Investment incentives and valuation of power sector infrastructure assets with increasing interconnection, intermittency and decarbonisation

This dissertation applies a variety of quantitative methods to electricity and carbon market data, utility company accounts data, capital and operating costs to analyse some of the challenges associated with investment in energy assets. In particular, three distinct research topics are analysed within this general theme: the efficiency of interconnector trading, the optimal sizing of intermittent wind facilities and the impact of carbon pricing on the cost of capital for investors are researched in successive sections.

Current methods and advances in forecasting of wind power generation

Wind power generation differs from conventional thermal generation due to the stochastic nature of wind. Thus wind power forecasting plays a key role in dealing with the challenges of balancing supply and demand in any electricity system, given the uncertainty associated with the wind farm power output. Accurate wind power forecasting reduces the need for additional balancing energy and reserve power to integrate wind power. Wind power forecasting tools enable better dispatch, scheduling and unit commitment of thermal generators, hydro plant and energy storage plant and more competitive market trading as wind power ramps up and down on the grid. This paper presents an in-depth review of the current methods and advances in wind power forecasting and prediction. Firstly, numerical wind prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed. Next the statistical and machine learning approach methods are detailed. Then the techniques used for benchmarking and uncertainty analysis of forecasts are overviewed, and the performance of various approaches over different forecast time horizons is examined. Finally, current research activities, challenges and potential future developments are appraised.