In the heart of China’s energy transition, a critical challenge has emerged: integrating high levels of renewable energy into distribution networks without compromising safety or efficiency. Kewei Wang, a researcher from the School of Electrical and Information Engineering at Jiangsu University, has taken a deep dive into this issue, publishing a comprehensive review in the journal *Energies*, translated to English as “Energies”. His work sheds light on the complexities and potential solutions for optimizing the dispatch of active distribution networks (ADNs) with high penetration of renewable energy sources.
As China advances its dual-carbon goals—peaking carbon emissions before 2030 and achieving carbon neutrality by 2060—the integration of renewable energy sources like wind and solar power into ADNs is becoming increasingly crucial. However, the intermittent nature of these energy sources introduces significant uncertainty, leading to voltage fluctuations and potential safety issues. “The high uncertainty in renewable energy output leads to increased system voltage fluctuations and localized voltage violations, posing safety challenges,” Wang explains.
Wang’s review analyzes the voltage impact patterns and control principles in distribution networks under varying levels of renewable energy penetration. He introduces optimization dispatch models for ADNs that focus on three key objectives: safety, economy, and low carbon emissions. “This paper provides a comprehensive review of research in this domain over the past decade,” Wang notes, highlighting the importance of addressing these challenges to ensure the stable and efficient operation of ADNs.
One of the key challenges in optimizing ADN dispatch is solving non-convex and nonlinear models. Wang’s review highlights various model reformulation strategies, such as semidefinite relaxation, second-order cone relaxation, and convex inner approximation methods, along with summarizing relevant intelligent solution algorithms. These strategies are essential for developing effective optimization models that can handle the complexities of ADNs with high renewable energy penetration.
In addition to these technical challenges, Wang’s review also addresses the high uncertainty of renewable energy output. He explores stochastic optimization dispatch strategies for ADNs, encompassing single-stage, two-stage, and multi-stage approaches. These strategies are crucial for managing the variability of renewable energy sources and ensuring the reliable operation of ADNs.
Looking ahead, Wang outlines potential future research directions for ADN optimization dispatch. One promising area is the application of large-scale deep reinforcement learning models in the power sector. These models have the potential to revolutionize ADN optimization dispatch by enabling more accurate predictions and better decision-making.
The implications of Wang’s research are significant for the energy sector. As the integration of renewable energy sources continues to grow, the need for effective optimization dispatch strategies will become increasingly important. Wang’s review provides a valuable resource for researchers and practitioners in the field, offering insights into the latest developments and future directions in ADN optimization dispatch.
In the words of Wang, “The high-proportion integration of renewable energy sources into active distribution networks can effectively alleviate the pressure associated with advancing China’s dual-carbon goals.” His research is a testament to the ongoing efforts to achieve this goal and highlights the importance of continued innovation and collaboration in the energy sector.
As the energy sector continues to evolve, the work of researchers like Kewei Wang will be crucial in shaping the future of ADN optimization dispatch. By addressing the challenges and opportunities presented by high renewable energy penetration, Wang’s research paves the way for a more sustainable and efficient energy future.