MC-ELECT (Monte Carlo Electricity Generation Portfolio Assessment Tool)

With the MC-ELECT modelling tool we evaluate the potential impacts of an energy, climate policy or policy mix on future generation investment in China’s electricity sector in 2030. This modelling tool extends the conventional load duration curve (LDC) optimal generation mix methods by incorporating Monte Carlo Simulation (MCS) to formally accounts for key uncertainties in generation investment and planning decision-making, including technology costs, carbon pricing and air emissions control. The tool uses the Mean Variance Portfolio (MVP) Theory to compare trade-offs between different future generation portfolios in terms of expected electricity generation costs and their associated uncertainties as well as their environmental impacts based on the calculated expected emissions of carbon emissions and local air pollutants (NOx, SO2, PM2.5).