Year

2017

Degree Name

Doctor of Philosophy

Department

School of Electrical, Computer and Telecommunications Engineering

Abstract

With the deregulation of electricity markets, electricity prices exhibit extreme volatilities including unexpected and abrupt spikes depicting variabilities in network conditions. These spikes may result into serious financial risks for a utility who want to buy electricity from a spot market and sell it to customers at a fixed price. In addition, electricity demand is becoming more uncertain due to rapid population growth, fast economic development and changes in climatic conditions. Due to high uncertainty and inelasticity in demand, generators with high marginal cost need to be deployed. In such case, accurate forecasts of demand and price are necessary for economic operation of a power system. Conventional methods on demand and price forecasting such as regression analysis and artificial neural networks exhibit drawbacks, especially with high variabilities in demand and price. Accordingly, novel forecasting tools need to be devised for accurate prediction of demand and price in competitive electricity market environment. This thesis focuses on three main interrelated subjects, which are electricity demand forecasting, electricity price forecasting and electricity demand response, to improve decision making of market participants and enhance the elasticity of electricity demand.

This thesis is unavailable until Saturday, January 12, 2019

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