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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 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.