A Stochastic Electricity Market Clearing Formulation with Consistent Pricing Properties

Zavala, Ví­ctor M.; Kim, Kibaek; Anitescu, Mihai; Birge, John R.

doi:10.1287/opre.2016.1576

Cited by 69 publications

(51 citation statements)

References 28 publications

(54 reference statements)

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“…Since their approach clears the day-ahead market in a deterministic manner, the merit order for all generators, except the intermittent generator, will be preserved. Zavala et al (2015) proves, in a model similar to that of Pritchard et al (2010), a bound for the dierence between day-ahead prices and expected real-time prices. The bound depends on the parameters describing the exibility costs, i.e., they show that day-ahead prices will converge to expected real-time prices when exibility costs are small enough.…”

Section: Introductionmentioning

confidence: 81%

Stochastic electricity dispatch: A challenge for market design

Bjørndal

Midthun

et al. 2018

Energy

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We consider an electricity market with two sequential market clearings, for instance representing a day-ahead and a real-time market. When the rst market is cleared, there is uncertainty with respect to generation and/or load, while this uncertainty is resolved when the second market is cleared. We compare the outcomes of a stochastic market clearing model, i.e. a market clearing model taking into account both markets and the uncertainty, to a myopic market model where the rst market is cleared based only on given bids, and not taking into account neither the uncertainty nor the bids in the second market. While the stochastic market clearing gives a solution with a higher total social welfare, it poses several challenges for market design. The stochastic dispatch may lead to a dispatch where the prices deviate from the bid curves in the rst market. This can lead to incentives for selfscheduling, require producers to produce above marginal cost and consumers to pay above their marginal value in the rst market. Our analysis show that the wind producer has an incentive to deviate from the system optimal plan in both the myopic and stochastic model, and this incentive is particularly strong under the myopic model. We also discuss how the total social welfare of the market outcome under stochastic market clearing depends on the quality of the information that the system operator will base the market clearing on. In particular, we show that the wind producer has an incentive to misreport the probability distribution for wind.

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Section: Introductionmentioning

confidence: 81%

Stochastic electricity dispatch: A challenge for market design

Bjørndal

Midthun

et al. 2018

Energy

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“…The mathematical equivalence between CP system (23a)-(23b) and (27) can be established by constructing the Schur system of (27) and noticing that it is equivalent to (25). Moreover, the second-stage steps are the same.…”

Section: Clustering-based Preconditionermentioning

confidence: 99%

“…Because the CP system (23) and the perturbed KKT system (27) are equivalent, we can establish the following result.…”

Section: Preconditioner Propertiesmentioning

confidence: 99%

Clustering-based preconditioning for stochastic programs

2015

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We present a clustering-based preconditioning strategy for KKT systems arising in stochastic programming within an interior-point framework. The key idea is to perform adaptive clustering of scenarios (inside-the-solver) based on their influence on the problem at hand. This approach thus contrasts with existing (outside-the-solver) approaches that cluster scenarios based on problem data alone. We derive spectral and error properties for the preconditioner and demonstrate that scenario compression rates of up to 94 % can be obtained, leading to dramatic computational savings. In addition, we demonstrate that the proposed preconditioner can avoid scalability issues of Schur decomposition in problems with large first-stage dimensionality.

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“…The model is scenario-based and inspired by the studies on stochastic electricity market clearing approaches [20]- [21]. The top priorities include that the GFUs should be free from guessing their future production or bearing the risks from the gas supply, and the value of sufficient and timely gas supply for the power systems should also be reflected in the gas market.…”

Section: Introduction Ccording To the Energy Information Administrmentioning

confidence: 99%

Clearing and Pricing for Coordinated Gas and Electricity Day-Ahead Markets Considering Wind Power Uncertainty

Chen

Wang

Sun

2018

IEEE Trans. Power Syst.

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Abstract-Natural gas and electricity systems are becoming increasingly strongly coupled. Gas-fired units (GFUs) are replacing retired coal plants, and the power systems are more dependent on the flexibilities provided by GFUs. The GFUs' power generation capability relies on the availability of gas resources, which is jointly determined by the capacity of gas suppliers and pipeline networks. However, the gas and electricity markets are operated separately. Consequently, the GFUs are forced to "represent" the entire power system to bid on the gas market: they must make forecasts regarding future gas consumption and bear the risk of improper contracts or being unable to meet generation schedules due to occasional insufficient gas supply. When facing larger shares of renewable energies and more-frequent gas network congestions, the current market framework is particularly unreliable and inefficient, as well as economically unfriendly to the investors of the GFU assets. In this paper, we try to develop a framework that can combine the two markets. By properly pricing the scarce resources, e.g., gas transmission capacity, the joint market can help allocate the resources more efficiently while satisfying the demands. Moreover, a more forward-looking day-ahead market clearing framework is presented by considering the uncertainty brought by renewable energies. The formulation and algorithm of the proposed joint market model will be presented, as are some case studies.Index Terms-day-ahead market, gas and electricity coordination, wind power, unit commitment, stochastic programming. 1

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A Stochastic Electricity Market Clearing Formulation with Consistent Pricing Properties

Cited by 69 publications

References 28 publications

Stochastic electricity dispatch: A challenge for market design

Stochastic electricity dispatch: A challenge for market design

Clustering-based preconditioning for stochastic programs

Clearing and Pricing for Coordinated Gas and Electricity Day-Ahead Markets Considering Wind Power Uncertainty

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