A novel distributed control framework for thermostatically controlled loads cluster

Abstract Renewable energy is developing rapidly, and its intermittency and uncertainty pose a serious challenge to the grid’s regulation capability. Demand response, as a crucial means of integrating flexible load-side resources, has become a key technology in alleviating grid pressure. This paper focuses on thermostatically controlled loads and proposes a novel distributed control framework. The framework decouples the load control loop by dividing the control process into a short distributed coordination phase and a long local tracking phase. This approach significantly reduces communication requirements of the load nodes. Through mathematical derivations, numerical simulations, and hardware-in-the-loop experiments, the theoretical feasibility and practical effectiveness of the proposed framework are validated.

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Publication Details

Journal
Discover Artificial Intelligence
Published
2025-06-16
DOI
https://doi.org/10.1007/s44163-025-00368-9
Primary Topic
Smart Grid Energy Management
Type
article
Field-Weighted Citation Impact
0.00

Funders

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article

A novel distributed control framework for thermostatically controlled loads cluster

Yuanshi Zheng, Zhenwei Yu, Li Ding
Discover Artificial Intelligence
Smart Grid Energy Management
article

A novel distributed control framework for thermostatically controlled loads cluster

Yuanshi Zheng, Zhenwei Yu, Li Ding
article en

Abstract

Abstract Renewable energy is developing rapidly, and its intermittency and uncertainty pose a serious challenge to the grid’s regulation capability. Demand response, as a crucial means of integrating flexible load-side resources, has become a key technology in alleviating grid pressure. This paper focuses on thermostatically controlled loads and proposes a novel distributed control framework. The framework decouples the load control loop by dividing the control process into a short distributed coordination phase and a long local tracking phase. This approach significantly reduces communication requirements of the load nodes. Through mathematical derivations, numerical simulations, and hardware-in-the-loop experiments, the theoretical feasibility and practical effectiveness of the proposed framework are validated.

Discover Artificial IntelligenceVol. 5(1)
Wuhan University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 88%
Smart Grid Energy Management
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