A tutorial review of complementarity models for decision-making in energy markets

Ruiz, Carlos; Conejo, Antonio J.; Fuller, J. David; Gabriel, Steven A.; Hobbs, Benjamin F.

doi:10.1007/s40070-013-0019-0

Cited by 55 publications

(33 citation statements)

References 56 publications

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“…is a profit maximizing agent that solves the following optimization problem: Generalized Nash equilibrium problem (which is also named a social equilibrium problem in economics). We refer to the survey in Facchinei •and Kanzow (2010) for a comprehensive presentation of that type of problems and to Ruiz et al (2014) for an overview of applications in the context of energy markets.…”

Section: Appendix B -Oligopolistic Spatial Arbitragesmentioning

confidence: 99%

Market Power and Spatial Arbitrage between Interconnected Gas Hubs

Massol

Banal‐Estañol

2016

SSRN Journal

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This paper examines the performance of the spatial arbitrages carried out between two regional markets for wholesale natural gas linked by a pipeline system. We develop a new empirical methodology to (i) detect if these markets are integrated, i.e., if all the spatial arbitrage opportunities between the two markets are being exploited, and (ii) decompose the observed spatial price differences into factors such as transportation costs, transportation bottlenecks, and the oligopolistic behavior of the arbitrageurs. Our framework incorporates a new test for the presence of market power and it is thus able to distinguish between physical and strategic behavior constraints on marginal cost pricing. We use the case of the "Interconnector" pipeline linking Belgium and the UK as an application. Our empirical findings show that all the arbitrage opportunities between the two zones are being exploited but confirm the presence of market power.

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Section: Appendix B -Oligopolistic Spatial Arbitragesmentioning

confidence: 99%

Market Power and Spatial Arbitrage between Interconnected Gas Hubs

Massol

Banal‐Estañol

2016

SSRN Journal

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“…First and foremost, each type of economic model represented can yield a different solution for a similar problem. Ruiz et al (2014) compare different economic models (e.g., perfect competition, generalized Nash equilibrium) as applied to electricity market modeling. The optimal strategy of each firm in a market will depend on the ability of each firm to predict how its own behavior will impact market outcomes (i.e., price taker versus price maker).…”

Section: Limitations Of Competitiveness Metrics and Their Quantificatmentioning

confidence: 99%

Competitiveness Metrics for Electricity System Technologies

Mai

Mowers

Newman

2021

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“…In the context of deregulated electricity market, several authors have argued that transmission planning techniques need to adopt optimisation [23] and strategic modelling of market participants [24], as opposed to 'rules of thumb' approaches driven by human management experience, traditionally performed by utility companies in the past [25]. These techniques include mathematical optimisation techniques or game theory models.…”

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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A tutorial review of complementarity models for decision-making in energy markets

Cited by 55 publications

References 56 publications

Market Power and Spatial Arbitrage between Interconnected Gas Hubs

Market Power and Spatial Arbitrage between Interconnected Gas Hubs

Competitiveness Metrics for Electricity System Technologies

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

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