In the dynamic world of energy transmission, the integration of wind power has introduced a new layer of complexity. The variability of wind power output and load demand poses significant challenges to the traditional planning models of transmission networks. However, a groundbreaking study led by Juanjuan Wang of the State Grid Weifang Power Supply Company in Weifang, China, is set to revolutionize how we approach this issue. The research, published in ‘Zhongguo dianli’ (China Electric Power), introduces a novel two-layer model for multi-objective flexible planning of transmission networks, considering the uncertainties of wind power and load.
The study addresses a critical gap in current transmission network planning models, which often rely on Direct Current (DC) power flows. This simplification, while useful for basic planning, can lead to inaccuracies. Wang’s approach, however, leverages Alternating Current (AC) power flow, providing a more precise and reliable framework. “By incorporating AC power flow, we can better capture the complexities and dynamics of modern power grids,” Wang explains. This enhanced accuracy is crucial for optimizing construction investments, reducing network losses, and improving operational efficiency.
The two-layer model developed by Wang and her team is a masterclass in multi-objective optimization. The upper layer focuses on the annual cost of construction investment, annual network loss, and operating efficiency. This layer employs a non-dominated sorting genetic algorithm-II to find the Pareto optimal planning schemes, which are then passed to the lower layer. The lower layer acts as a stress test, evaluating the endurance of these schemes against the uncertainties of wind power and load. “The lower-level model ensures that our planning schemes are robust and resilient,” Wang notes. “It helps us understand how well our plans can handle real-world variability.”
One of the standout features of this research is the transformation of the nonlinear nonconvex planning model into a second-order cone planning model with a convex feasible region. This transformation, achieved through second-order cone relaxation, makes the model more tractable and solvable using the Cplex solver. This advancement is a game-changer for the energy sector, as it allows for more efficient and effective planning of transmission networks.
The implications of this research are far-reaching. As the energy sector continues to integrate more renewable sources like wind power, the need for flexible and accurate planning models becomes paramount. Wang’s work provides a roadmap for future developments, offering a framework that can be adapted and scaled for various grid configurations and renewable energy sources. This could lead to more stable and efficient power grids, reducing costs and enhancing reliability for consumers.
The study’s effectiveness was validated using an adjusted 39-node system, demonstrating its practical applicability. As the energy sector evolves, the insights from Wang’s research will undoubtedly shape future developments, driving innovation and efficiency in transmission network planning.
