Decision modelling tools for utilities in the deregulated energy market

Abstract

This thesis examines the impact of the deregulation of the energy market on decision making and optimisation in utilities and demonstrates how decision support applications can solve specific encountered tasks in this context. The themes of the thesis are presented in different frameworks in order to clarify the complex decision making and optimisation environment where new sources of uncertainties arise due to the convergence of energy markets, globalisation of energy business and increasing competition.

This thesis reflects the changes in the decision making and planning environment of European energy companies during the period from 1995 to 2004. It also follows the development of computational performance and evolution of energy information systems during the same period. Specifically, this thesis consists of studies at several levels of the decision making hierarchy ranging from top-level strategic decision problems to specific optimisation algorithms. On the other hand, the studies also follow the progress of the liberalised energy market from the monopolistic era to the fully competitive market with new trading instruments and issues like emissions trading.

This thesis suggests that there is an increasing need for optimisation and multiple criteria decision making methods, and that new approaches based on the use of operations research are welcome as the deregulation proceeds and uncertainties increase. Technically, the optimisation applications presented are based on Lagrangian relaxation techniques and the dedicated Power Simplex algorithm supplemented with stochastic scenario analysis for decision support, a heuristic method to allocate common benefits and potential losses of coalitions of power companies, and an advanced Branch-and-Bound algorithm to solve efficiently non-convex optimisation problems. The optimisation problems are part of the operational and tactical decision making process that has become very complex in the recent years.

Similarly, strategic decision support has also faced new challenges. This thesis introduces two applications involving multiple criteria decision making methods. The first application explores the decision making problem caused by the introduction of 'green' electricity that creates additional value for renewable energy. In this problem the stochastic multi-criteria acceptability analysis method (SMAA) is applied. The second strategic multi-criteria decision making study discusses two different energy-related operations research problems: the elements of risk analysis in the energy field and the evaluation of different choices with a decision support tool accommodating incomplete preference information to help energy companies to select a proper risk management system. The application is based on the rank inclusion in criteria hierarchies (RICH) method.