Game-Theoretic Control of Small-Scale Power Systems

Weaver, Wayne W.; Krein, P.T.

doi:10.1109/tpwrd.2008.2007022

Cited by 123 publications

(102 citation statements)

References 17 publications

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“…However, we can obtain some insights on the computational complexity by assuming a constant trading pricep(a) in (19). The computational complexity for comparing the amount sold by sellers with the energy requested by the buyers in (19) is O(L + M ), where seller L and buyer M determine the trading price (see Fig.…”

Section: Phase 2 -Market and Tradingmentioning

confidence: 99%

“…2). In this respect, if assume that the trading price does not change, the amount of computation required to calculatep −si 2τi in (19) is O(1) since this value is independent of the market size. The computational complexity needed for calculating…”

Section: Phase 2 -Market and Tradingmentioning

confidence: 99%

“…A game theoretic approach to control individual sources/loads was adopted in [19], which enhanced the reliability and robustness of a power system without using central control. In [20], a new technique based on cooperative game theory is proposed to allow wind turbines to aggregate their generated energy and optimize their profits.…”

Section: Introductionmentioning

confidence: 99%

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A Game-Theoretic Approach to Energy Trading in the Smart Grid

Wang

Saad

Han

et al. 2014

IEEE Trans. Smart Grid

374

213

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Abstract-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.Index Terms-Electric storage unit, noncooperative games, double auctions, energy management. I. INTRODUCTIONModernizing the electric power grid and realizing the vision of a "smart grid" is contingent upon the deployment of novel smart grid elements such as renewable energy sources and energy storage units [1]. In this respect, electric storage units are inherently devices that can store energy, or extra electricity available at participating customers. Deployment of storage units in future smart grid systems faces many challenges at different levels such as studying the impact of integrating storage units on the grid's operation, determining the required grid infrastructure (communication and control nodes) to enable smart energy exchange, and developing new power management strategies [2]- [6]. The potential economic impact of deploying energy storage units was explored in [7], which also studied the feasible level of energy storage in the distribution system. The possibility of having groups of controllable loads and sources of energy in power systems was investigated in [8] which deployed a distribution network of solar panels or wind turbines. In [9], distributed resources are allocated by the provision of twoway energy flow and a unified, operational value proposition of energy storage is presented. Other related problems have assessed the advantages of deploying and maintaining storage units such as [10]-[18].One main challenge pertaining to introducing energy storage units within the smart grid is the analysis of the energy trading decision making processes involving complex interactions be-

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Section: Phase 2 -Market and Tradingmentioning

confidence: 99%

Section: Phase 2 -Market and Tradingmentioning

confidence: 99%

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A Game-Theoretic Approach to Energy Trading in the Smart Grid

Wang

Saad

Han

et al. 2014

IEEE Trans. Smart Grid

374

213

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“…Saad et al, (2011) [4] analyzed the cooperative ties between the micro grids using the game theory. Friedman et al, (1993) [5] and Weaver and Krein (2009) [6] analyzed the relationships in the power exchange market where the double auctions had taken place. Using the game theory, Kasbekar and Sarkar (2012) [7] analyzed the pricing models in a competitive power exchange market where several independent micro grids experience power shortage or surplus, so that one has a chance to sell its surplus to the other.…”

Section: Research Trendmentioning

confidence: 99%

Hybrid Advanced Metering Infrastructure Design for Micro Grid Using the Game Theory Model

Seo¹

2015

IJSEIA

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“…As example, in [32] a cooperative game theory method is proposed to deal with the sellerto-buyer matching in microgrids perspective. Also to be used in microgrids, in [40] a noncooperative game theory method is proposed to control the energy resources. The approach intents to determine the equilibrium between the generation and the consumption.…”

Section: Background and Aimmentioning

confidence: 99%

Coalition of distributed generation units to Virtual Power Players - a game theory approach

Morais

Sousa

Santos

et al. 2015

ICA

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Abstract. Smart Grids (SGs) have emerged as the new paradigm for power system operation and management, being designed to include large amounts of distributed energy resources. This new paradigm requires new Energy Resource Management (ERM) methodologies considering different operation strategies and the existence of new management players such as several types of aggregators. This paper proposes a methodology to facilitate the coalition between distributed generation units originating Virtual Power Players (VPP) considering a game theory approach. The proposed approach consists in the analysis of the classifications that were attributed by each VPP to the distributed generation units, as well as in the analysis of the previous established contracts by each player. The proposed classification model is based in fourteen parameters including technical, economical and behavioural ones. Depending of the VPP strategies, size and goals, each parameter has different importance. VPP can also manage other type of energy resources, like storage units, electric vehicles, demand response programs or even parts of the MV and LV distribution network. A case study with twelve VPPs with different characteristics and one hundred and fifty real distributed generation units is included in the paper.

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Game-Theoretic Control of Small-Scale Power Systems

Cited by 123 publications

References 17 publications

A Game-Theoretic Approach to Energy Trading in the Smart Grid

A Game-Theoretic Approach to Energy Trading in the Smart Grid

Hybrid Advanced Metering Infrastructure Design for Micro Grid Using the Game Theory Model

Coalition of distributed generation units to Virtual Power Players - a game theory approach

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