Dr Yihong Zhou (周羿宏)
University of Oxford, Oxford, UK.
Building the control layer for flexible, AI-native power systems.
I design optimization and AI methods that make flexible loads dependable enough for real grid operation, with safety guarantees and accelerator-native computation.
Future power systems will not be operated simply by adding more forecasting tools and control dashboards. They will need a new algorithmic layer that turns millions of grid-edge devices and AI-supporting data centres into "grid-intelligent" assets that operators can trust. This ambition forms the foundation of my research.
faster chance-constrained approximations
agents trained in GradMAP without parameter sharing
GPU-accelerated batched power-flow speedups
2026 IEEE special sessions chaired
Representative work I am proud to be building on.
Strengthened Wasserstein-robust approximation for large-scale decisions under uncertainty, with major speedups.
Multi-agent proximal learning for 1,000 agents without parameter sharing, embedding three-phase AC load flow.
Shows how AI data centres can become fast grid resources, with validation on real GPUs.
Three questions driving the next stage.
Can AI infrastructure support the grid it depends on?
Designing power-aware workload control and flexibility products for AI/HPC data centres.
Can learning agents be made safe enough for energy systems?
Combining optimization structure, probabilistic safety, and multi-agent learning.
Can grid computation become AI-native?
Building batched, accelerator-ready power-flow and dispatch tools for modern AI ecosystems.
Active in the emerging AI-for-energy community.
- Panelist, IEEE PES International Meeting 2026, Hong Kong.
- Special Session Chair, IEEE I&CPS Asia 2026, Kunming.
- Special Session Chair, IEEE EI2 2026, Shanghai.
- Organiser and chair, Oxford Workshop on Safeguarded AI Agents for Grid-Edge Flexibility.
PhD in grid flexibility, MSc in AI, now building at the interface of both.
PhD, University of Edinburgh, 2025. Visiting PhD Student at Oxford, 2024--2025. Current postdoctoral work is part of ARIA SAGEflex at the University of Oxford.
未来电力系统不能只是简单叠加更多预测工具和控制面板。它需要一层新的算法基础设施，把数以百万计的电网边缘设备和支撑 AI 的数据中心，转化为运行者能够信任的“grid-intelligent”资产。这一目标构成了我研究工作的基础。
机会约束近似加速
GradMAP 中无参数共享训练的智能体
GPU 批量潮流计算加速
2026 IEEE special session chair
我最想持续往下推进的几条研究线。
下一阶段围绕三个问题展开。
AI 基础设施能否支撑其所依赖的电网？
研究面向 AI/HPC 数据中心的电力感知工作负载控制和灵活性产品。
学习型智能体能否安全进入能源系统？
结合优化结构、概率安全保证和多智能体学习方法。
电网计算能否成为 AI 原生工具？
构建批量化、可加速的潮流与调度工具，融入现代 AI 生态。
积极参与 AI for Energy 新兴学术社区。
- IEEE PES International Meeting 2026 圆桌嘉宾，香港。
- IEEE I&CPS Asia 2026 Special Session Chair，昆明。
- IEEE EI2 2026 Special Session Chair，上海。
- Oxford Workshop on Safeguarded AI Agents for Grid-Edge Flexibility 组织者与主席。
博士研究电网灵活性，硕士训练人工智能，现在连接二者。
2025 年获爱丁堡大学博士学位，2024--2025 年曾在牛津大学访问。目前在牛津大学 ARIA SAGEflex 项目中开展博士后研究。
News
| May 16, 2026 | I will serve as a Special Session Chair for IEEE EI2 2026 Special Session 02 on collaborative planning and operation of source-network-load-storage in new-type power systems. |
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| May 16, 2026 | I will serve as a Special Session Chair for IEEE I&CPS Asia 2026 Special Session 18 on AI-enabled optimization for integrated energy and transportation systems in smart cities. |
| May 16, 2026 | Our paper Grid-Intelligent AI Data Centres for Primary Response is now available in IEEE Transactions on Industry Applications. |
| May 13, 2026 | Our preprint JAX-Based Batched AC Power Flow for GPU Acceleration and AI Ecosystem Integration is now available on arXiv. |
| May 12, 2026 | Our paper Strengthened and Faster Linear Approximation to Joint Chance Constraints with Wasserstein Ambiguity has been published online in INFORMS Journal on Computing. |
| Apr 27, 2026 | Our preprint GradMAP: Gradient-Based Multi-Agent Proximal Learning for Grid-Edge Flexibility is now available on arXiv. |
| Feb 03, 2026 | We hosted the first FleXEdge project workshop at the University of Oxford, with collaborators from Imperial College London, the University of Edinburgh (EPCC), and LIST (Luxembourg Institute of Science and Technology). |
| Jan 30, 2026 | Our paper, “Independent Aggregators Securing End-User Wasserstein Distributionally Robust Flexibility through Bilevel Incentives,” has been published in Applied Energy and is now available on ScienceDirect. |
| Jan 28, 2026 | We have two papers accepted for (Power Systems Computation Conference) PSCC 2026! See you in Limassol, Cyprus! |
| Jan 25, 2026 | I created my first personal website! |
| Oct 10, 2025 | Our preprint FICA: Faster Inner Convex Approximation of Chance Constrained Grid Dispatch with Decision-Coupled Uncertainty is available on arXiv. |
| Sep 18, 2025 | I officially received my PhD degree from the University of Edinburgh. |
| Jan 20, 2025 | Our preprint Strengthened and Faster Linear Approximation to Joint Chance Constraints with Wasserstein Ambiguity is now available on arXiv. |