【目的】为解决新型电力系统中风电高比例渗透引发的电价高波动性与预测难题，提出一种适用于风力发电商的动态竞价策略。【方法】首先，构建了多市场因素融合的Informer（Market-Informer）预测模型，通过引入碳价、绿证价格及煤炭价格等关键变量，实现日前电价预测；进一步，将预测信息嵌入基于多智能体双延迟深度确定性策略梯度（multi-agent twin delayed deep deterministic policy gradient， MATD3）算法的竞价决策框架中。该框架通过在包含水电、火电、光伏发电商的市场环境中进行集中训练，最终实现风力发电商的最优竞价策略。【结果】以欧洲某电力市场2022年数据为案例进行竞价，结果显示其预测方向准确性（directional accuracy coefficient，DAC）在10%的误差水平下可达94.3%，较传统自回归积分移动平均模型（autoregressive integrated moving average model，ARIMA）提升了18.6个百分点。该策略使系统总成本降低11.4%，风力发电商收益提升9.8%，中标率提高18.7%，收益波动降低22.3%。【结论】算例分析验证了“预测-决策”动态耦合机制在提升可再生能源竞价能力与低碳转型中的有效性，为高比例可再生能源电力市场提供了智能化决策范式。

[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.