Degree Name

Doctor of Philosophy


School of Electrical, Computer and Telecommunications Engineering


Climate change mitigation is one of the significant global concerns, and mitigation strategies have been initiated at different sectors including electric power industry. Reduction of Greenhouse Gas (GHG) emissions, less dependency on fossil fuels and increased utilisation of renewable resources are some of the most common climate change mitigation initiatives. Since the conventional electric power generation systems are built based on the fossil fuel based electricity generation technologies, electric power utilities which own these generation systems are responsible for reducing GHG emissions from electricity generation. To achieve this, generation technologies are shifting from conventional to non-conventional, with a gradual increase in the renewable energy penetration. As a result, electricity utilities are required to include the generation mix changes in their electricity network planning practices. The uncertainty and variability in the availability of generation output introduces challenges for electricity utilities to maintain the specified reliability level despite significant increase in the penetration of renewable resources in the grid. The collection of papers in this thesis aims to develop electricity generation planning techniques, which can address the emission reduction in electricity generation, the uncertainty in power availability of renewable resources, and the generation adequacy of both conventional and renewable generation technologies. In order to address these aspects, following approaches have been developed in this thesis

A novel probabilistic load flow algorithm for distribution network with renewable distributed generations (DGs) has been developed considering the coincidental variations between different demand groups and renewable generation sources to assess the probability distribution of power flow through the distribution feeders. The simulation results show the effectiveness of Pearson‟s distribution functions based proposed approach in modelling the probability distribution of the random variables with non-Gaussian distribution.

New indices and assessment methodologies for distribution network adequacy assessment are proposed. A new approach has been developed considering the joint probability distribution between demand and renewable generation availability to estimate the adequacy of energy supply and service continuation in the distribution network with renewable DG systems. The results demonstrate that proposed analytical methods for distribution network adequacy assessment reduces the computational effort with acceptable accuracy compared to the computationally extensive simulation based methods.

A novel approach has been proposed for a renewable based hybrid energy system (HES) design in a distribution network thereby achieving sustainability in power generation and distribution. A life cycle assessment process is used to estimate the embodied primary emission of the energy generated from renewable based generation systems. A multi-objective optimisation model is developed to find the optimum solutions for renewable based HES. The potential impact of energy storage systems in the renewable based HES design has been quantified through analysis.

The performances of different climate change mitigation technologies in emission reduction from an electricity generation system of New South Wales have been studied. A new methodology has been developed to model the embodied emission of the energy supplied through the electricity grid. The results suggest that implementation of climate change mitigation technologies strongly influences the emission reduction capability of renewable generation systems, and coordination between climate change mitigation technologies is also essential to achieve the emission reduction target efficiently.

A new wind generation planning methodology has been developed using a multiobjective optimisation technique to share the spatially diverse wind generation within multi-area power systems. A trade-off analysis method has been developed to examine the collective effect of multiple objective functions of load carrying capacity of wind farms, emission offset and capacity upgrade of transmission network interconnections for wind resource sharing strategy. The influence of correlation coefficients between the generation output of wind farms and the system demand on the wind generation capacity allocation has been observed from the simulation results of a case study involving Southeast Australian power systems. It is found that the uncertainty and fluctuation in the output of wind generation systems can be mitigated with the aid of energy storage systems.

A novel power dispatch strategy has been developed using a stochastic programming model to improve the schedulability and the supply reliability of a battery energy storage system (BESS) integrated wind farm. Moreover, a ranked based dispatch control strategy has been devised by arranging the total BESS integrated with the wind farm into multiple battery units to maintain the wind farm schedulability at a level of expectation. The results emphasises that the proposed strategy can schedule the output of wind farm without the requirement of alteration, and battery lifetime is maximised by avoiding the occurrence of frequent charging and discharging cycles.

In order to consider the correlation between renewable generation and system demand, a unique non-iterative method has been developed incorporating a joint probability distribution of demand and renewable generation to estimate the effective load carrying capability of the renewable generation plants. The results indicate that the proposed method reduces computational burden while maintaining acceptable accuracy level as compared to the existing methods.