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Is Energy Storage an Economic Opportunity for the EcoNeighborhood? ∗
, 2013
"... In this article, we consider houses belonging to an econeighborhood in which inhabitants have the capacity to optimize dynamically the energy demand and the energy storage level so as to maximize their utility. The inhabitants ’ preferences are characterized by their sensitivity toward comfort vers ..."
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In this article, we consider houses belonging to an econeighborhood in which inhabitants have the capacity to optimize dynamically the energy demand and the energy storage level so as to maximize their utility. The inhabitants ’ preferences are characterized by their sensitivity toward comfort versus price, the optimal expected temperature in the house, thermal loss and heating efficiency of their house. At his level, the econeighborhood manager shares the resource produced by the econeighborhood according to two schemes: an equal allocation between the houses and a priority based one. The problem is modeled as a stochastic game and solved using stochastic dynamic programming. We simulate the energy consumption of the econeighborhood under various pricing mechanisms: flat rate, peak and offpeak hour, blue/white/red day, peak day clearing and a dynamic update of the price based on the consumption of the econeighborhood. We observe that economic incentives for houses to store energy depend deeply on the implemented pricing mechanism and on the homogeneity in the houses ’ characteristics. Furthermore, when prices are based on the consumption of the econeighborhood, storage appears as a compensation for the errors made by the service provider in the prediction of the consumption of the econeighborhood.
Charging scheduling of electric vehicles with local renewable energy under uncertain electric vehicle arrival and grid power price
 IEEE Trans. Veh. Technol
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Designing pricing strategies for coordination of networked distributed energy resources
"... Abstract: We study the problem of aggregator’s mechanism design for controlling the amount of active, or reactive, power provided, or consumed, by a group of distributed energy resources (DERs). The aggregator interacts with the wholesale electricity market and through some marketclearing mechanism ..."
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Abstract: We study the problem of aggregator’s mechanism design for controlling the amount of active, or reactive, power provided, or consumed, by a group of distributed energy resources (DERs). The aggregator interacts with the wholesale electricity market and through some marketclearing mechanism is incentivized to provide (or consume) a certain amount of active (or reactive) power over some period of time, for which it will be compensated. The objective is for the aggregator to design a pricing strategy for incentivizing DERs to modify their active (or reactive) power consumptions (or productions) so that they collectively provide the amount that the aggregator has agreed to provide. The aggregator and DERs ’ strategic decisionmaking process can be cast as a Stackelberg game, in which aggregator acts as the leader and the DERs are the followers. In previous work [Gharesifard et al., 2013b,a], we have introduced a framework in which each DER uses the pricing information provided by the aggregator and some estimate of the average energy that neighboring DERs can provide to compute a Nash equilibrium solution in a distributed manner. Here, we focus on the interplay between the aggregator’s decisionmaking process and the DERs ’ decisionmaking process. In particular, we propose a simple feedbackbased privacypreserving pricing control strategy that allows the aggregator to coordinate the DERs so that they collectively provide the amount of active (or reactive) power agreed upon, provided that there is enough capacity available among the DERs. We provide a formal analysis of the stability of the resulting closedloop system. We also discuss the shortcomings of the proposed pricing strategy, and propose some avenues of future work. We illustrate the proposed strategy via numerical simulations.
A Game Theoretical Approach to Modeling Energy Consumption with Consumer Preference
"... AbstractWe propose a new game theoretical equilibrium model to analyze residential users' electricity consumption behavior in smart grid where energy usage and price data are exchanged between users and utilities via advanced communication. Consideration is given to users' possible prefe ..."
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AbstractWe propose a new game theoretical equilibrium model to analyze residential users' electricity consumption behavior in smart grid where energy usage and price data are exchanged between users and utilities via advanced communication. Consideration is given to users' possible preference on convenience over costsaving under the realtime pricing in smart grid, and each user is assumed to have a preferred time window for using a particular appliance. As a result, each user (player) in the proposed energy consumption game wishes to maximize a payoff or utility consisting of two parts: the negative of electricity cost and the convenience of using appliances during their preferred time windows. Extensive numerical tests suggest that users with less flexibility on their preferred usage times have larger impact on the system performance at equilibrium. This provide insights for utilities to design their pricing based demand response schemes.
