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