Game-theory-based generation maintenance scheduling in electricity markets

Min, Chang-Gi; Kim, M.K.; Park, J.K.; Yoon, Yong-Tae

doi:10.1016/j.energy.2013.03.060

Cited by 43 publications

(22 citation statements)

References 18 publications

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“…EP corresponds to the presumed strategy of participant A's opponent. For instance, 32  column in 1 A EP is A's type 1 payoff when B uses strategy 3 against A's type 1 and strategy 2 against A's type 2. The same notation applies to n B EP .…”

Section: Expected Values Of the Payoffsmentioning

confidence: 99%

Non-cooperative game-theoretic model of demand response aggregator competition for selling stored energy in storage devices

2017

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Section: Expected Values Of the Payoffsmentioning

confidence: 99%

Non-cooperative game-theoretic model of demand response aggregator competition for selling stored energy in storage devices

2017

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“…A bilevel model of GenCo that comprised several virtual power plants has been provided in [15] to maximize the profit and minimize the emitted emission at the same time. A game-theoretic-based framework of GenCos and independent system operator has been developed in [16] N to analyze the strategic behavior of GenCo. A GenCo model that comprised combined heat and power units and thermal units has been analyzed in [17] to maximize the profit and minimize the emitted emission.…”

Section: A Literature Reviewmentioning

confidence: 99%

“…Eqs. (15) and (16) are provided to limit the positive and negative energy deviations. It should be noted that the positive energy deviation is limited by the produced power by thermal units, wind farms, and photovoltaic systems.…”

Section: B Thermal Units' Constraintsmentioning

confidence: 99%

Offering Decision of Risk-Based Wind-Photovoltaic-Thermal GenCo Using Downside Risk Constraints Approach

et al. 2020

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“…Both non-cooperative [60] and cooperative [61] game theoretical models were used to clear deregulated electricity markets or were used to model the operation of microgrids [62]. Min et al [63] defined the generators' strategies by Nash equilibria. Wu et al [64] discussed coalition formation and profit allocation of RES generators within a distributed energy network comprised of controllable demand.…”

Section: Review Of Game Theory and Artificial Intelligence Techniquesmentioning

confidence: 99%

Game-theoretic modeling of curtailment rules and network investments with distributed generation

et al. 2017

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Renewable energy has achieved high penetration rates in many areas, leading to curtailment, especially if existing network infrastructure is insufficient and energy generated cannot be exported. In this context, Distribution Network Operators (DNOs) face a significant knowledge gap about how to implement curtailment rules that achieve desired operational objectives, but at the same time minimise disruption and economic losses for renewable generators. In this work, we study the properties of several curtailment rules widely used in UK renewable energy projects, and their effect on the viability of renewable generation investment. Moreover, we propose a new curtailment rule which guarantees fair allocation of curtailment amongst all generators with minimal disruption. Another key knowledge gap faced by DNOs is how to incentivise private network upgrades, especially in settings where several generators can use the same line against the payment of a transmission fee. In this work, we provide a solution to this problem by using tools from algorithmic game theory. Specifically, this setting can be modelled as a Stackelberg game between the private transmission line investor and local renewable generators, who are required to pay a transmission fee to access the line. We provide a method for computing the equilibrium of this game, using a model that captures the stochastic nature of renewable energy generation and demand. Finally, we use the practical setting of a grid reinforcement project from the UK and a large dataset of wind speed measurements and demand to validate our model. We show that charging a transmission fee as a proportion of the feed-in tariff price between 15%-75% would allow both investors to implement their projects and achieve desirable distribution of the profit. Overall, our results show how using gametheoretic tools can help network operators to bridge the knowledge gap about setting the optimal curtailment rule and determining transmission charges for private network infrastructure. $ This paper builds on significant extensions, both in theoretical results and new datasets, of preliminary work presented at two international conferences: AAMAS 2016 [1] and IEEE ISGT Europe 2016 [2].it might have negative effects on the operation, resilience and safety of the electricity grid. RES are intermittent and have variable power outputs due to constantly changing primary resources and weather patterns, which are difficult to predict. The challenges faced by network operators relate to reverse power flows, increased power losses, harmonics, voltage fluctuations, thermal capacity of equipment, frequency and voltage regulation and can compromise the system reliability [5].An additional barrier is that grid infrastructure is inadequate to support continuous RES development or distributed generation (DG), especially in the area of distribution networks. Often high investment takes place in remote areas of the grid, where projects face favourable resource conditions and planning approval. Typically, in the UK, ...

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Game-theory-based generation maintenance scheduling in electricity markets

Cited by 43 publications

References 18 publications

Non-cooperative game-theoretic model of demand response aggregator competition for selling stored energy in storage devices

Non-cooperative game-theoretic model of demand response aggregator competition for selling stored energy in storage devices

Offering Decision of Risk-Based Wind-Photovoltaic-Thermal GenCo Using Downside Risk Constraints Approach

Game-theoretic modeling of curtailment rules and network investments with distributed generation

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