Scheduling HVAC Loads to Promote Renewable Generation Integration with Learning-Based Joint Chance-Constrained Approach

Ge Chen; Hongcai Zhang; Hongxun Hui; Yonghua Song

doi:10.17775/CSEEJPES.2022.06580

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Scheduling HVAC Loads to Promote Renewable Generation Integration with Learning-Based Joint Chance-Constrained Approach

Regular Papers | 更新时间：2026-04-16

- Scheduling HVAC Loads to Promote Renewable Generation Integration with Learning-Based Joint Chance-Constrained Approach
- Scheduling HVAC Loads to Promote Renewable Generation Integration with Learning-Based Joint Chance-Constrained Approach
- 中国电机工程学会电力与能源系统学报(英文) 2026年12卷第2期页码：734-748
- 作者机构：
  
  Department of Electrical and Computer Engineering, State Key Laboratory of Internet of Things for Smart City, University of Macau, Macao,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2022.06580
  中图分类号：
- 纸质出版：2026
- 稿件说明：
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Ge Chen, Hongcai Zhang, Hongxun Hui, 等. Scheduling HVAC Loads to Promote Renewable Generation Integration with Learning-Based Joint Chance-Constrained Approach[J]. 中国电机工程学会电力与能源系统学报(英文), 2026,12(2):734-748.

Ge Chen, Hongcai Zhang, Hongxun Hui, et al. Scheduling HVAC Loads to Promote Renewable Generation Integration with Learning-Based Joint Chance-Constrained Approach[J]. CSEE Journal of Power and Energy Systems, 2026, 12(2): 734-748.
Ge Chen, Hongcai Zhang, Hongxun Hui, 等. Scheduling HVAC Loads to Promote Renewable Generation Integration with Learning-Based Joint Chance-Constrained Approach[J]. 中国电机工程学会电力与能源系统学报(英文), 2026,12(2):734-748. DOI： 10.17775/CSEEJPES.2022.06580.

Ge Chen, Hongcai Zhang, Hongxun Hui, et al. Scheduling HVAC Loads to Promote Renewable Generation Integration with Learning-Based Joint Chance-Constrained Approach[J]. CSEE Journal of Power and Energy Systems, 2026, 12(2): 734-748. DOI： 10.17775/CSEEJPES.2022.06580.

摘要

Abstract

Integration of distributed renewable generation (DRG) in distribution networks can be effectively promoted by scheduling flexible resources such as heating

ventilation

and air conditioning (HVAC) loads. However

finding the optimal scheduling for them is not trivial because DRG outputs are highly uncertain. To address this issue

this paper proposes a learning-based joint chance-constrained approach to coordinate HVAC loads with DRG. Unlike cutting-edge works adopting individual chance constraints to manage uncertainties

this paper controls the violation probability of all critical constraints with joint chance constraints (JCCs). This joint manner can explicitly guarantee operational security of the entire system based on operators' preferences. To overcome intractability of JCCs

we first prove that JCCs can be safely approximated by robust constraints with proper uncertainty sets. A famous machine learning algorithm

one-class support vector clustering

is then introduced to construct a small enough polyhedron uncertainty set for these robust constraints. A linear robust counterpart is further developed based on the strong duality to ensure computational efficiency. Numerical results based on various distributed uncertainties confirm the advantages of the proposed model in optimality and feasibility.

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