Cohort: 2 

Location: Beijing, China 

Application Title: City-scale AI orchestration system for efficient and low-carbon grid dispatch and virtual power plant operations 

Team Members: Wei Xie (executive sponsor), Guanxun Diao, Yuezhong Tang, Jun Zhu, Ling Luo, Xiao Luo

The challenge 

As one of the world’s largest and most complex megacities, Shanghai faces growing challenges in managing its power grid. Rising electricity demand, carbon reduction targets and the increasing share of variable renewable energy all add to operational complexity. 

The grid serves more than 1.7 million corporate users and 10.6 million residential users. Balancing supply and demand requires continuous coordination across generation, transmission, load and storage. Traditional forecasting and regulation systems struggle to manage this complexity in real time. Manual coordination is too slow for the second-level decisions in response to respond to renewable fluctuations, while expanding thermal generation or storage capacity entails high costs and additional carbon impact. 

The solution 

A unified AI platform for city-scale grid orchestration 

To address these constraints, the State Grid Corporation of China developed a city-scale AI orchestration platform that integrates forecasting, trading, regulation and settlement into a single operational system. 

The platform is built on the Guangming Large Model, a proprietary multimodal large language model trained on more than 200 terabytes of power system data and a domain-specific token library of 3.5 million energy-related terms. It continuously predicts and optimizes power flows across Shanghai’s grid using real-time data from edge gateways, enterprise systems and grid sensors. This provides detailed visibility across generation, transmission and consumption, enabling more coordinated management of distributed energy resources. 

How it works 

Real-time prediction and adaptive control 

The system combines spatiotemporal modelling, Model Predictive Control (MPC) and NSGA-II optimisation to support adaptive control with second-level response times. It operates on an on-premises architecture with edge–cloud coordination, allowing real-time orchestration of thousands of distributed energy devices. 

These include air conditioners, electric vehicle chargers and industrial equipment, which can be coordinated dynamically while maintaining user comfort and grid stability. The platform uses multiple intelligent agents to manage forecasting, trading, regulation and settlement within a unified workflow, supported by human-in-the-loop oversight through operational dashboards. 

This approach reduces reliance on manual balancing and enables more consistent responses to rapidly changing grid conditions. 

How it differs from conventional approaches 

From isolated tools to integrated orchestration 

Conventional grid management systems typically address forecasting, regulation and trading as separate functions. State Grid’s platform brings these capabilities together, allowing decisions to be coordinated rather than sequential. 

Instead of reacting to fluctuations after they occur, the system anticipates changes in demand and supply and adjusts control strategies accordingly. This architecture has enabled applications such as reactive power demand response in Shanghai’s Lin-gang industrial zone, helping stabilize voltage levels without requiring additional infrastructure investment. 

Impact 

• 2.03 GW of dispatchable capacity coordinated across 49 virtual power plant operators and more than 4,000 users 
• More than RMB 8 billion saved through avoided generation and transmission investments 
• 1.236 billion kWh of clean energy consumed annually, with 510,000 tonnes of CO₂ reduced per year 
• 99.999% voltage compliance and 99.9983% grid reliability 
• Improvements of 12.5% in forecasting accuracy, 72% in regulation response, and 51% in settlement efficiency 
• Over 15,000 active users across grid operations and virtual power plant teams 

Why it matters 

Managing grid complexity in renewable-heavy systems 

The State Grid Corporation of China’s platform illustrates how AI-based orchestration can support power system operations in large, renewable-heavy urban environments. 

By integrating forecasting, control and settlement within a single system, the platform helps maintain grid stability while reducing the need for costly infrastructure expansion. This approach supports more efficient use of existing assets as cities work to meet growing demand and decarbonisation goals.