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Electricity Retailing Decision Making Based on Data Mining Techniques ELECTRICITY RETAILING DECISION MAKING BASED ON DATA MINING TECHNIQUES By JIAJIA YANG B.E. M.E. A thesis in fulfilment of the requirements for the degree of Doctor of Philosophy School of Electrical Engineering and Telecommunications Faculty of Engineering April 2018 PLEASE TYPE THE UNIVERSITY OF NEW SOUTH WALES Thesis/Dissertation Sheet Surname or Family name: Yang First name: Jiajia Other name/s: Abbreviation for degree as given in the University calendar: PhD School: School of Electrical Engineering and Faculty: Faculty of Engineering Telecommunications Title: Electricity Retailing Decision Making Based on Data Mining Techniques Abstract 350 words maximum: (PLEASE TYPE) With the continuous development of Smart Grid, especially the emergence of Energy Internet, there is an increasing amount of measurement data available collected from power system end-users. Through data mining techniques, these measurement data can enable a better understanding of the load composition and end-user consumption behaviours, and therefore would provide great potentials for developing more flexible and targeted or even customized pricing schemes for electricity retail. This research starts with a comprehensive literature survey on decision-making for electricity retailers. Publications on electricity retailing in the last two decades are surveyed and discussed in detail. Then, key business framework of electricity retailers is studied. It elaborates the typical business process of electricity retailers and its procedure of creating a new sales agreement. Considering the drawbacks of existing load data mining methods, a new non-intrusive load monitoring method is proposed which is able to cope with the big load data in the Smart Grid environment. After obtained the status of all identified appliances, a statistical residential load model is developed. With this load model, the appliance identification results can be conveniently used in demand-side management and developing electricity retailing strategies. Next, this research proposes the idea of using the results of residential appliance identification and end-user behaviour analysis to help retail pricing. The problem of designing customized pricing strategies for different residential users is investigated based on the identification results of residential electric appliances and classifications of end-users according to their consumption behaviours. A novel framework of customizing electricity retail prices is proposed. When to customize retail prices through appliance identification, load data at least sampled at every minute is needed. Differently, this research explores another data mining technique to customize electricity retail prices using the half-hourly sampled electricity consumption data. A model of customizing electricity retail prices based on load profile clustering analysis is developed. Electricity usage data collected by the Smart Grid, Smart City (SGSC) project in Australia is used to demonstrate the feasibility and efficiency of the developed models and algorithms. Declaration relating to disposition of project thesis/dissertation I hereby grant to the University of New South Wales or its agents the right to archive and to make available my thesis or dissertation in whole or in part in the University libraries in all forms of media, now or here after known, subject to the provisions of the Copyright Act 1968. I retain all property rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation. I also authorise University Microfilms to use the 350 word abstract of my thesis in Dissertation Abstracts International (this is applicable to doctoral theses only). …………………………………………………………… ……………………………………..……………… ……….……………………... Signature Witness Signature …….… Date The University recognises that there may be exceptional circumstances requiring restrictions on copying or conditions on use. Requests for restriction for a period of up to 2 years must be made in writing. Requests for a longer period of restriction may be considered in exceptional circumstances and require the approval of the Dean of Graduate Research. FOR OFFICE USE ONLY Date of completion of requirements for Award: ORIGINALITY STATEMENT ‘I hereby declare that this submission is my own work and to the best of my knowledge it contains no materials previously published or written by another person, or substantial proportions of material which have been accepted for the award of any other degree or diploma at UNSW or any other educational institution, except where due acknowledgement is made in the thesis. Any contribution made to the research by others, with whom I have worked at UNSW or elsewhere, is explicitly acknowledged in the thesis. I also declare that the intellectual content of this thesis is the product of my own work, except to the extent that assistance from others in the project's design and conception or in style, presentation and linguistic expression is acknowledged.’ Signed …………………………………………….............. Date …………………………………………….............. COPYRIGHT STATEMENT ‘I hereby grant the University of New South Wales or its agents the right to archive and to make available my thesis or dissertation in whole or part in the University libraries in all forms of media, now or here after known, subject to the provisions of the Copyright Act 1968. I retain all proprietary rights, such as patent rights. I also retain the right to use in future works (such as articles or books) all or part of this thesis or dissertation. I also authorise University Microfilms to use the 350 word abstract of my thesis in Dissertation Abstract International (this is applicable to doctoral theses only). I have either used no substantial portions of copyright material in my thesis or I have obtained permission to use copyright material; where permission has not been granted I have applied/will apply for a partial restriction of the digital copy of my thesis or dissertation.' Signed ……………………………………………........................... Date ……………………………………………........................... AUTHENTICITY STATEMENT ‘I certify that the Library deposit digital copy is a direct equivalent of the final officially approved version of my thesis. No emendation of content has occurred and if there are any minor variations in formatting, they are the result of the conversion to digital format.’ Signed ……………………………………………........................... Date ……………………………………………........................... Abstract With the continuous development of Smart Grid, especially the emergence of Energy Internet, there is an increasing amount of measurement data available collected from power system end-users. Through data mining techniques, these measurement data can enable a better understanding of the load composition and end-user consumption behaviors, and therefore would provide great potentials for developing more flexible and targeted or even customized pricing schemes for electricity retail. This research starts with a comprehensive literature survey on decision-making for electricity retailers. Publications on electricity retailing in the last two decades are surveyed and discussed in detail. Then, key business framework of electricity retailers is studied. It elaborates the typical business process of electricity retailers and its procedure of creating a new sales agreement. Considering the drawbacks of existing load data mining methods, a new non-intrusive load monitoring method is proposed which is able to cope with the big load data in the Smart Grid environment. After obtained the status of all identified appliances, a statistical residential load model is developed. With this load model, the appliance identification results can be conveniently used in demand-side management and developing electricity retailing strategies. Next, this research proposes the idea of using the results of residential appliance identification and end-user behaviour analysis to help retail pricing. The problem of designing customized pricing strategies for different residential users is investigated based on the identification results of residential electric appliances and classifications of end-users according to their consumption behaviours. A novel framework of customizing electricity retail prices is proposed. When to customize retail prices through appliance identification, load data at least sampled at every minute is needed. Differently, this research explores another data mining technique to customize electricity retail prices using the half-hourly sampled electricity consumption data. A model of customizing electricity retail prices based on load profile clustering analysis is developed. Electricity usage data collected by the Smart Grid, Smart City (SGSC) national demonstration project in Australia is used to demonstrate the feasibility and efficiency of the developed models and algorithms. I In memory of my father To my beloved mother II Acknowledgements Thanks to the guidance, supervision, and advice from my supervisors, my PhD research and study can always be kept on the right track and carried out in an efficient way. My supervisors have been a constant source of academic support, encouragement and innovative ideas for my research. My thanks and appreciations firstly go to Professor Zhao Yang Dong. It is extremely lucky for me to join the research group led by Professor Zhao Yang Dong. Thanks Professor Zhao Yang Dong for your prompt response no matter when I have a request, and for all the
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