In the rapidly evolving energy sector, the integration of distributed generation (DG) sources like solar and wind power has brought significant economic and environmental benefits. However, the high intermittency of these sources poses challenges to the operation of distribution networks, including reverse power flow, voltage violations, and increased power losses. Enter distributed energy storage (DES), a technology that can decouple generation and consumption, storing excess energy during off-peak hours and discharging it during peak demand. But where and how to place these storage systems optimally? This is the crux of the research led by Ruihua Si from the State Grid Henan Economic Research Institute in Zhengzhou, China.
The traditional approach to DES placement often relies on balanced network models and extreme scenarios, which do not accurately reflect the complexities of real-world, unbalanced distribution networks. Si’s research, published in Energies, addresses these challenges head-on. “Existing DES placement studies are commonly based on a balanced network model, whereas practical distribution networks are unbalanced,” Si explains. “In addition, existing DES placement studies are mostly based on an extreme scenario and rarely consider the operational complexity resulting from the uncertainties of DGs and loads.”
Si’s team proposes a novel hierarchical and sequential DES placement strategy that considers multiple operational scenarios. The framework includes three layers: the outer layer, which uses a multi-scenario comprehensive loss sensitivity index (MSCLSI) to identify the most effective DES placement location; the inter layer, which determines the sizing of DES; and the inner layer, which schedules the DES operation. This approach is solved using a hybrid method combining particle swarm optimization and second-order cone programming (PSO-SOCP).
The significance of this research lies in its practical applicability. By considering multiple scenarios and the inherent unbalance of distribution networks, Si’s method provides a more realistic and effective DES placement strategy. “The results of DES placement based on the MSCLSI are 36.47% lower in terms of expected gain compared to the results based on the CLSI,” Si notes, highlighting the superiority of their approach. This means more efficient use of DES, leading to cost savings and improved grid stability.
The commercial implications are substantial. As the energy sector continues to integrate more renewable sources, the need for effective energy storage solutions becomes paramount. Si’s research offers a roadmap for utilities and grid operators to optimize DES placement, reducing operational costs and enhancing grid reliability. This could lead to more widespread adoption of DES, accelerating the transition to a more sustainable and resilient energy infrastructure.
Looking ahead, this research sets the stage for future developments in the field. As Si points out, “Based on the environment of multi-system development, considering the characteristics of DES with a small capacity, flexible layout and dispersion, the market mechanism suitable for DES participation can be specified in the future, and the way in which DES can coordinate with other adjustable flexible resources can be expanded.” This opens up exciting possibilities for integrating DES with other flexible resources, creating a more dynamic and responsive energy ecosystem.
