Dispatch of distributed generators under local-information constraints

Pantoja, Andrés; Quijano, Nicanor; Passino, K.M.

doi:10.1109/acc.2014.6859190

Cited by 28 publications

(22 citation statements)

References 18 publications

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“…On the other hand, the coverage of the total demand is respected because the set ∆ is also invariant under the distributed replicator dynamics equation. This property has been proved in [71] using some topological properties of the connected graphs to show that iṗ i = 0 when p(0) ∈ ∆. With this property, it is ensured that the demanded power P D is supplied by the DGs in the dispatch process.…”

Section: Distributed Replicator Dynamics For Optimal Dispatchmentioning

confidence: 98%

“…The main stability result has been recently presented in [71] as a proposition, which states the general payoff functions conditions to guarantee the convergence of the method.…”

Section: Distributed Replicator Dynamics For Optimal Dispatchmentioning

confidence: 99%

“…A communication infrastructure with limited message passing can be effectively used among the multi-agent system. To guarantee the power September 16, 2016 DRAFT 17 balance, the results presented in [71] are used, where it is proven the invariance of the constraint set ∆. The full-information constraint for the calculation of the original average payoff is relaxed and the distributed replicator dynamics equation can be used to satisfy the network constraints in the general EDP given by the topology of the graph.…”

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confidence: 99%

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The Role of Population Games and Evolutionary Dynamics in Distributed Control Systems: The Advantages of Evolutionary Game Theory

et al. 2017

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Recently, there has been an increasing interest in studying large-scale distributed systems in the control community. Efforts have been invested in developing several techniques wishing to address the main challenges found in this kind of problems, for instance, the amount of information to guarantee the proper operation of the system and the economic costs associated to the required communication structure. Moreover, another issue appears when there is a large amount of required data to control the system. The measuring and transmission processes, and the computation of the control inputs make closed-loop systems suffer from high computational burden.One way to overcome such problems is to consider the use of multi-agent systems framework, which may be cast in game-theoretical terms. Game theory studies interactions between self-interested agents. Particularly, this theory tackles with the problem of interaction between agents using different strategies that wish to maximize their welfares. For instance in [1], the authors provide connections between games, optimization, and learning for signal processing in networks. Other approaches in terms of learning and games can be found in [2]. In [3], distributed computation algorithms are developed based on generalized convex games that do not require full information, and where there is a dynamic change in terms of network topologies. Applications of game theory in control of optical networks and game-theoretic methods for smart grids are described in [4], [5], [6]. Another approach in game theoretical methods is to design protocols or mechanisms that possess some desirable properties [7]. This approach leads to a broad analysis of multi-agent interactions, particularly those involving negotiation and coordination problems [8]. Other game-theoretical applications to engineering are reported in [9].From a game-theoretical perspective, it can be distinguished among three types of games: matrix games, continuous games, and differential/dynamic games (see "The relationship among matricial games, full-potential population games, and resource allocation problems"). In matrix games (that generally use the normal form), individuals play simultaneously and only once, and the decision is mainly based on a static terms. In this case, players or agents are individually treated. Differently, in continuous games, players have infinitely many pure strategies [10], [11]. On the other hand, in dynamic games it is assumed that players can use some type of learning mechanism that allows them to adjust the actions taken based on their past decisions. Basically, it can be said that dynamic games are characterized by three main problems: i) how to model the environment where players interact; ii) how to model the objectives of players; and iii) how to specify the order in which the actions are taken and how much information each player possesses. In this case, it is assumed that there are interactions between large number of agents (which are usually unknown) [12].Among these dynamic game...

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Section: Distributed Replicator Dynamics For Optimal Dispatchmentioning

confidence: 98%

“…The main stability result has been recently presented in [71] as a proposition, which states the general payoff functions conditions to guarantee the convergence of the method.…”

Section: Distributed Replicator Dynamics For Optimal Dispatchmentioning

confidence: 99%

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confidence: 99%

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The Role of Population Games and Evolutionary Dynamics in Distributed Control Systems: The Advantages of Evolutionary Game Theory

et al. 2017

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“…Evolutionary game dynamics has been successfully applied as a tool to solve NP-hard combinatorial optimization problems [16], [17]. Recently, in the energy and power systems domain, authors of [18], [19] introduced an approach for distributed optimal dispatch of distributed generators under communication infrastructure constraints.…”

Section: Introductionmentioning

confidence: 99%

Mixed strategist dynamics application to electrical vehicle distributed load scheduling

Ovalle

Hably

Bacha

et al. 2015

IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society

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In this paper, an application of evolutionary game dynamics is proposed in order to profit from the desirable features of mixed strategist dynamics in the solution of distributed resource allocation problems. The key idea of this approach is to define mixed strategies that represent the vertices of the convex hull of the set of feasible solutions defined by the constraints of the local optimization problems. With this approach, the dynamics of the state vector is restricted to a subset of the simplex. Details of the defintion of appropriate mixed strategies, payoff functions, and the multi-population modeling followed for this problem are provided.

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“…In [8], the authors developed a distributed algorithm that is resilient against potential packet drops and applied the algorithm to DER coordination. In [9], a strategy based on the local replicator equation was presented for economic dispatch of DGs. Other algorithms that can be applied to the DER coordination include the leaderfollower consensus algorithm [10], two-level incremental cost consensus algorithm [11], distributed algorithm based on the consensus and bisection method [12], and minimum-time consensus algorithm [13], just to name a few.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical control framework for integrated coordination between distributed energy resources and demand response

Lian

Sun

et al. 2017

Electric Power Systems Research

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Dispatch of distributed generators under local-information constraints

Cited by 28 publications

References 18 publications

The Role of Population Games and Evolutionary Dynamics in Distributed Control Systems: The Advantages of Evolutionary Game Theory

The Role of Population Games and Evolutionary Dynamics in Distributed Control Systems: The Advantages of Evolutionary Game Theory

Mixed strategist dynamics application to electrical vehicle distributed load scheduling

Hierarchical control framework for integrated coordination between distributed energy resources and demand response

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