In the ever-evolving landscape of energy markets, integrating renewable sources like wind power presents both opportunities and challenges. A recent study led by Wenjing Zhu from the School of Electric Power Engineering at South China University of Technology, and affiliated with the Guangdong Key Laboratory of Clean Energy Technology, offers a novel approach to address these challenges. The research, published in the International Journal of Electrical Power & Energy Systems, focuses on designing a transition mechanism for wind farms to participate in electricity markets, aiming to minimize subsidies and revenue disparities.
The study introduces a single leader multi-follower bi-level programming model. This model employs multi-objective optimization to determine the medium- and long-term contract coverages for both subsidized and unsubsidized wind farms. “Our goal was to minimize subsidy expenditures and revenue disparity,” explains Zhu. The upper-level model focuses on these objectives, while the lower-level model jointly clears day-ahead and real-time energy and reserve markets, considering the uncertainty of wind farm outputs via stochastic optimization to maximize social welfare.
One of the key innovations in this research is the transformation of the bi-level model into a multi-objective mixed-integer nonlinear programming model using the Karush-Kuhn-Tucker condition and the big M method. The study also proposes linearization strategies to handle complex terms in the upper-level objective. “We adopted the adaptive weighted-sum algorithm to obtain uniformly distributed Pareto optimal solutions,” Zhu notes. This approach reduces the maximum-minimum solution distance difference by 42.356%, average distance by 5.2%, and standard deviation by 3.499%.
The simulations conducted on a 44-unit 1560-bus system under two forms of medium- and long-term trading demonstrate the effectiveness of the proposed method. The Pareto solution closest to the utopia point reduces subsidies by 66.67% and profit disparity by 55.82%. These findings suggest that by optimizing contract coverage, government subsidies can be significantly reduced, and the unit profit disparities among wind farms built in different periods can be minimized, facilitating a smooth policy transition.
The implications of this research are profound for the energy sector. As wind power continues to grow as a significant component of the energy mix, ensuring fair market participation and minimizing subsidies are critical for sustainable development. The adaptive weighted-sum algorithm and the bi-level programming model offer a robust framework for achieving these goals. “Our method provides a practical tool for policymakers and market operators to design more efficient and equitable transition mechanisms,” Zhu concludes.
This study not only advances the scientific understanding of electricity market dynamics but also offers actionable insights for commercial impacts. By reducing subsidies and minimizing revenue disparities, the proposed mechanism can enhance the financial viability of wind farms, attract more investments, and ultimately accelerate the transition to a cleaner energy future. As the energy sector continues to evolve, such innovative approaches will be crucial in shaping a more sustainable and equitable energy landscape.