Wind Power Trading Under Uncertainty in LMP Markets

Abstract

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.

Use of wind power forecasting in operational decisions.

“…Overall, more testing on real-world wind data would contribute to validate the methodology and document the advantages of the proposed modeling framework. We are addressing some of these issues in our recent work, which is documented in [102].…”

“…We have conducted a more detailed case study based on hypothetical wind farm data, as documented in the next section. We have also applied the model to a case study of a real-world wind farm [102]. In all three objective functions, the sample profit, , is given by (7-1).…”

“…Hence, it is not important to have synchronized time series of prices and wind power in this case study. In current work, we are testing the model on data from a real wind farm, and in this case we do use synchronized wind and price data [102].…”

Optimal Bidding Strategies for Wind Power Producers in the Day-ahead Electricity Market

“…Optimal trading strategies for wind generation that consider the risk to power producers have been extensively researched [10][11][12][13][14][15][16][17][18][19][20]. In [10], different risk management approaches for wind trading in the electricity market were discussed, and the utility function method was considered to be more effective than the mean variance model.…”

“…Xue et al [11] defined the power producer's attitude towards risk as the membership function based on the fuzzy set theory, and subsequently proposed a multi-objective optimal bidding strategy; this fuzzy optimization method was extended to coordinated trading of wind generators and an energy storage device (ESD) in [12], which took into account the risk quantified by computing expected energy not served (EENS). Bidding strategies based on utility and the conditional value at risk (CVaR) were derived to study the optimal bidding strategies for WPPs in [13,14]. Reference [15] developed optimal bidding strategy including various trading floors based on the scenario method, and α-CVaR was used to calculate the expected profit of the (1 − α)100% scenarios with lowest profit similar to that reported in [16].…”