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Demand Response Model Development for Smart Households Using Time of Use Tariffs and Optimal Control—The Isle of Wight Energy Autonomous Community Case Study

Lookup NU author(s): Dr Adib Allahham, Professor Damian Giaouris

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

© 2020 by the authorsResidential variable energy price schemes can be made more effective with the use of a demand response (DR) strategy along with smart appliances. Using DR, the electricity bill of participating customers/households can be minimised, while pursuing other aims such as demand-shifting and maximising consumption of locally generated renewable‐electricity. In this article, a two‐stage optimization method is used to implement a price‐based implicit DR scheme. The model considers a range of novel smart devices/technologies/schemes, connected to smart‐meters and a local DR‐Controller. A case study with various decarbonisation scenarios is used to analyse the effects of deploying the proposed DR‐scheme in households located in the west area of the Isle of Wight (Southern United Kingdom). There are approximately 15,000 households, of which 3000 are not connected to the gas‐network. Using a distribution network model along with a load flow software‐tool, the secondary voltages and apparent‐power through transformers at the relevant substations are computed. The results show that in summer, participating households could export up to 6.4 MW of power, which is 10% of installed large‐scale photovoltaics (PV) capacity on the island. Average carbon dioxide equivalent (CO2e) reductions of 7.1 ktons/annum and a reduction in combined energy/transport fuel‐bills of 60%/annum could be achieved by participating households.

Publication metadata

Author(s): Khanna S, Becerra V, Allahham A, Giaouris D, Foster JM, Roberts K, Hutchinson D, Fawcett J

Publication type: Article

Publication status: Published

Journal: Energies

Year: 2020

Volume: 13

Issue: 3

Online publication date: 22/01/2020

Acceptance date: 21/01/2020

Date deposited: 10/02/2020

ISSN (print): 1996-1073

ISSN (electronic): 1996-1073

Publisher: MDPI AG

URL: https://doi.org/10.3390/en13030541

DOI: 10.3390/en13030550

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