Advanced computational techniques for electricity market analysis considering sustainable energy supply issues

Electricity industries worldwide are undergoing rapid and deepening change. This has been largely driven by the market-oriented restructuring underway in many countries and the increasing global concerns about climate change.Unceasingly climate change is becoming one of a major challenge for the sustainable development of power industries, as power generations are the major sources of Greenhouse Gas (GHG) emissions. This research is dedicated to developing advanced computational techniques to solve several power system problems that emerge in deregulated electricity markets and changing environmental protection regulations. Three major objectives are included and achieved in this research. The first objective is to assess and quantify the potential impacts on, and changes in, economic efficiency in the electricity generation sector in Australian national electricity market after the implementation of ETS, as opposed to without the introduction of ETS. The focus is on the relative changes in electricity wholesale prices, generators merit order of dispatch, market power and possible compensation to carbon intensive generators.The second objective is to create a constrained multi-objectives evolutionary optimisation model based on decomposition for power system dispatch by minimising generation costs and carbon emissions.The third objective is to develop an electricity price forecast model using differential evolution (DE) algorithm-enhanced evolutionary extreme learning machine (E-ELM). The main goal is to provide more accurate and reliable prediction of electricity price to facilitate energy market participants’ portfolio and risk management.