Risk-based methods for the valuation and planning of sustainable energy assets.

This research project aims to develop and apply appropriate methods dealing with risk and uncertainty at a technology and energy system level providing decision support to the various stakeholders involved in the planning, development and operation of sustainable energy investments. The thesis comprises a portfolio of research activities fulfilling the set research objectives. Outcomes of this research portfolio have been either published or are under the peer review process. More specifically, following a systematic literature review to identify the state-of- the-art in risk-based methods for sustainable energy systems planning and feasibility studies, a cluster analysis was applied based on data from existing offshore wind energy installations in the UK, to distinguish investment strategies followed by equity investors. This study has identified three distinct clusters of investors, namely the late entry, pre-commissioning and build-operate-transfer investors. Subsequently, a high-fidelity lifecycle techno economic model was developed allowing for the temporal valuation of a renewable energy investment. This integrated model has allowed for a set of parametric equations to be developed through appropriate selection of approximation models linking global design parameters to key performance indicators. Furthermore, a stochastic extension of the financial appraisal model has allowed for a transition from the conventional deterministic terminology to a stochastic one, assigning confidence levels to key performance indicators (KPIs). Additionally, the development of a purpose-specific tool for the evaluation of the operational phase KPIs, such as the availability, operating cost and energy production losses due to planned and unplanned maintenance has allowed for the development of better-informed risk control policies. Finally, having analysed uncertainties at a technology level, a stochastic optimisation framework was developed for deriving optimal national power generation technology mixes taking into account uncertainties for a series of scenarios linked to national energy strategies through appropriate constraints in the analysis.