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

Fuente: arXiv
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Main Authors: Yi, Xinyi, Lestas, Ioannis
Format: Preprint
Published: 2025
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author Yi, Xinyi
Lestas, Ioannis
author_facet Yi, Xinyi
Lestas, Ioannis
contents We address the problem of temperature regulation and optimal energy sharing in district heating systems (DHSs) where the demand and system parameters are unknown. We propose a temperature regulation scheme that employs data-driven on-policy updates that achieve these objectives. In particular, we show that the proposed control scheme converges to an optimal equilibrium point of the system, while also having guaranteed convergence to an optimal LQR control policy, thus providing good transient performance. The efficiency of our approach is also demonstrated through extensive simulations.
format Preprint
id arxiv_https___arxiv_org_abs_2509_16083
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle On-Policy Reinforcement-Learning Control for Optimal Energy Sharing and Temperature Regulation in District Heating Systems
Yi, Xinyi
Lestas, Ioannis
Systems and Control
We address the problem of temperature regulation and optimal energy sharing in district heating systems (DHSs) where the demand and system parameters are unknown. We propose a temperature regulation scheme that employs data-driven on-policy updates that achieve these objectives. In particular, we show that the proposed control scheme converges to an optimal equilibrium point of the system, while also having guaranteed convergence to an optimal LQR control policy, thus providing good transient performance. The efficiency of our approach is also demonstrated through extensive simulations.
title On-Policy Reinforcement-Learning Control for Optimal Energy Sharing and Temperature Regulation in District Heating Systems
topic Systems and Control
url https://arxiv.org/abs/2509.16083