Nodal, Uniform, or Zonal Pricing: Distribution of Economic Surplus

Feng, Donglai; Fuller, J. David

doi:10.1109/tpwrs.2005.846042

Cited by 81 publications

(62 citation statements)

References 15 publications

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“…The main differences between nodal, uniform, and zonal pricing are shown in [4]. On the other hand, the integration of renewable energies in electricity markets is described in [5].…”

Section: A Literature Reviewmentioning

confidence: 99%

Optimal Wind Reversible Hydro Offering Strategies for Midterm Planning

Nieta

Contreras

Muñoz

et al. 2015

IEEE Trans. Sustain. Energy

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A coordinated strategy between wind and reversible hydro units for the midterm planning that reduces the imbalance of wind power and improves system efficiency is proposed. A stochastic mixed integer linear model is used, which maximizes the joint profit of wind and hydro units, where conditional value at risk (CVaR) is used for model risk. The offering strategies studied are 1) separate wind and hydro pumping offer, where the units work separately without a physical connection and 2) a single wind and hydro pumping offer with a physical connection between them to store wind energy for future use. The effects of a coordinated wind-hydro strategy for midterm planning are analyzed, considering CVaR and the future water value. The future water value in the reservoirs is analyzed hourly for a period of 1 week and 2 months, in two realistic case studies.Index Terms-Midterm planning, offering strategies, reversible hydro unit, wind power.

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“…The main differences between nodal, uniform, and zonal pricing are shown in [4]. On the other hand, the integration of renewable energies in electricity markets is described in [5].…”

Section: A Literature Reviewmentioning

confidence: 99%

Optimal Wind Reversible Hydro Offering Strategies for Midterm Planning

Nieta

Contreras

Muñoz

et al. 2015

IEEE Trans. Sustain. Energy

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show abstract

“…Considering market regulations, Singh et al (1998) compare a nodal pricing framework with cost-allocation procedures in the case of competitive electricity markets, and analyze some game-theoretic aspects of the proposed model. The paper of Ding and Fuller (2005) considers nodal, zonal and uniform marginal prices and emphasizes that the nodal marginal price correctly accounts for transmission constraints and losses in some cases.…”

Section: Introductionmentioning

confidence: 99%

Competition and Cooperation in a Bidding Model of Electrical Energy Trade

Csercsik

2015

Netw Spat Econ

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A cooperative game-theoretic framework is introduced to study the behavior of cooperating and competing electrical-energy providers in the wholesale market considering price-preference rational consumers. We study the physical and economic aspects of the power transmission system operation focussing on the incentives for group formation. We analyze the interactions of generators in an idealized environment described by a DC load flow model where the network is lossless and is operated by an independent network operator who ensures network stability and fulfillment of consumption needs while taking into account the preferences of consumers over generators. We show that cooperation of generators may be necessary to divert consumers from their previous providers. In the second part of the paper we assume an iterative process in which the generators publish their price offers simultaneously, based on which the consumers preferences are determined. We study the dynamics of the prices and profits as the system evolves in time while each coalition is trying to maximize its expected profit in each step. The model deals with network congestion and n − 1 line-contingency reliability as not every generator-consumer matching is allowed to ensure the safe operation of the transmission system. The profit of the generators is determined as the dif- ference between their income and their production cost, which is a quadratic concave function of the production amount.Any non-monopolistic proper subset of the generators may cooperate and harmonize their offered prices to increase their resulting profit. Since we allow the redistribution of profits among cooperating generators, a transferableutility game-theoretic framework is used. Furthermore, as cooperation affects the outsiders as well, the resulting game is defined in partition function form. The model is able to demonstrate some interesting benefits of cooperation as well as the effect of market regulations and asymmetric information on the resulting profits and total social cost.

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“…Ding and Fuller (2005) analyze the economic effects of different pricing and congestion management regimes on the Italian electricity system. Their research shows that generation costs are identical among the investigated regimes, but the distribution of benefits and costs among market participants differs.…”

Section: Introductionmentioning

confidence: 99%

The Benefit of Coordinating Congestion Management in Germany

Kunz

Zerrahn

2013

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. The management of congestion within the German electricity transmission network has become more important during the last years. This emerging relevance is caused by the increase of renewable generation and the partial phaseout of nuclear power plants. Both developments yield a change in the transmission flow pattern and thus the need for congestion management. Currently, four German transmission system operators (TSOs) are in charge of managing congestion using curative methods, particularly re-dispatch of power plants. However, the existence of four TSOs within Germany induces the question whether coordination between them in managing national congestion would be beneficial. To address this issue, we apply a generalized Nash equilibrium model to analyze different degrees of coordination, covering the German electricity market with a detailed representation of the generation and network structure. Our results indicate that the costs of congestion management decrease in a rising degree of coordination as TSOs take into account congestion in other operators' zones. Total costs are highest in case each TSO is solely responsible for its own zone, and lowest if one integrated entity is in charge of mitigating congestion. We conclude that, in a setup with multiple TSOs, inducing coordination, for instance through a common market, has the potential of lowering the overall costs of congestion management. Terms of use: Documents inJEL Codes: C61, L94, Q40

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Nodal, Uniform, or Zonal Pricing: Distribution of Economic Surplus

Cited by 81 publications

References 15 publications

Optimal Wind Reversible Hydro Offering Strategies for Midterm Planning

Optimal Wind Reversible Hydro Offering Strategies for Midterm Planning

Competition and Cooperation in a Bidding Model of Electrical Energy Trade

The Benefit of Coordinating Congestion Management in Germany

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