Data-driven Distributionally Robust Energy Consumption Scheduling of HVAC based on Disjoint Layered Ambiguity Set

Yingjie Wang, Yuefang Du, Chao Duan, Haotian Xu, Lin Jiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes a distributionally robust optimization approach (DROA) based on a disjoint layered ambiguity set for scheduling the energy consumption of the heating, ventilation and air conditioning (HVAC) system. The uncertainties of the predicted outdoor temperature error and indoor temperature variations that are caused by human activities are taken into account in the energy consumption scheduling of the HVAC based on historical data. The maximum uncertainty set of outdoor temperature is divided into disjoint subintervals and the probabilistic information of these subintervals is obtained to construct a disjoint layered ambiguity set. A nonlinear HVAC's energy consumption problem is formulated by using the DROA method to deal with these two uncertainties based on a disjoint layered ambiguity set with distributionally robust chance constraints (DRCCs). In order to solve this nonlinear problem, these DRCCs are converted to a linear programming problem and solved by using linear programming. Simulation results illustrate the effectiveness of the proposed method and comparing them with the DROA based on a nest layered ambiguity set and the traditional robust approach (ROA), the proposed DROA based on a disjoint layered ambiguity set reduces the electricity cost and guarantees the thermal comfort level of users.

Original languageEnglish (US)
Title of host publication2019 IEEE Power and Energy Society General Meeting, PESGM 2019
PublisherIEEE Computer Society
ISBN (Electronic)9781728119816
DOIs
StatePublished - Aug 2019
Event2019 IEEE Power and Energy Society General Meeting, PESGM 2019 - Atlanta, United States
Duration: Aug 4 2019Aug 8 2019

Publication series

NameIEEE Power and Energy Society General Meeting
Volume2019-August
ISSN (Print)1944-9925
ISSN (Electronic)1944-9933

Conference

Conference2019 IEEE Power and Energy Society General Meeting, PESGM 2019
CountryUnited States
CityAtlanta
Period8/4/198/8/19

Keywords

  • Distributionally robust optimization
  • HVAC
  • demand response
  • energy consumption scheduling

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Nuclear Energy and Engineering
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering

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