Analysing electricity markets with evolutionary computation

The use of electricity in 21st century living has been firmly established throughout most of the world, correspondingly the infrastructure for production and delivery of electricity to consumers has matured and stabilised. However, due to recent technical and environmental–political developments, the electricity infrastructure worldwide is undergoing major restructuring. The forces driving this reorganisation are a complex interplay of technical, environmental, economic and political factors. The general trend of the reorganisation is a dis–aggregation of the previously integrated functions of generation, transmission and distribution, together with the establishment of competitive markets, primarily in generation, to replace previous regulated monopolistic utilities. To ensure reliable and cost effective electricity supply to consumers it is necessary to have an accurate picture of the expected generation in terms of the spatial and temporal distribution of prices and volumes. Previously this information was obtained by the regulated utility using technical studies such as centrally planned unit–commitment and economic–dispatch. However, in the new deregulated market environment such studies have diminished applicability and limited accuracy since generation assets are generally autonomous and subject to market forces. With generation outcomes governed by market mechanisms, to have an accurate picture of expected generation in the new electricity supply industry, it is necessary to complement traditional studies with new studies of market equilibrium and stability. Models and solution methods have been developed and refined for many markets, however they cannot be directly applied to the generation market due to the unique nature of electricity, having high inelastic demand, low storage capability and distinct transportation requirements. Intensive effort is underway to formulate solutions and models that specifically reflect the unique characteristics of the generation market. Various models have been proposed including game theory, stochastic and agent–based systems. Similarly there is a diverse range of solution methods including, Monte–Carlo simulations, linear–complimentary and quadratic programming. These approaches have varying degrees of generality, robustness and accuracy, some being better in certain aspects but weaker in others.