Capturing the benefits of high performance computing for investment decisions in electricity markets: an emphasis on capacity expansion in a carbon-constrained and uncertain future
Charles Ikenna Nweke (BEng)
Project submitted in partial fulfilment of the requirements for the degree of MSc (Energy and Resources), UCL School of Energy and Resources, Australia.
Best Three Minute Thesis, UCL Australia Graduate Research Conference 2012.
Uncontrolled penetration of wind capacity into the Australian electricity network could result in increased emissions and production costs in the system, according to my capacity expansion planning (CEP) modelling.
CEP selects the optimal mix from competing generation options, but requires a shift from orthodox methods proven to be effective in the past. My thesis assessed the viability of an exhaustive method of modelling demand in CEP (with chronology retained), against the backdrop of ongoing enhancements in computing performance. The traditional modelling of load demand using Load Duration Curve approximation was also measured against increasing level of intermittent generation, using a South Australian electricity model.
While I found uncontrolled penetration of wind could force up costs and emissions, the more detailed chronological method was better able to manage such unfavourable investment paths.