基于Informer-MATD3的风力发电商现货价格预测-日前竞价两阶段决策模型OA

[Objective]To address the challenges of high electricity price volatility and forecasting difficulties caused by the high penetration of wind power in the new power system,this paper proposes a dynamic bidding strategy suitable for wind power producers.[Methods]The study first constructs a Market-Informer forecasting model that integrates multiple market factors.By introducing key variables such as carbon prices,green electricity certificate prices,and coal prices,it achieves day-ahead electricity price forecasting.Furthermore,the forecast information is embedded into a bidding decision framework designed based on the multi-agent twin delayed deep deterministic policy gradient(MATD3)algorithm.This framework is centrally trained in a market environment that includes hydropower,thermal power,and photovoltaic power producers,which finally contributes to the generation of optimal bidding strategies for wind power producers.[Results]Using data from a European electricity market in 2022 as a case study for bidding,the results show that the directional accuracy coefficient(DAC)of forecasting can reach 94.3%under a 10%error margin,representing an 18.6 percentage-point improvement over the traditional autoregressive integrated moving average(ARIMA)model.This strategy reduces the total system cost by 11.4%,increases the revenue of wind power producers by 9.8%,raises the bid-winning rate by 18.7%,and decreases revenue volatility by 22.3%.[Conclusions]The case study verifies the effectiveness of the"forecasting-decision"dynamic coupling mechanism in enhancing the bidding capacity of renewable energy and promoting low-carbon transition,providing an intelligent decision-making paradigm for power markets with a high penetration of renewable energy.