Modelling Uncertainty in Modern Power Systems

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Over the past two decades, three major phenomena have significantly impacted how electrical power systems are planned and operated including: (i) the restructuring of electricity markets; (ii) the integration of large-scale wind and solar variable generators into the supply-side, and; (iii) the addition of new resources, particularly self-generation of electricity, into the demand-side. Market restructuring has introduced economic uncertainty because electricity market prices for energy and reserve services are no longer fixed. Rather, prices are determined in real-time and forward auctions, and thus, are uncertain. Energy production using wind and solar-powered generators is environmentally friendly. However, because of the natural variability of wind speed and solar radiation, the generation outputs are uncertain. On the demand-side, the consumption patterns of electricity consumers have historically been predictable because consumers have been using electricity passively. However, smart grid technologies and demand-side resources have enabled consumers to have control over their electricity use, and thus, their consumption patterns are no longer predictable and straightforward. In addition, the increasing adoption of roof-top solar modules for on-site power generation is offsetting the consumption needs of consumers. This adds to the variability of net demand, i.e., electricity that is supplied by the central grid. To run power systems efficiently, a clear understanding of these uncertainties is necessary. Previous research has focused on solutions for modelling and forecasting uncertain variables. Nevertheless, there are still areas where available solutions are limited. Big data solutions and deep learning techniques present new opportunities to address some of the limitations of conventional modelling techniques. In this research, the ultimate objective is to reduce the cost of electricity to society. Achieving this objective requires optimal operation and planning models that are capable of representing power systems in practical yet accurate manners. Such models need to take into account economic factors (e.g., short- and long-term electricity prices), supply resources, and the net demand for electricity. The applicant has identified particular research gaps in these areas. These gaps will be explored, and solutions will be developed based on big data solutions and deep learning methods. These are effective methods for exploring large, complex datasets. The developed solutions will help power system policymakers, operators and planners in making short- and long-term decisions based on practically sound models. The research will train highly qualified personnel on the applications of big data solutions in power systems; this is an emerging area, and thus, the research will likely create new intellectual property in Canada.