Coordinated residential energy resource scheduling with human thermal comfort modelling and renewable uncertainties

Shu Wang, Fengji Luo, Zhao Yang Dong, Zhao Xu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)

Abstract

With development of two-way communication technology, residential users are able to reshape their energy consumption patterns based on demand response signals. This study proposes an optimal residential energy resource scheduling model to minimise the home electricity cost while fully considering the user's life convenience, the user's thermal comfort, and renewable uncertainties. The proposed model accounts for the characteristics of shiftable appliance, air-conditioning system, electric vehicle's charging pattern, and renewable generation of both wind and solar power. Wasserstein distance metric and K-medoids-based scenario generation and reduction techniques are used to address the renewable uncertainty. An adaptive thermal comfort model is employed to estimate the user's indoor thermal comfort degree. A waiting cost model is applied to measure the user's preference on the household appliance's operation. In addition, a recently proposed metaheuristic optimisation algorithm (the natural aggregation algorithm) is used to solve the proposed model. The simulation results show the proposed model is effective in minimising the household's daily electricity bill while preserving the user's comfort level.

Original languageEnglish
Pages (from-to)1768-1776
Number of pages9
JournalIET Generation, Transmission and Distribution
Volume13
Issue number10
DOIs
Publication statusPublished - 21 May 2019

Keywords

  • air conditioning
  • demand side management
  • distributed power generation
  • domestic appliances
  • electric vehicles
  • energy consumption
  • optimisation
  • power generation scheduling
  • renewable energy sources
  • thermal comfort

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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