Data Trading for Renewable Energy Producers in Electricity Markets

Due to the unpredictable nature of renewable energy, renewable energy producers (REPs) must carefully determine their renewable offerings in the day-ahead market. Access to additional data can significantly reduce forecasting errors, thereby improving REPs' profits in electricity markets. This paper proposes a bi-level Stackelberg game between the data aggregator and the REP to study the impact of data trading on the REP's bidding strategy and profits. At the upper level, the data aggregator sets the data prices for different datasets to maximize profit. At the lower level, the REP aims to maximize his profit by making data purchasing and renewable bidding decisions. This model the REP's decision-making under renewable uncertainty using chance constraints. To solve the bi-level optimization problem, we convert the chance constraints into a deterministic form, thereby deriving an equivalent single-level optimization problem. Numerical results show that the REP can achieve 7% more profit when participating in the data market compared to not engaging in data trading under high uncertainty cases. Furthermore, the results demonstrate that the REP benefits more from data trading under higher renewable uncertainty, higher confidence levels, and higher market prices.