In the dynamic world of energy, the integration of renewable sources like wind power into the grid presents both opportunities and challenges. Traditional methods of coordinating transmission networks and energy storage often fall short, leading to conservative planning and overlooked errors in predicting wind farm outputs. Enter Qiang Li, a researcher from the Economic and Technological Research Institute at State Grid Shanxi Electric Power Company, who has developed a groundbreaking approach to address these issues.
Li’s innovative method, published in ‘Zhongguo dianli’ (Chinese Power System Technology), focuses on creating a more accurate and efficient way to plan transmission networks and energy storage systems. “Traditional planning often overlooks the probability errors in clustering new energy output scenarios,” Li explains. “This leads to suboptimal results and higher costs.” By generating a typical scenario set that clusters load demand and wind farm output under annual cycles, Li’s approach considers the error in scenario probability, establishing an uncertain set of scenario probability distribution based on 1-norm and ∞ norm.
The heart of Li’s research lies in a multi-stage coordinated distributed robust programming model. This model takes into account various costs, including transmission line investment, energy storage investment, operation costs, and carbon trading costs. “Our model aims to optimize these costs comprehensively,” Li states, highlighting the economic benefits for the energy sector. The model is solved using the column and constraint generation (C&CG) algorithm, which transforms the complex problem into an iterative solution of main and sub-problems.
The practical implications of Li’s work are significant. By improving the coordination between transmission networks and energy storage, utilities can reduce costs and enhance the reliability of the grid. This is particularly relevant in the context of carbon trading, where accurate planning can minimize the financial impact of carbon emissions. The research was validated on an improved IEEE-30 node system, demonstrating its effectiveness in real-world scenarios.
Li’s findings could reshape the future of energy planning. As the energy sector continues to evolve, with a growing emphasis on renewable sources and carbon reduction, methods like Li’s will be crucial. They offer a more precise and cost-effective way to integrate new energy sources into the grid, paving the way for a more sustainable and efficient energy future. The research, published in Zhongguo dianli, provides a robust framework for future developments in transmission network and energy storage coordination, setting a new standard for the industry.
