In the rapidly evolving energy landscape, the integration of distributed power sources, new energy storage systems, and charging facilities is transforming distribution networks. This shift is pushing the limits of traditional planning methods, which rely heavily on manual decision-making. Enter artificial intelligence (AI), a technology poised to revolutionize how we plan and optimize these complex systems.
A recent study published by researchers from the State Grid Economic and Technological Research Institute and Tianjin University delves into the challenges and opportunities presented by AI in distribution network planning. Led by LI Jingru and a team of experts, the research highlights the profound changes occurring in the physical, digital, and commercial forms of distribution networks.
“The traditional planning method based on manual decision-making hinders distribution network optimization due to massive factors, complex structures, and numerous pieces of equipment,” explains LI Jingru, the lead author. This statement underscores the need for more sophisticated tools to manage the increasing complexity of modern distribution networks.
The study, published in the journal ‘Dianli jianshe’ (translated as ‘Electric Power Construction’), identifies several key challenges in AI-based distribution network planning. These include the precise spatiotemporal prediction of source-load, probabilistic balance of power and energy, and the coordinated planning of source-grid-load-storage. Additionally, the research emphasizes the need for digitalization and intelligence empowerment to address these issues effectively.
One of the standout findings is the potential of AI to overcome technical bottlenecks in distribution network planning. The researchers focus on critical aspects such as knowledge graph construction, source-load scenario generation, power-energy balance, planning demand reduction, and intelligent network planning. These areas are crucial for enhancing the efficiency and reliability of distribution networks.
However, the journey is not without its hurdles. The study acknowledges difficulties in processing unstructured and semi-structured data, limited scenario applicability, low accuracy in demand deduction, lack of interpretability, and high-dimensional solution spaces for planning schemes. To tackle these challenges, the researchers propose several technical solutions, including graph learning, transfer learning, multimodal fusion, enhanced interpretability, and human-machine hybrid intelligence enhancement.
The commercial implications of this research are significant. As the energy sector continues to embrace renewable sources and advanced storage technologies, the need for intelligent and adaptive distribution networks becomes paramount. AI-based planning can provide the scalability and flexibility required to meet these demands, ultimately leading to more efficient and reliable energy distribution.
Looking ahead, the researchers plan to continue investigating AI-based distribution network planning methods. Their goal is to address key challenges and provide valuable insights for the development and digital-intelligent transformation of distribution network planning technology systems under the new power system framework.
As the energy sector navigates the complexities of a rapidly changing landscape, AI stands out as a beacon of innovation. The work of LI Jingru and the team from the State Grid Economic and Technological Research Institute and Tianjin University is paving the way for a future where distribution networks are smarter, more efficient, and better equipped to meet the demands of a sustainable energy future.
