Wind Power Trading Under Uncertainty in LMP Markets

By Audun Botterud, Zhi Zhou, Jianhui Wang, Ricardo J. Bessa, H. Keko, J. Sumaili, Vladimiro Miranda. IEEE Transactions on Power Systems, 2012

201 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/22bcdee8ea6e840bef93626a2826724e1fef3ded

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Citations in the corpus

Two-Stage Minimax Regret Robust Unit Commitment (2013, background): “, [30] and [31]), and it is indicated in [26] that managing risk through robust optimization and through stochastic programming incorporating risk measures (e.”
Risk-Constrained Profit Maximization for Microgrid Aggregators With Demand Response (2015, methodology): “The standard deviation for RT price is set to be higher than that for DA price to reflect a typical scenario in practice [39].”
Integration costs revisited – An economic framework for wind and solar variability ☆ (2015, background): “Such strategic behavior can be profitable if real-time and day-ahead markets are not arbitrage free, or if punitive mark-ups for forecast errors are imposed [6,15,80,100]; [64].”
Comprehensive review of renewable energy curtailment and avoidance: A specific example in China (2015, background): “In order to optimize wind power trading in day-ahead electricity markets under uncertainty in wind power and prices, a newmodel is presented, which can effectively control the trade-off between risk and return for wind power producers [46].”
Mean-Variance Optimization-Based Energy Storage Scheduling Considering Day-Ahead and Real-Time LMP Uncertainties (2018, methodology): “The forecast uncertainties of the DA and RT locational marginal price (LMP) on each interval follow a Gaussian distribution [10], then the proposed model is formulated as a mixed-integer conic programming model (MICP), instead of a large amount of pre-assumed LMP scenarios, which leads to efficient ”
Probabilistic Forecasts of Wind Power Generation Accounting for Geographically Dispersed Information (2014, background): “On e should also be informed about the possibilities of actual en ergy production to exceed or to be less than the expected value, hence allowing minimizing expected balancing costs [7].”
Probabilistic Forecast for Multiple Wind Farms Based on Regular Vine Copulas (2018, background): “The uncertainty information is very useful in solving a series of decision-making problems in the power system, such as trading wind power in electricity market [2], [3], designing energy storage systems(ESS) [4], setting the operating reserve [5], [6], calculating probabilistic power flows [5], opt”
On-line quantile regression in the RKHS (Reproducing Kernel Hilbert Space) for operational probabilistic forecasting of wind power (2016, background): “For electricity market agents, this information can be embed 12 in bidding optimization problems for electrical energy [7, 8] and ancillary 13 services markets [9].”
Review of photovoltaic power forecasting (2016, background): “To deepen in the understanding of such penalties, readers are referred to Jacobs (2012), Botterud et al. (2012) and Mathiesen et al. (2013).”
A Novel Probabilistic Optimal Power Flow Model With Uncertain Wind Power Generation Described by Customized Gaussian Mixture Model (2016, unknown): “In particular, the power market researches give considerable attentions to the VaR, conditional VaR and their optimization [40]–[42].”
Optimal Bidding Strategy for Microgrids Considering Renewable Energy and Building Thermal Dynamics (2014, background): “In [24]–[28], different operation frameworks for DISCOs, RETAILERs, and larger customers in the competitive electricity market have been proposed.”