Low-carbon
power systems
Planning and operating electricity systems with renewable generation, energy storage and flexible demand.
PLANNING & OPERATIONENERGY SYSTEMS INTEGRATION
Power for AI.
AI for power.
Connecting the future of computing with a low-carbon electricity system.
We study how data centres, intelligent optimisation and flexible energy systems can work together to support the grid.
Explore our research
An integrated energy system.
WELCOME TO THE WEI SUN LAB
We bring together power systems, machine learning and optimisation to understand how computing and clean energy can evolve together.
Led by Dr Wei Sun, Chancellor’s Fellow in Energy Systems Integration at the University of Edinburgh, our research connects low-carbon electricity planning with the operation of flexible data centres.
01 / RESEARCH
Our research
From the electricity network to the computing job, we connect planning decisions with how systems operate.
Low-carbon
AI & advanced
optimisation
Applied machine learning and optimisation for power flow, scheduling and decisions under complex system constraints.
MACHINE LEARNINGData centre
flexibility
Coordinating AI workloads, cooling and UPS / battery systems to integrate computing demand with the grid.
COMPUTING & THE GRID02 / PROJECTS
Research in practice
CLEAN POWER PROCUREMENT
Clean energy for computing loads
We investigate how data centres can procure and use clean electricity, combining renewable energy, storage and flexible demand. Our research explores how procurement strategies and coordinated energy planning can support the transition to a low-carbon power system.
Impact of Edge AI Inference on the Power System
Technical leadership on a project funded by the Department for Energy Security and Net Zero (DESNZ).
Aligning computing loads with renewable energy
Exploring computing flexibility using PyPSA-GB, an open-source model of the Great Britain electricity system co-developed by Wei Sun.
Low-carbon scheduling of computing jobs
Ensemble reinforcement learning aligns workloads with cleaner electricity while balancing carbon emissions and quality of service.
03 / PUBLICATIONS
Publications
Recent research on computing flexibility and intelligent power-system operation.
IEEE TRANSACTIONS ON SMART GRID
Multi-Objective Low-Carbon Scheduling Method for Data Centers Based on Ensemble Reinforcement Learning
Y. Wang, W. Sun, P. Ren, and G. P. Harrison
Vol. 17, no. 1, pp. 297–308IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS
Grid Frequency Stability Support Potential of Data Center: A Quantitative Assessment of Flexibility
P. Ren, W. Sun, Y. Wang, and G. P. Harrison
Early access · Linked version: arXiv preprintAPPLIED ENERGY
A Self-Supervised Constraint-Embedded Neural Network for AC Optimal Power Flow
Y. Wang, W. Sun, and G. P. Harrison
Vol. 426, Part A, article 128584ARXIV · PREPRINT
Hosting Capacity Assessment of Data Centers With Voltage Ride-Through Capability in Power Systems
P. Ren, W. Sun, and F. Teng
arXiv:2609.03030 · Not yet peer reviewed04 / PEOPLE
Our team
PRINCIPAL INVESTIGATOR
Dr Wei Sun
Chancellor’s Fellow in
Energy Systems Integration
University of Edinburgh
Wei studies how AI and data centres can support a low-carbon electricity system. His research uses applied data science and advanced optimisation to quantify computing flexibility and improve its integration with the power grid.
His current work explores shifting computing jobs across time and locations to align with renewable generation, alongside the integration of on-site storage and clean energy.
Co-developer of PyPSA-GB and co-organiser of the 1st International Workshop on Managing Global Energy Demand of AI Data Centres at Imperial College London.
PhD students
Jiakai Wu
PhD Student
Pengyu Ren
PhD Student
Waritsara Laaiad
PhD Student
Yifan Wang
PhD Student