Control, optimisation, uncertainty, and coupled infrastructure

Control and optimisation of electricity and heating systems

Control and optimisation methods for electricity and heating systems under uncertainty, with emphasis on district heating, coupled dynamics, and operational guarantees.

This project direction focuses on how electricity and heating systems can be operated efficiently and reliably when demand, disturbances, or system parameters are uncertain. It brings together online control, reinforcement learning, and robust-control ideas for district heating and coupled power-heat systems.

Research Focus

Optimality-embedded data-enabled policy optimization for real-time economic dispatch and temperature regulation
Reinforcement-learning control for energy sharing and temperature regulation in district heating systems
Mixed H-infinity / passivity approaches for ancillary-service provision in coupled electricity-heating systems

Related Publications

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[C1]Conference Paper

A Mixed ℋ∞--Passivity Approach for Leveraging District Heating Systems as Frequency Ancillary Service in Electric Power Systems

X. Yi, I. Lestas

IEEE Power & Energy Society General Meeting (PESGM), 2026.

Selected for the Emerging Technologies Best Paper Session.

2026

[C2]Conference Paper

On-Policy Reinforcement-Learning Control for Optimal Energy Sharing and Temperature Regulation in District Heating Systems

X. Yi, I. Lestas

IEEE Conference on Decision and Control (CDC), 2025.

2025

[C3]Conference Paper

Optimal Energy-Sharing and Temperature Regulation in District Heating Systems

X. Yi, I. Lestas

IFAC Conference on Networked Systems (NecSys), 2025.

2025

[P1]Journal Manuscript

Optimality-Embedded Data-Enabled Policy Optimization for Real-Time Economic Dispatch and Temperature Regulation in District Heating Systems

X. Yi, I. Lestas

manuscript under review.