Project Title:R&D and Application of Virtual Power Plant (VPP) Solution based on Trusted Computing and Energy Storage System (ESS) Precise Regulation
Participating Enterprises: Shanghai Jiaqi Electric Power Co., Ltd., University of Shanghai for Science and Technology, Shanghai Rongheyuan Energy Storage Co., Ltd.
Award Received: Scientific and Technological Progress Award - Bronze Award
Achievement Level: International Advanced Level
Project Number: ECF-2025-SET-1005
Main Participants: Hu Jinshuang, Sun Weiqing, Shi Jie, Yang Wenwei, Wang Haibing, Wang Youjia, Yuan Liyang, Qin Xiangfu
Expert Review Comments:
International advanced level. This project addresses the core challenge of ensuring both data trustworthiness and real-time performance in virtual power plant (VPP) construction. By integrating trusted computing, blockchain, and generative artificial intelligence technologies, the project developed a unified VPP solution that combines precision energy storage control with data security. It enables trustworthy data chaining, traceable management, and intelligent dispatch optimization, significantly improving the reliability and control efficiency of VPP operations.
The technology has been successfully demonstrated in Shanghai and Jiangsu, producing notable economic and social benefits. Its high replicability and strong promotion potential make it an effective technical foundation for distributed energy coordination and green, low-carbon transformation.
Main Innovations:
(1) Data Credibility and Real-Time Integration:
To resolve the contradiction between data traceability and real-time requirements in virtual power plant environments, the project developed a post-blockchain data application modeling algorithm that ensures data credibility, traceability, and real-time responsiveness.
Based on AntChain blockchain hardware, the system enables VPP operational data to be recorded on-chain with high performance, high reliability, and robust privacy protection.
Through a self-developed trust label technology, it ensures immutable storage of operational data, guaranteeing the trustworthiness and traceability of aggregated settlement data from large-scale user resources.
On this foundation, the project further developed algorithms for response capability evaluation, response plan formulation and clearing deviation optimization, and response result verification, ensuring the real-time effectiveness of on-chain data and improving operational settlement accuracy for virtual power plants.
(2) Precision Energy Storage Control via Generative AI:
The project developed a generative AI-based precision control strategy for energy storage systems to serve as a “balancing unit” within the VPP.
Generative AI models are used to predict user-side distributed renewable generation, load demand, and assess the status of controllable resources.
Based on real-time state evaluation and control of energy storage systems, the system treats storage assets as equivalent to aggregated balancing resources, effectively compensating for multi-source uncertainties and enabling precise dispatch of the virtual power plant.
In a demonstration project in Shanghai Songjiang, this energy storage control strategy effectively mitigated fluctuations from photovoltaic and load variations, improving response accuracy by approximately 20%.
Main Uses and Technical Principles:
1) Uses:
Designed for use by virtual power plant operators (aggregators) to support operational management and coordination.
2) Technical Principles:
A virtual power plant (VPP) leverages operators, aggregators, or power retailers to integrate demand-side resources through advanced information and communication technologies (ICT), forming scalable, flexible regulation capacity to support grid stability.
From a technical perspective, a VPP aggregates controllable loads, distributed generation, electric vehicles, and distributed energy storage from numerous user-side assets via IoT and AI technologies. Through an internet-based platform, it integrates geographically dispersed and diverse resources to participate in grid operations as a “virtual power source” under market-based mechanisms.
This project provides a comprehensive technical support platform enabling VPP operators to efficiently conduct business operations, ensuring secure, reliable, and intelligent coordination among distributed energy resources.
Technical Applications:
The project results have been implemented and validated through multiple agreements, demonstrations, and service deployments, including:
2024 Annual Virtual Power Plant Application Certification (Jiaqi Electric Power)
Electricity Demand Response Aggregation Agreement (Shanghai Hongqiao Business District Energy Service – Jiaqi Electric Power)
User Application Certification (Energy Service)
User Application Certification (New Energy)
Electricity Demand Response Aggregation Agreement (Youfu & Jiaqi)
Electricity Demand Response Aggregation Agreement (Shanghai Di – Jiaqi Electric Power)
Nantong Guoxuan Virtual Power Plant SaaS Service Contract
Shanghai Jiuxing Energy Virtual Power Plant SaaS Service Contract
User Application Certification – Shanghai Rongfangneng New Energy Technology Co., Ltd.
These successful applications verify the technology’s high reliability, scalability, and practical value, establishing it as a benchmark solution for the next-generation intelligent and trustworthy virtual power plant ecosystem.
