Behavioural Demand Response for Future Smart Homes: Investigation of Demand Response Strategies for Future Smart Homes that Account for Consumer Comfort, Behaviour and Cybersecurity

Smart metering and precise measurement of energy consumption levels have brought more detailed information and interest on the actual load profile of a house which continues to improve consumer-retailer relationships. Participation in demand response (DR) programs is one of these relationships but studies have shown that there are considerable impacts resulting to some level of discomfort on consumers as they aim to follow a suggested load profile. This research therefore investigates the impact on consumers while participating in DR programs by evaluating various perspectives that includes:  Modelling the causes discomfort during participation in DR programs;  Evaluation of user participation capabilities in DR programs;  Identification of schedulable and non-schedulable loads and opportunities;  Application of load scheduling mechanism which caters for specific user concerns.  Investigation towards ensuring a secure and robust system design. The key source of information that enhances this work is obtained from data on historical user behavior which can be stored within a smart controller installed in the home and optimised using genetic algorithm implemented on MATLAB. Results show that user participation in DR programs can be improved and effectively managed if the challenges facing home owners are adequately understood. This is the key contribution of this work whereby load schedules created are specifically tailored to meet the need of the users hence minimizing the impact of discomfort experienced due to participation in DR programs. Finally as part of the test for robustness of the system design in order to prevent or minimize the impact of any event of a successful cyber-attack on the load or price profiles, this work includes means to managing any such attacks thereby mitigating the impact of such attacks on users who participate in demand response programs. Solutions to these attacks are also proffered with the aim of increasing robustness of the grid by being sufficiently proactive.

Grantor dc:publisher.institution

University of Bradford

Author dc:creator

• Anuebunwa, Ugonna R.

Advisors dc:contributor.advisor

• Pillai, Prashant
• Rajamani, Haile S.

• Base loads
• Demand response
• Discomfort
• Essential loads
• Fuzzy logic
• Genetic algorithm
• Load profiles and schedules
• Optimisation
• Smart homes
• Smart metering
• User participation index

Statement dc:rights

• <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/"><img alt="Creative Commons License" style="border-width:0" src="http://i.creativecommons.org/l/by-nc-nd/3.0/88x31.png" /></a><br />The University of Bradford theses are licenced under a <a rel="license" href="http://creativecommons.org/licenses/by-nc-nd/3.0/">Creative Commons Licence</a>.

Language dc:language.iso

en

Handle dc:identifier.uri

http://hdl.handle.net/10454/17359

OAI identifier oai:identifier

oai:bradscholars.brad.ac.uk:10454/17359