Distributionally Robust Energy Consumption Scheduling of HVAC Considering the Uncertainty of Outdoor Temperature and Human Activities

Yingjie Wang; Qi Zeng; Zhongbei Tian; Yuefang Du; Pingliang Zeng; Lin Jiang

doi:10.17775/CSEEJPES.2021.08170

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Distributionally Robust Energy Consumption Scheduling of HVAC Considering the Uncertainty of Outdoor Temperature and Human Activities

Special Section Papers | 更新时间：2026-10-15

- Distributionally Robust Energy Consumption Scheduling of HVAC Considering the Uncertainty of Outdoor Temperature and Human Activities
- CSEE Journal of Power and Energy Systems Vol. 9, Issue 3, Pages: 896-909(2023)
- 作者机构：
  
  1. Department of Electrical Engineering and Electronics, The University of Liverpool, Liverpool, U.K
  2. School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China,Chengdu,China
  3. School of Automation, Hangzhou Dianzi University,Hangzhou,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2021.08170
  CLC：
- Published：01 May 2023
- 稿件说明：
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Yingjie Wang, Qi Zeng, Zhongbei Tian, et al. Distributionally Robust Energy Consumption Scheduling of HVAC Considering the Uncertainty of Outdoor Temperature and Human Activities[J]. CSEE Journal of Power and Energy Systems, 2023, 9(3): 896-909.
DOI：

Yingjie Wang, Qi Zeng, Zhongbei Tian, et al. Distributionally Robust Energy Consumption Scheduling of HVAC Considering the Uncertainty of Outdoor Temperature and Human Activities[J]. CSEE Journal of Power and Energy Systems, 2023, 9(3): 896-909. DOI： 10.17775/CSEEJPES.2021.08170.

摘要

Abstract

Achieving a low or zero carbon target is to reduce energy demand and improve energy efficiency of electricity consumers. One of the main electricity consumers in power systems is heating

ventilation

and air conditioning systems (HVACs)

which cost around 30% of the total usage in commercial buildings. This paper investigates the scheduling problem of HVAC energy consumption taking into account two uncertainties: outdoor temperature and human activities. The distributionally robust optimization approach (DROA) is extended to deal with these two uncertainties which are modeled by the proposed disjointed layered ambiguity sets according to historical data. Based on the proposed DROA method

the distributionally robust chance constraints (DRCCs) will be formulated as a nonlinear optimization problem

converted into a linear optimization problem using duality theorem and solved using SeDuMi solver. Simulation results are used to compare with existing methods

which shows the proposed DROA can decrease 2.81% and 0.14% of the electricity cost in comparison with traditional RO method and DROA based on a nest layered ambiguity set

respectively. Also

the proposed DROA decreases the number and maximum of violations from the comfort level of users. The multi-zone HVAC system model is used in the case study to verify the proposed DROA with the disjointed ambiguity set. The consecutive simulation results illustrate the proposed DROA approach can provide stable performance in a three-day scheduling period.

关键词

Keywords

references

11. ( 2021, Jan. ). ASHRAE Standard 90.1. Energy standard for buildings except low-rise residential buildings . [Online ] . Available: https://www.ashrae.org/technical-resources/bookstore/standard-90-1.

29. Timeanddate. ( 2021, Jan. ). Past Weather in Austin , Texas, USA. ( 2013 ). [Online ] . Available: https://ww w.timeanddate.com/weather/usa/austin/his-toric?month=8=2013.

30. Campus Meeting Rooms. ( 2021, Jan. 19 ). University of Liverpool Conferences and Events . University of Liverpool. [Online ] . Available: https://www.liverpool.ac.uk/conferences-and-events/venues/campus-meeting-rooms/.

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