Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/78387
Title: Distributed residential energy resource scheduling with renewable uncertainties
Authors: Luo, FJ
Dong, ZY
Xu, Z 
Kong, WC
Wang, F
Keywords: Renewable energy sources
Energy management systems
Tariffs
Power markets
Solar power stations
Power generation scheduling
Monte Carlo methods
Sampling methods
Probability
Domestic appliances
Optimisation
Cost reduction
Distributed power generation
Power generation economics
Distributed residential energy resource scheduling
Renewable uncertainty
Two-way communication technology
Home energy management system
HEMS
Smart home environment
Electricity tariff
Solar power output
Monte Carlo sampling technique
Probabilistic solar radiation model
Automatically controlled household appliance
Optimal DRER scheduling model
Heuristic optimisation algorithm
Natural aggregation algorithm
Australian solar data
Issue Date: 2018
Publisher: Institution of Engineering and Technology
Source: IET generation, transmission & distribution, 19 June 2018, v. 12, no. 11, p. 2770-2777 How to cite?
Journal: IET generation, transmission & distribution 
Abstract: Advances in metering and two-way communication technologies foster the studies of Home Energy Management System (HEMS). This study proposes a new HEMS, which optimally schedules the distributed residential energy resources (DRERs) in a smart home environment with varying electricity tariff and high solar penetrations. The uncertainties of solar power output are captured by using Monte Carlo sampling technique to generate multiple solar output scenarios based on the probabilistic solar radiation model. The homeowner's rigid and elastic restrictions on the operations of the automatically controlled household appliances are modelled. Based on this, an optimal DRER scheduling model is proposed to minimise the home operation cost while taking into account the homeowner's requirements. A new heuristic optimisation algorithm recently proposed by the authors, i.e. natural aggregation algorithm, is used to solve the proposed model. Simulations based on real Australian solar data are conducted to validate the proposed method.
URI: http://hdl.handle.net/10397/78387
ISSN: 1751-8687
EISSN: 1751-8695
DOI: 10.1049/iet-gtd.2017.1136
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

6
Citations as of Dec 10, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.