Assessment of the cost associated with wind generation prediction errors in a liberalized electricity market

By A. Fabbri, T. Román, J. R. Abbad, Victor M. Quezada. IEEE Transactions on Power Systems, 2005

480 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/96ff15703fb93f9b1499243cc4a5e71534abe4b1

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Citations in the corpus

Pool Strategy of a Price-Maker Wind Power Producer (2013, methodology): “As far as the second-stage variables are concerned, we employ Beta distributions to model wind power generation, as advocated in [24].”
Combined Operations of Renewable Energy Systems and Responsive Demand in a Smart Grid (2011, background): “T ODAY, the integration of large amounts of renewable energy systems (RESs) with the grid [1]–[6] is widely studied by many researchers, but only few of them address these problems in connection with a consumers’ potential participation to the electricity market [7]–[9], or analyze the additional ba”
Resilience-Oriented Critical Load Restoration Using Microgrids in Distribution Systems (2016, methodology): “• The beta function is used as the probability distribution to represent prediction errors of wind and solar power [11], and its shaping parameters are calculated with the method proposed in [29] and [30].”
A new self-scheduling strategy for integrated operation of wind and pumped-storage power plants in power markets (2011, background): “In spite of various advantages, operation of wind energy resources in the restructured power systems has a lot of problems [1–3].”
A tractable approximation of non-convex chance constrained optimization with non-Gaussian uncertainties (2015, background): “According to Fabbri et al. (2005), the generation of a wind generator can be described by a Beta distribution and the distribution parameters can be calculated from weather forecasts.”
Evaluating the Impacts of Real-Time Pricing on the Cost and Value of Wind Generation (2010, methodology): “Refere nce [1] uses a probabilistic approach to estimate the energy redispatch costs associated with day- or hour-ahead wind forecast errors, and estimates that these costs can be as hig h as 10% of a wind generator’s energy revenues.”
Intermittency and the Value of Renewable Energy (2011, unknown): “…engineering literature focuses on the variability of renewable output and allows for changes in utility operations to be based on rules of thumb, rather than reoptimizing policies in response to the renewable energy (see, e.g., Fabbri et al. 2005; Mills and Wiser 2010; Lueken, Cohen, and Apt 2012).”
Wind power forecasting : state-of-the-art 2009. (2009, background): “The same shape of error distribution can be found in [155],[156].”
A Versatile Probability Distribution Model for Wind Power Forecast Errors and Its Application in Economic Dispatch (2013, background): “The forecast error is usually assumed as a random variable that obeys the law of Gaussian [3]–[5] or Beta [6], [7] distribution.”
Increased Wind Revenue and System Security by Trading Wind Power in Energy and Regulation Reserve Markets (2011, background): “It is assumed that the wind forecast probability distribution follows the beta distribution ), with a finite support between 0 and 1 [7], [24].”
Probabilistic load flow in systems with wind generation (2009, methodology): “Due to the bounded nature of the power produced by a wind farm, a Beta distribution will be used, as in [30].”
A Statistical Model for Wind Power Forecast Error and its Application to the Estimation of Penalties in Liberalized Markets (2011, methodology): “A probabilistic methodology to estimate the costs associated with prediction error has been presented in [19].”
Decentralized Energy Management System for Networked Microgrids in Grid-Connected and Islanded Modes (2016, background): “distribution to represent prediction errors of wind and solar power [22], [28].”