Biography：

Dr. Xingyu Yan received his Ph.D. in Electrical Engineering from École Centrale de Lille (L2EP), France, in 2018. He subsequently worked at the Private Higher Institute of YNCREA in Hauts-de-France from 2017 to 2018. From 2018 to 2020, he served at the Economic and Technological Research Institute of the Global Energy Interconnection Development and Cooperation Organization (GEIDCO) in Beijing.Since 2020, Dr. Yan has focused on research related to power demand-side management in electricity market environments, as well as the application of artificial intelligence in virtual power plant (VPP) aggregation, trading, and optimal scheduling. He has led more than ten scientific research and consulting projects supported by the National Natural Science Foundation of China, the China Postdoctoral Science Foundation, and the State Grid Corporation of China. He has published more than 40 peer-reviewed papers indexed in SCI and EI.

(https://www.linkedin.com/in/xingyu-yan-7876742b/ )

Publications：       

Representative Articles

(https://scholar.google.com/citations?user=5AjsLwMAAAAJ&hl=en)

1. Xingyu Yan, Ciwei Gao*,Yurui Xia, Tao Chen, Dongjun Won, and Hyoseop Leeand. Optimal Scheduling of Virtual Power Plant based on Twin Delayed Deep Deterministic Policy Gradient Algorithm, 6th International Conference on AEES, 2025

2. Xingyu Yan, Ciwei Gao*, and Bruno Francois. Multi-objective Optimization of a Virtual Power Plant with Mobile Energy Storage for a Multi-stakeholders Energy Community, Applied Energy, 2025, (SCI Q1)

3. Xingyu Yan, Ciwei Gao*, Meng Song, Mian Rizwan. AUGMECON2-based multi-objective optimization of virtual power plant considering economical and security operation of the distribution networks, Sustainable Energy Grids & Networks, 2024, (SCI Q1)

4. Xingyu Yan, Ciwei Gao*, Han Jiang, Bruno Francois. Multi-objective optimization and profit allocation of virtual power plant considering the security operation of distribution networks, Journal of Energy Storage, 2024, (SCI Q1)

5. Xingyu Yan, Ciwei Gao*, Yuting Mou, Dhaker Abbes. Consensus Alternating Direction Multiplier Method based Fully Distributed Peer-to-Peer Energy Transactions Considering the Network Transmission Distance, Sustainable Energy Grids & Networks, 2024, (SCI Q1)

6. Meng Song, Rongnan Deng, Xingyu Yan*, et al. Two-Stage Time-Linked Demand Response for Distribution System Resilience Enhancement, Applied Energy, 2024, (SCI Q1)

7. Meng Song, Xingyu Yan*, Wang Xudong, et al. Optimal Scheduling Strategy of Networked Data Centers in Multiple Transactive Energy Markets, IEEE, CSEE Journal of Power and Energy Systems, 2024, (SCI Q1)

8. Xingyu Yan, Ciwei Gao*, Jing Meng, Dhaker Abbes. An Analytical Target Cascading Method-Based Two-Step Distributed Optimization Strategy for Energy Sharing in a Virtual Power Plant, Renewable Energy, 2024, (SCI Q1)

9. Xingyu Yan, Ciwei Gao*, Hao Ming, et al. Optimal scheduling strategy and benefit allocation of multiple VPPs based on general Nash bargaining theory, International Journal of Electrical Power & Energy Systems, 2023, (SCI Q1)

10. Xingyu Yan, Meng Song*, Jiacheng Cao, et al. Peer-to-Peer transactive energy trading of multiple microgrids considering renewable energy uncertainty, International Journal of Electrical Power & Energy Systems, 2023, (SCI Q1)

11. Xingyu Yan, Ciwei Gao*, Tao Chen, et al. Framework Design and Application Prospect for Digital Twin Virtual Power Plant System, ‌Proceedings of the CSEE, 2023, (EI)

12. Xingyu Yan, Ciwei Gao*, Meng Song, et al. An IGDT-based Day-ahead Co-optimization of Energy and Reserve in a VPP Considering Multiple Uncertainties, IEEE Transactions on Industry Applications, 2022 (SCI Q2)

13. Xingyu Yan, Dhaker Abbes, Bruno Francois*, Development of a tool for urban microgrid optimal energy planning and management, Simulation Modelling Practice and Theory, 2018 (SCI Q2)

14. Xingyu Yan, Dhaker Abbes, Bruno Francois*, Uncertainty analysis for day ahead power reserve quantification in an urban microgrid including PV generators, Renewable Energy, 2017 (SCI Q1)

Academic Activities

1. Invited Speaker, “Optimal Energy and Frequency Regulation Markets Bidding Strategy of a VPP”, 2025 IEEE the 9th EI2, Jilin, China.

2. Oral, “Optimal Scheduling of VPP based on Twin Delayed Deep Deterministic Policy Gradient Algorithm”, 2025 IEEE the 6th AEES, Xi’an, China.Best Paper Award.

3. Oral, 2022 IEEE PSET, Aalborg, Denmark, Online.Best Oral Presentation Award.

4. Session Chair & Invited Speaker of Section “Power Market and Demand Response”, 2022 IEEE 5th CIEEC, Nanjing, China, Online.

5. Oral, 2022 IEEE 5th CIEEC, Nanjing, China, Online.Best Paper Award.

6. Oral, 2021 IEEE IAS I&CPS Asia, Chendu, China, Online.

7. Oral, 2020 IEEE iSPEC, Chendu, China.

8. Oral, International conference on EPE 2016, ECCE Europe, Karlsruhe, Germany.

9. IAP Workshop on Microgrids and Smart Grids, June 2016, Ghent University, Ghent, Belgium.

10. Poster, International conference on PowerTech 2015, Eindhoven, The Netherlands.

11. Oral at CRIEC, Colloque de Recherche Inter Écoles Centrales, Lyon 2014 and Lille 2015, respectively.

Research：

(https://www.researchgate.net/profile/Xingyu-Yan-3?ev=hdr_xprf )

Recent project:Optimized operation of Virtual Power Plant (VPP) based on artificial intelligence (AI) methods

The project is to leverage existing VPP technology and develop artificial intelligence techniques to address technical challenges in VPP modeling, trading, and regulation.

Task 1: Develop a transaction decision-making technology for virtual power plants based on multi-agent systems, construct an aggregated interactive transaction model suitable for VPPs and their internal distributed resource entities, propose a multi-agent system-based transaction decision-making method for VPP multiple entities, and form a transaction-driven normalized market operation mechanism for VPP.

Task 2: Based on AI methods such as deep reinforcement learning, propose a centralized control strategy for VPP interaction operation technology that considers uncertainties, a distributed optimization control strategy for VPP based on federated learning, and a real-time operation control strategy for VPP based on deep reinforcement learning, along with efficient solving methods.

Teaching：

[1] Fundamentals of Power Economics, Graduate Course (English)

Graduates：

Others:

I enjoy sports such as basketball, football, and table tennis. I also speak French fluently.

I am currently affiliated with the Power Economy and Technology Research Institute (PETRI) in the School of Electrical Engineering at Southeast University. If you are interested in electricity markets, demand-side management, or applying artificial intelligence to power systems, I would be happy to have you join our research group.