Two-time-scale coordinated optimal control of building energy systems for demand response considering forecast uncertainties

Research output: Journal article publicationJournal articleAcademic researchpeer-review

10 Citations (Scopus)

Abstract

Predictive scheduling is essentially needed to optimize energy dispatch of building energy systems for demand response, as it can maximize the benefits considering future conditions. However, the optimized demand response probably cannot be achieved and energy demands probably cannot be fully satisfied in operation due to the large discrepancy between the predicted and actual conditions. Generic and comprehensive optimal control methods of building energy systems for demand response, which can achieve maximized benefits while ensuring satisfaction of actual energy demands, are still absent. In this study, a two-time-scale coordinated optimal control strategy is proposed to optimize energy dispatch between building energy systems for demand response considering forecast uncertainties. The control strategy includes two coordinated schemes: a stochastic scheduling scheme and a real-time optimal control scheme. Forecast uncertainties are quantified based on real meteorological data. The optimal start time of scheduling optimization horizon is investigated. The control strategy was tested on a platform with PV and battery, established based on the Zero Carbon Building in Hong Kong. Results show that the optimal scheduling optimization horizon start time is the initial of the off-peak period. The proposed strategy reduces up to 14.7% cost compared with existing strategies while satisfying actual energy demands.

Original languageEnglish
Article number124204
JournalEnergy
Volume253
DOIs
Publication statusPublished - 15 Aug 2022

Keywords

  • Building energy systems
  • Demand response
  • Forecast uncertainties
  • Multi-time scale optimal control
  • Real-time optimal control
  • Stochastic scheduling

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Modelling and Simulation
  • Renewable Energy, Sustainability and the Environment
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Pollution
  • General Energy
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Management, Monitoring, Policy and Law
  • Electrical and Electronic Engineering

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