Cooperation model in the electricity energy market using bi-level optimization and Shapley value

Acuña, Luceny Guzmán; Ríos, Diana Ramírez; Paternina-Arboleda, Carlos D.; Ponzón, Esneyder González

doi:10.1016/j.orp.2018.07.003

Cited by 22 publications

(23 citation statements)

References 43 publications

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“…Several technics have been used to support the remuneration and its negotiation. In the works of [14,15], methods are shown for energy scheduling based on cooperative game theory. The authors of [14] created a model that generation companies and different marketers can cooperate in order to maximize their profits.…”

Section: Related Literaturementioning

confidence: 99%

“…In the works of [14,15], methods are shown for energy scheduling based on cooperative game theory. The authors of [14] created a model that generation companies and different marketers can cooperate in order to maximize their profits. They inference that, with bilateral contracts, these agents can establish Energies 2019, 12, 2721 4 of 23 collaboration agreements that would be more profitable, despite the existence of natural competition.…”

Section: Related Literaturementioning

confidence: 99%

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Demand Response and Distributed Generation Remuneration Approach Considering Planning and Operation Stages

Silva¹,

Faria²,

Vale

2019

Energies

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The need for new business models to replace existing ones, soon obsolete, is a subject often discussed among researchers in the area. It is essential to find a practical solution that includes the concepts of demand response and distributed generation in the energy markets, these being the future of the electricity grid. It is believed that these resources can bring advantages to the operation of the system, namely increasing technical efficiency. However, one of the problems is the aggregation of small resources as a result of the associated uncertainties. The authors propose a business model with three main phases used in planning: optimal scheduling, aggregation, and remuneration. In this paper, a new phase was added, the classification, with the main purpose of assisting the aggregator of these small resources in operating situations. The focus is on the fair remuneration of participants in the management of the market, in addition to minimizing operating costs. After testing four different remuneration methods, it was proved that the method proposed by the authors obtained better results, proving the viability of the proposed model.

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Section: Related Literaturementioning

confidence: 99%

Section: Related Literaturementioning

confidence: 99%

Demand Response and Distributed Generation Remuneration Approach Considering Planning and Operation Stages

Silva¹,

Faria²,

Vale

2019

Energies

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“…They solved this problem using a bi-level nested ant colony optimization algorithm. Acun˜a et al 19 studied a cooperation model between a generating company and several marketers. They studied two schemes in their work: The first scheme is a vertical cooperation between a generation company and several marketers, for which the bi-level optimization was used.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic p+q maximal hub location problem for freight transportation planning with rational markets

Alizadeh

Nishi

2019

Advances in Mechanical Engineering

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In this article, a dynamic maximal hub location covering problem for a freight transportation system is studied, in which the model has the possibility of having expansion scenarios for future according to the forecasts of increasing demands. Two expansion scenarios are to add up the number of hubs in the network and to add up more carriers. As the markets are involved in the pricing procedure, the model is a bi-level problem which needs more effort to deal with, for which in this work two reformulations based on Karush-Kuhn-Tucker conditions and duality theory are utilized to reformulate the bi-level problem to a single-level one. To solve the model efficiently, a decomposition method is applied and numerical examples are solved to verify the accuracy of the proposed model.

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“…Within this context, the Shapley value has been proposed as a fair mechanism for the allocation of resources and costs on such networks. The Shapley value has been considered in different ways as a mechanism for pricing demand response (O'Brien, Gamal and Rajagopal, 2015), demand or load (Chapman, Mhanna and Verbič, 2017), supply or generation (Acuña et al, 2018), and potentially all simultaneously (Burgess, Chapman and Scott, 2018). Although computing the Shapley value exactly is impractical in these contexts, sample-based approximations are a promising avenue for implementing Shapely value pricing schemes in real-world electricity systems.…”

Section: Future Work and Applicationsmentioning

confidence: 99%

Stratied Finite Empirical Bernstein Sampling

M¹,

Chapman²

2019

Preprint

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We derive a concentration inequality for the uncertainty in stratified random sampling. Minimising this inequality leads to an iterated online method for choosing samples from the strata. The inequality is versatile and considers a range of factors including: the data ranges, weights, sizes of the strata, as well as the number of samples taken, the estimated sample variances and whether strata are sampled with or without replacement. We evaluate the improvement this method reliably offers against other methods over sets of synthetic data, and also in approximating the Shapley value of cooperative games. The method is seen to be competitive with the performance of perfect Neyman sampling, even without prior information on strata variances. We supply a multidimensional extension of our inequality and discuss some future applications.MSC 2010 subject classifications: 94A20 91A12 60E15.

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Cooperation model in the electricity energy market using bi-level optimization and Shapley value

Cited by 22 publications

References 43 publications

Demand Response and Distributed Generation Remuneration Approach Considering Planning and Operation Stages

Demand Response and Distributed Generation Remuneration Approach Considering Planning and Operation Stages

Dynamic p+q maximal hub location problem for freight transportation planning with rational markets

Stratied Finite Empirical Bernstein Sampling

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Cooperation model in the electricity energy market using bi-level optimization and Shapley value

Cited by 22 publications

References 43 publications

Demand Response and Distributed Generation Remuneration Approach Considering Planning and Operation Stages

Demand Response and Distributed Generation Remuneration Approach Considering Planning and Operation Stages

Dynamic p+q maximal hub location problem for freight transportation planning with rational markets

Stratied Finite Empirical Bernstein&nbsp;Sampling

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Product

Resources

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Stratied Finite Empirical Bernstein Sampling