Author manuscript, published in "NETGCOOP 2012, Avignon: France (2012)" Charging Games in Networks of Electrical Vehicles
, 2013
"... Abstract—In this paper, a static noncooperative game formulation of the problem of distributed charging in electrical vehicle (EV) networks is proposed. This formulation allows one to model the interaction between several EV which are connected to a common residential distribution transformer. Each ..."
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Abstract—In this paper, a static noncooperative game formulation of the problem of distributed charging in electrical vehicle (EV) networks is proposed. This formulation allows one to model the interaction between several EV which are connected to a common residential distribution transformer. Each EV aims at choosing the time at which it starts charging its battery in order to minimize an individual cost which is mainly related to the total power delivered by the transformer, the location of the time interval over which the charging operation is performed, and the charging duration needed for the considered EV to have its battery fully recharged. As individual cost functions are assumed to be memoryless, it is possible to show that the game of interest is always an ordinal potential game. More precisely, both an atomic and nonatomic versions of the charging game are considered. In both cases, equilibrium analysis is conducted. In particular, important issues such as equilibrium uniqueness and efficiency are tackled. Interestingly, both analytical and numerical results show that the efficiency loss due to decentralization (e.g., when cost functions such as distribution network Joule losses or life of residential distribution transformers when no thermal inertia is assumed) induced by charging is small and the corresponding ”efficiency”, a notion close to the Price of Anarchy, tends to one when the number of EV increases.
Infrastructure Resilience Using CyberPhysical GameTheoretic Approach
"... Abstract—We consider a class of infrastructures supported by cyber and physical components, which are subject to disruptions. We study reinforcement strategies for cyber and physical components to achieve resilience, specified by the probability of infrastructure survival, against disruptions using ..."
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Abstract—We consider a class of infrastructures supported by cyber and physical components, which are subject to disruptions. We study reinforcement strategies for cyber and physical components to achieve resilience, specified by the probability of infrastructure survival, against disruptions using a gametheoretic formulation. The game utility function is a sum of the infrastructure survival probability term and a cost term. We account for cyberphysical interactions at two different levels: (i) the conditional survival probability of cyber subinfrastructure is specified by a linear function of the marginal probability, and (ii) the survival probabilities of components are determined by the numbers of cyber and physical component attacks as well as reinforcements. At Nash Equilibrium, we identify 12 performance regions based on cyberphysical correlations and component costs, where each is determined by a lower survival probability of either cyber or physical subinfrastructure. We also derive sensitivity functions that highlight the dependence of infrastructure survival probability on cost parameters and component probabilities as well as cyberphysical correlations, under statistical independence conditions. We apply this approach to models of the energy grid derived at different levels of abstraction. I.
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"... Plug in electric vehicles co rt g y. T ew bas the per household per month decrease. 2015 Elsevier Ltd. All rights reserved. pected xpecte ot a US relieve the g relationships and actions among rational players. This characteristic renders it an ideal tool to model and understand the inherent comple ..."
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Plug in electric vehicles co rt g y. T ew bas the per household per month decrease. 2015 Elsevier Ltd. All rights reserved. pected xpecte ot a US relieve the g relationships and actions among rational players. This characteristic renders it an ideal tool to model and understand the inherent complexity of demand response (DR) resulting from this interaction. Publications in this area range from load shifting approaches [7,8] to using storage devices such as PEVs in microgrid storage d and extended to up to 6% household ances, higher savings can be achieved either by adding mor ageable devices to the simulation or by incorporating e vehicles (EVs) of some sort. In this study, the focus will on plugin EVs (PEVs). The remainder of this paper is structured as follows: The fundamental DR simulation platform, Okeanos, is introduced and key concepts are highlighted in Section ‘‘Okeanos”; Results of load ⇑ Corresponding author.