IEEE TRANSACTIONS ON POWER SYSTEMS 1 Cooperative trading of a price-maker wind power producer: A data-driven approach considering uncertainty Rongquan Zhang, Saddam Aziz, Member, IEEE, Sadiq Ahmad, Member, IEEE Rizwan Qureshi, Member, IEEE, Gangqiang Li Senior Member, IEEE, and Siqi Bu, Senior Member, IEEE Abstract—This paper presents a novel framework for coop- erative 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. Index Terms—wind power; uncertainty; cooperative; convolu- tional neural network; improved firefly algorithm I. I NTRODUCTION I N recent years, sustainable energy projects, such as wind power gained popularity, as a way to minimise air pollution and global climate change [1]. However, the uncertainty and the limited predictive capability of wind power production may cause significant challenges to the power market. The latest data from China’s National Energy Administration indicates that the installed number of wind power in northwest China, such as Gansu province, accounts for more than 20% of the total power capacity with a steady growing rate [2]. As a result of this development, electricity prices have risen sharply, as the costs of production deviations, from the generation dispatching plan [3]. Facing this circumstances, wind power producers are requested by market operators to participate in the power market as a price-maker (this is, bidding price and quantity) [4]. As a result, understanding how to create fair bidding behaviour and maximise profitability is critical, for a wind power producer. There are several possible ways of improving the economic profits of the wind power producer. The first is based on Rongquan Zhang, Saddam Aziz, and Gangqiang Li are with the Schenzhen University, Schenzhen, Guangdong China, Sadiq Ahmad is with the department of electrical engineering, COMSATS University, Islamabad, Pakistan (e-mail: engrsadiqahmad@gmail.com). Rizwan Qureshi is with the school of computing National Univer- sity of Computer and Emerging Sciences, Karachi, Pakistan (e-mail: engr.rizwanqureshi786@gmail.com Dr. Siqi Bu is with the Polytechnic University Hong Kong, Hong Kong data-driven approaches to reduce the market uncertainties, i.e., wind power generation (WPG) and electricity price (EP). Data- driven approaches can broadly split into statistical models, machine learning models, and deep learning models. Many efforts are made in the past for devising optimal bidding strategies [5], [6] In [7], Li et al. analyzed the trading model for a wind power producer considering uncertainties of WEG and EP by adopting four different statistical models. It showed that advanced forecasting methods not only reduce penalty costs but also improve the expected profits. However, the statistical models reflect the dynamic trend by linear curve fitting, thus maybe not suitable for forecasting wind power generation and electricity price in high-dimension [8]. In [9], Catalao et al. used a hybrid intelligent approach con- sisting of the wavelet transform, particle swarm optimization, and adaptive-network-based fuzzy inference system (machine learning models) to generate the scenarios for electricity prices and wind power production, and then proposed a two- stage stochastic-programming-based bidding model for the wind power producer. The drawback is that, machine learning models are prone to over-fitting and gradient vanishing as the volume and dimension of data increases [10]. Deep learning, perhaps the most powerful forecasting tech- nology, is widely used nowadays, due to the large datasets and computational power available today. In [11], Zhang et al. proposed a hybrid deep-learning framework to predict day-ahead electricity prices. In [12], Wang et al. exploited a deep-learning-based ensemble approach for probabilistic wind power forecasting. The experimental results showed [11], [12] superior forecasting performance of deep learning models, compared to statistical models and machine learning models. In [13], a stochastic-based decision-making framework for wind power producers and demand response aggregators was modeled for participating in the day-ahead market bidding. The results showed that the cooperative bidding of WES and demand response (DR) aggregators could improve its expected profits, and alleviate the uncertainty risk for wind power production. A survey of optimization approaches in smart grid is presented in [14], and communication technologies in [15]. In [16], a stochastic bidding model for cooperative operation between wind power producer and energy storage was developed to maximize their profits. To the authors’ knowledge, cooperative transaction among power producers in previous studies has received a little attention. In [17], an optimization model for a price-maker wind