In a significant stride toward optimizing power grid planning, researchers have developed a novel model that could revolutionize how energy storage and distributed generation are integrated into distribution networks. The study, led by Bo Jia of the Information & Communication Company at State Grid Ningxia Electric Power Co., Ltd., introduces a nonlinear constraint assessment model based on generalized Benders decomposition. This model aims to minimize the present value of total cost while considering various economic factors and operational constraints.
The research, published in the journal *Sustainable Energy Research*, addresses a critical challenge in modern energy systems: efficiently assessing the impact of energy storage and distributed generation on power grids. “Our model constructs nonlinear constraints for storage power, capacity, and SVG construction, incorporating costs related to grid construction, power generation, maintenance, and dismantling,” explains Jia. This comprehensive approach ensures that the model accounts for the economic implications of integrating new energy technologies into existing infrastructure.
One of the standout features of this research is the use of generalized Benders decomposition to break down the complex problem into manageable sub-problems. The main problem is solved iteratively through linear evaluation, while the sub-problems are tackled using the interior point method. This dual approach not only enhances computational efficiency but also ensures that the solution converges to the optimal value quickly. “The model and algorithm proved effective in terms of planning cost and computational efficiency,” Jia notes, highlighting the practical benefits of the research.
The study’s experimental results are particularly compelling. Using a 24-node distribution network as a test case, the researchers found that incorporating energy storage slightly increased construction costs but significantly reduced operational costs. Overall, the total operation cost of the grid was reduced by more than 10%. Moreover, the computation process converged to the optimal value after just six iterations, taking only 2.4 seconds. These findings underscore the potential of the model to provide more scientific and efficient decision-making support for grid planning.
The implications of this research extend beyond immediate cost savings. As the energy sector continues to evolve, the integration of renewable energy sources and energy storage systems will become increasingly important. This model offers a robust framework for assessing the feasibility and economic viability of such integrations, ultimately shaping the future of power grid planning. “This research provides a more scientific and efficient decision-making support tool for grid planning,” Jia concludes, emphasizing the broader impact of the work.
As the energy sector grapples with the challenges of decarbonization and digitalization, innovative models like this one will be crucial in driving progress. By offering a more precise and efficient way to evaluate the integration of new technologies, this research paves the way for a more sustainable and cost-effective energy future.