Cooperative trading of a price-maker wind power producer: A data-driven
approach considering uncertainty
Abstract
This paper presents a novel framework for cooperative trading in a
price-maker wind power producer, that participates in the short-term
electricity balance markets. In this framework, market price uncertainty
is first modeled using a price uncertainty predictor, consisting of
ridge regression (RR), nonpooling convolutional neural network (NPCNN),
and linear quantile regression (LQR). RR is employed to select the
correlated features to the corresponding forecast day, NPCNN is employed
to extract the nonlinear features, and LQR is employed to estimate the
price uncertainty. Then, an improved firefly algorithm (IFA) is proposed
to solve the optimization problem. IFA uses the adaptive moment
estimation method to improve the convergence speed and search for the
global solution. Finally, the Shapley value is employed for the profit
distribution of cooperative power producers. Illustrative examples show
the effectiveness of the proposed framework and optimization model