Declared strategy of risk-constrained wind power participating in the power markets considering multiple uncertainties

Abstract

The large-scale integration of wind power has significantly increased the demand for frequency regulation in power grids, making the declaration strategy of wind power producers increasingly crucial. However, existing wind power declaration strategy often lack practicality, as they typically fail to fully account for uncertainties in wind power output, electricity price fluctuations, and revenue risks. To address this issue, this paper proposes a multi-market bidding strategy for wind power that incorporates interval probabilistic forecasting and risk-coordinated constraints to account for multiple uncertainties in wind power generation. First, a cooperative trading mechanism for wind farms in the electricity market is established. Then, a risk-constrained wind power joint declaration decision model (RCWP) is developed. The model employs Interval Stochastic Constrained Optimization-Long Short-Term Memory (ISCO-LSTM) to address uncertainties in market prices and wind power output. Additionally, the LOF-Interpolation-Joint Adaptive Noise Reduction and Reconstruction (LOFI-JANRR) method is integrated to enhance the quality of input data used for forecasting. Finally, the model is solved using an Enhanced Population-Based Beluga Whale Optimization (EPBWO) algorithm. A comprehensive evaluation of real-world case studies demonstrates that the proposed RCWP model enhances wind farm revenues while effectively mitigating the probability of low returns under extreme scenarios. Moreover, compared to other algorithms, the proposed approach exhibits superior optimal performance and forecasting accuracy.