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

Sourav Khanna, Victor Becerra, Adib Allahham, Damian Giaouris, Jamie M. Foster, Keiron Roberts, David Hutchinson and Jim Fawcett
Additional contact information
Sourav Khanna: School of Energy and Electronic Engineering, University of Portsmouth, Portsmouth PO1 3DJ, UK
Victor Becerra: School of Energy and Electronic Engineering, University of Portsmouth, Portsmouth PO1 3DJ, UK
Adib Allahham: School of Engineering, Newcastle University, Newcastle upon Tyne NE17RU, UK
Damian Giaouris: School of Engineering, Newcastle University, Newcastle upon Tyne NE17RU, UK
Jamie M. Foster: School of Mathematics and Physics, University of Portsmouth, Portsmouth PO1 2UP, UK
Keiron Roberts: Faculty of Technology, University of Portsmouth, Portsmouth PO1 3AH, UK
David Hutchinson: Faculty of Technology, University of Portsmouth, Portsmouth PO1 3AH, UK
Jim Fawcett: Isle of Wight Council, County Hall, Newport, Isle of Wight PO30 1UD, UK

Energies, 2020, vol. 13, issue 3, 1-27

Abstract: Residential 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 (CO 2 e) reductions of 7.1 ktons/annum and a reduction in combined energy/transport fuel-bills of 60%/annum could be achieved by participating households.

Keywords: demand response; electric vehicle; solar photovoltaics; battery; optimisation; non-linear programming; sustainability (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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