Page 500 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

Use Case Status: The use case is part of a larger product development

2�2 Benefits of the use case

1. Improving Energy Efficiency. Through real-time optimization of generating unit outputs,
the Intelligent Section Flow Agent ensures that coal-fired power plants operate at their
most efficient levels. This reduces coal consumption, minimizes carbon emissions, and
promotes cleaner energy usage compared to manual scheduling methods.
2. Enhancing Economic Efficiency of Power Grid Operations. The Intelligent Section Flow
Agent optimizes control strategies to maintain transmission flows within safe and efficient
limits. By reducing the need for costly investments in transmission infrastructure and
lowering operational expenses, the agent ensures cost-effective power grid operations,
which enhances economic productivity and stability.
3. Facilitating Renewable Energy Development. The agent ensures the stable operation of
long-distance transmission networks, facilitating the efficient delivery of renewable energy
from resource-rich regions in southwestern and northwestern China to load centers in
the southeast. By supporting renewable energy integration, the Intelligent Section Flow
Agent accelerates the transition to a sustainable energy system and reduces reliance on
fossil fuels.

2�3 Future Work

Our future work will focus on refining and extending the Intelligent Section Flow Agent system
to address emerging challenges in power grid operation. The enhancements will include
architectural improvements, advanced modeling techniques, and practical deployment
strategies to maximize system robustness, adaptability, and scalability.

1� Enhanced Data Management and Integration

Future developments will emphasize integrating additional data sources, such as high-temporal-
resolution measurements and environment information, to enrich real-time monitoring.
Improved preprocessing algorithms will be developed to handle diverse data formats, enhance
quality, and ensure greater accuracy for downstream decision-making processes.

2� Advanced Agent Training

We plan to explore hybrid learning approaches by incorporating physical knowledge into
reinforcement learning models. This integration will enhance model generalization, enabling
the Intelligent Section Flow Agent to handle more complex and extreme scenarios while
maintaining grid safety and efficiency. Moreover, transfer learning techniques will be utilized
to accelerate agent training across similar operational regions, reducing computational costs.

3� Distributed and Scalable Architecture

The scheduling platform will be optimized for greater scalability through enhanced distributed
computing frameworks. This will ensure reliable deployment in large-scale, multi-regional
power grid systems.

4� Real-Time Optimization and Emergency Handling
Future iterations will incorporate adaptive algorithms to improve the agents' real-time response
capability to dynamic grid changes and emergencies. Advanced risk assessment modules will

464