Portfolio-based decision-support tool for generation investment and planning in uncertain and low-carbon future electricity industries

Growing concerns over high and volatile fossil-fuel prices, energy security and climate change present significant sustainability challenges for electricity industries around the world. They have particular implications for generation investment and planning as decisions to build power plants represent major commitments with regard to future electricity industry costs, fuel dependencies, energy security outcomes, and greenhouse emissions. Increasing uncertainty about future fuel prices, plant construction costs, climate change policies and electricity demand adds to the challenges. In consequence, generation investment decision-making is transitioning from traditional cost minimisation approaches towards more complex assessments incorporating future uncertainties and a range of industry objectives.This thesis presents a novel generation investment and planning decision-support tool for assessing possible future generation portfolios within an electricity industry under uncertainty and multiple policy objectives. The tool extends conventional optimal generation mix concepts by using Monte Carlo simulation and portfolio analysis techniques to determine probability distributions of future expected generation costs and greenhouse emissions for a wide range of generation portfolios. The tool can incorporate complex and correlated probability distributions for future fossil-fuel prices, carbon prices, plant investment costs, and electricity demand including price elasticity impacts. It supports sophisticated risk assessments, including downside economic risks, for any generation portfolio. A post-processing analysis is also implemented to assess the potential operational dispatch constraints and costs associated with different portfolios in meeting time-varying demand.Applications of this tool are demonstrated through case studies of electricity industries with different generation options facing a range of future uncertainties. The results provide high-level insights into issues that will likely be key drivers of future industry performance such as the impact of different sources of uncertainty, the role of future demand changes in response to electricity prices, and the interaction between wind penetration and carbon pricing on the expected costs, cost risks, and emissions of different portfolios. The implications of nuclear power and the value of different technologies within generation portfolios are also explored. These results highlight the potential value of the tool in facilitating utility and policy decision-making in the challenging task of generation investment and planning for increasingly uncertain future electricity industries