorable result that the optimal local solution of each selfish consumer is also the solution of a global objective. We evaluate this approach for managing both the demand and the grid load and we show that load control can be effectively achieved implementing a distributed solution, which significantly reduce the signaling burden over the network.

A game-theoretic approach to optimizing the scale of incorporating renewable sources of energy and electricity storing systems in a regional electrical grid

“…as piece-wise approximations of the convex functions p z j (l) be the price at which a unit volume of electric energy is sold by the generator to utility company j, j ∈ 1, n, in the period of time from hour l −1 to hour l, l ∈ 1, 24, p u k (l) be the price at which a unit volume of electric energy is sold by the generator to group of advanced customers k, k ∈ 1, r in the period of time from hour l − 1 to hour l, l ∈ 1, 24, (24) be the vector whose component p u k (l) is the price at which a unit volume of electric energy is sold to group of advanced customers k by the generator in the period of time from hour l − 1 to hour l, k ∈ 1, r , l ∈ 1, 24.…”

“…. , 0), where˜ u is the vector of the same dimension asũ with all non-zero components equalling 1, where u ∈ R 24 + is the vector with all the components equalling 1. With the use of this notation, the goal function of group of advanced customers k, k ∈ 1, r can be written as follows:…”

“…In the last several years, papers on using game-theoretic approaches to studying various aspects of "smart" grid such as, for instance, energy consumption schedules [21,22], demand-side management problems [23,24], including those in which the presence of systems for storing electricity is taken into consideration [25], were published. However, none of all the above publications deals with problems under consideration in the present paper.…”

“…However, none of all the above publications deals with problems under consideration in the present paper. It is for this reason, the author decided to provide a brief review of a set of examples from three groups of typical publications on issues more closely related to the subject of the present paper than those in the publications [21][22][23][24][25] to let the reader form the impression on what concerns researchers in the field the most.…”

Ad Hoc LTE Method for Resilient Smart Grid Communications

“…A remote terminal unit (RTU), a communication interface connected to a smart meter, transmits information collected by the smart meter to a metering data management system that processes it and delivers feedback. For example, a smart meter could provide the current energy consumption value and a metering data management system could specify the electricity price due to the total consumption of multiple smart meters [2,3], or a system operator could turn on/off each user's device according to a direct load control program that follows the grid state and the defined load shaping policy [4]. There are various options for the applied wireless communication technology such as long term evolution (LTE), worldwide interoperability for microwave access (WiMAX), IEEE 802.15.4 and IEEE 802.11 [5].…”