Transition Mechanism Design for Wind Power Participating in Energy Markets

[Objective] This study addresses the challenges faced by wind farms， exhibiting varying construction costs， in their participation in electricity markets. It seeks to facilitate a seamless transition from a fixed-price procurement model to a competitive spot market model for wind power while accounting for the inherent uncertainty of wind power generation. Consequently， designing a robust transition mechanism is essential for wind power participation in electricity markets. [Methods] A medium- and long-term contract mechanism is proposed to adjust wind farm revenue by modifying contract coverage. A bi-level programming is employed to model participation in both the medium- and long-term and spot markets. The upper-level problem is to determine the optimal medium- and long-term contract coverage of wind power， with the objective of minimizing government subsidy costs and improving the fairness index of unit generation profits across all types of wind farms. The lower-level problem is framed as a joint clearing model for energy and reserve markets， accounting for the uncertainty of wind power output， which is represented by typical scenarios. The bi-level model is transformed into a single-level optimization model using the Karush-Kuhn-Tucker condition substitution and the big M method. Additionally， the corresponding linearization methods are proposed to handle the product terms involving price， continuous variables， and absolute value terms in the upper objective， eventually transforming the single-level optimization model into a mixed-integer linear programming model. To enhance computational efficiency， the check-add method is applied to address the capacity constraints of the transmission lines when solving the bi-level programming model. A simulation analysis of a real 44-unit， 1560-bus system containing two wind farms was conducted to validate the effectiveness of the proposed method. [Results] The contract hedging effect enables the wind farm to mitigate the risk of spot price fluctuations. Within the decision cycle， the subsidy amount is reduced by 1.34 million yuan， and the unit profit gap narrows by 0.015 yuan/kWh. Additionally， decision-makers can effectively adjust the policy impact by tuning the weighting factors. [Conclusions] The results indicate that By determining the optimal contract coverage ratio， the proposed approach effectively reduced government subsidy costs and narrowed the per-unit profit gap between wind farms， achieving a smooth transition to market participation.