Data Analytics in Competitive Electricity Markets to Uncover the Impact of Emerging Technologies
Abstract
The electrical power industry has entered a transition towards sustainable, reliable and clean solutions. It is a continuous revolution trending to a large-scale expansion of renewables in power systems. There have been, however, serious concerns over reliable and secure operation of power systems. Energy storage facilities are increasingly being used to help integrate renewable energy resources into the grid. While understanding the environmental benefits of these emerging technologies
is straightforward, the economic impacts of their integration in a competitive market is more complicated. These emerging technologies are likely to have an economically-important effect on the dynamics of electricity prices. This is a concern to different sections of electricity markets including power suppliers, policy makers, and end users.
This thesis focuses on applying data mining tools to competitive electricity markets in order to uncover the impact of emerging technologies such as wind power and storage systems on the dynamics of electricity prices. Data-driven approaches are developed to explore the impact on wholesale prices of individual wind farms and independently-operated large-scale energy storage systems. Additionally, this thesis proposes a data-driven methodology to determine a justified support scheme for upcoming wind farms by incorporating their estimated revenue and levelized cost of energy. Moreover, an operation-inspired electricity price prediction scheme is developed to improve the economic profit obtained from operation of storage facilities in competitive markets.
Numerical simulations are provided for the electricity markets of Alberta and Ontario. The results prove the efficiency and accuracy of proposed methodologies in estimating the impact on wholesale prices of emerging technologies. In addition, the obtained results from both competitive markets indicate that the presented methodology in this thesis is able to estimate the revenue of an upcoming wind farm with reasonable accuracy, which successively determines the support scheme awarded to the project. Moreover, the performed analyses manifest the effectiveness of the proposed price prediction scheme in improving the economic performance of storage systems.
Description
Keywords
Engineering--Electronics and Electrical
Citation
Zamanidehkordi, P. (2017). Data Analytics in Competitive Electricity Markets to Uncover the Impact of Emerging Technologies (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/25516