Wind and Energy Markets: A Case Study of Texas

Baldick, Ross

doi:10.1109/jsyst.2011.2162798

Cited by 83 publications

(27 citation statements)

References 2 publications

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“…The VoWS is subject to jurisdictional variations and generally depends on tax credits and monetary incentives [26] or the loss of opportunity cost [27] of wind producers. Although the VoWS is currently used in many power systems, [28], its high value may result in an inefficient dispatch of conventional generators because it could lead to unnecessary cycling [26], [23].…”

Section: A Concept Of Hybrid Unit Commitmentmentioning

confidence: 99%

“…1 and as explained in the original IUC model in [17]. However, constraints (23) and (24) can be removed from the model, since they hold automatically because of constraints (25) and (26). Note that since the IUC formulation does not allow any load shedding, its central forecast and bounds must be served at any cost, which may result in infeasibility.…”

Section: B Mathematical Formulationmentioning

confidence: 99%

“…The SUC formulation includes constraints (2)-(16) and the IUC formulation includes constraints (2)- (9) and (17)- (26). The DUC formulation is identical to the IUC but without bounds and without constraints (23)- (26).…”

Section: A Data and Test Systemmentioning

confidence: 99%

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A Hybrid Stochastic/Interval Approach to Transmission-Constrained Unit Commitment

Dvorkin

Pandžić

Ortega‐Vazquez

et al. 2015

IEEE Trans. Power Syst.

133

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This paper proposes a new transmission-constrained unit commitment method that combines the cost-efficient but computationally demanding stochastic optimization and the expensive but tractable interval optimization techniques to manage uncertainty on the expected net load. The proposed hybrid unit commitment approach applies the stochastic formulation to the initial operating hours of the optimization horizon, during which the wind forecasts are more accurate, and then switches to the interval formulation for the remaining hours. The switching time is optimized to balance the cost of unhedged uncertainty from the stochastic unit commitment against the cost of the security premium of the interval unit commitment formulation. These hybrid, stochastic, and interval formulations are compared using Monte Carlo simulations on a modified 24-bus IEEE Reliability Test System. The results demonstrate that the proposed unit commitment formulation results in the least expensive day-ahead schedule among all formulations and can be solved in the same amount of time as a full stochastic unit commitment. However, if the range of the switching time is reduced, the hybrid formulation in the parallel computing implementation outperforms the stochastic formulation in terms of computing time.

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Section: A Concept Of Hybrid Unit Commitmentmentioning

confidence: 99%

Section: B Mathematical Formulationmentioning

confidence: 99%

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A Hybrid Stochastic/Interval Approach to Transmission-Constrained Unit Commitment

Dvorkin

Pandžić

Ortega‐Vazquez

et al. 2015

IEEE Trans. Power Syst.

133

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“…However, the rapid growth of wind power in Texas inspired work critical of subsidies that were "compensating the market for unfavorable production locations" for wind power and had resulted in negative electricity prices ( [30]; p. 30). Zarnikau ([31]; p. 3912) noted that wind-generated power cost three times more than producing electricity from natural gas, adding that wind power in the Electricity Reliability Council of Texas (ERCOT) produced "adverse impacts on system reliability."…”

Section: Study Regionmentioning

confidence: 99%

Spatial Distribution of Estimated Wind-Power Royalties in West Texas

Brannstrom¹,

Tilton²,

Klein³

et al. 2015

Land

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Wind-power development in the U.S. occurs primarily on private land, producing royalties for landowners through private contracts with wind-farm operators. Texas, the U.S. leader in wind-power production with well-documented support for wind power, has virtually all of its ~12 GW of wind capacity sited on private lands. Determining the spatial distribution of royalty payments from wind energy is a crucial first step to understanding how renewable power may alter land-based livelihoods of some landowners, and, as a result, possibly encourage land-use changes. We located ~1700 wind turbines (~2.7 GW) on 241 landholdings in Nolan and Taylor counties, Texas, a major wind-development region. We estimated total royalties to be ~$11.5 million per year, with mean annual royalty received per landowner per year of $47,879 but with significant differences among quintiles and between two sub-regions. Unequal distribution of royalties results from land-tenure patterns established before wind-power development because of a "property advantage," defined as the pre-existing land-tenure patterns that benefit the fraction of rural landowners who receive wind turbines. A "royalty paradox" describes the observation that royalties flow to a small fraction of landowners even though support for wind power exceeds 70 percent.

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“…A large presence of wind power tends to increase the occurrences of negative prices [21], [22], typically during periods with low load and high wind generation, when thermal plants are forced to operate at their minimum output. Subsidies for renewable generation, such as production tax credits and renewable energy credits, also contribute to negative prices, since wind power plants can make a profit, even if the prices drop below zero [23].…”

Section: Wind Power Trading In Lmp Marketsmentioning

confidence: 99%

Wind Power Trading Under Uncertainty in LMP Markets

Botterud

Zhou

Wang

et al. 2012

IEEE Trans. Power Syst.

173

103

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This paper presents a new model for optimal trading of wind power in day-ahead (DA) electricity markets under uncertainty in wind power and prices. The model considers settlement mechanisms in markets with locational marginal prices (LMPs), where wind power is not necessarily penalized from deviations between DA schedule and real-time (RT) dispatch. We use kernel density estimation to produce a probabilistic wind power forecast, whereas uncertainties in DA and RT prices are assumed to be Gaussian. Utility theory and conditional value at risk (CVAR) are used to represent the risk preferences of the wind power producers. The model is tested on real-world data from a large-scale wind farm in the United States. Optimal DA bids are derived under different assumptions for risk preferences and deviation penalty schemes. The results show that in the absence of a deviation penalty, the optimal bidding strategy is largely driven by price expectations. A deviation penalty brings the bid closer to the expected wind power forecast. Furthermore, the results illustrate that the proposed model can effectively control the trade-off between risk and return for wind power producers operating in volatile electricity markets.

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Wind and Energy Markets: A Case Study of Texas

Cited by 83 publications

References 2 publications

A Hybrid Stochastic/Interval Approach to Transmission-Constrained Unit Commitment

A Hybrid Stochastic/Interval Approach to Transmission-Constrained Unit Commitment

Spatial Distribution of Estimated Wind-Power Royalties in West Texas

Wind Power Trading Under Uncertainty in LMP Markets

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