Energy transition and climate policy selection with stochastic demand: Evidence from Australian electricity generation expansion planning
校准了一个电力扩张规划模型，模拟澳大利亚国家电力市场在需求不确定性下从化石燃料转向可再生能源的成本最优路径，并比较碳税与排放上限两种气候政策的效果。
Australia’s Nationally Determined Contributions made in 2022, following the Paris Agreement in 2016, include a pledge to reduce greenhouse gas emissions to 43% below 2005 levels by 2050. This paper calibrates a General Expansion Planning (GEP) model to the Australian National Electricity Market (NEM) to forecast cost-efficient ways to meet future electricity demand while transitioning from fossil-fuel-based technologies to renewable energy sources. The model incorporates uncertainty in the Australian electricity market and employs the stochastic dual dynamic programming (SDDP) methodology to devise plans for the period from 2020 to 2050. By comparing the effects of carbon taxes and emissions caps, the study highlights differences between states with substantial initial coal resources and those without coal-powered plants. The imposition of carbon taxes or capped emissions can reduce emissions by substantial amounts, with carbon taxes being more cost-effective than emission caps for most states. The model predicts that without climate policies, emissions in NEM will initially fall by 32% from 2020 to 2035, but will then rise to 92% of 2020 levels by 2050. Conversely, implementing carbon taxes or emissions caps can reduce emissions by nearly 50% from 2020 to 2050. • We develop a generation expansion planning (GEP) model for the Australian electricity market. • We employ the stochastic dual dynamic programming (SDDP) methodology to find cost-minimising plans under demand uncertainty. • Our model considers five electricity generation technologies that use fossil fuel or renewables. • We incorporate climate policies to analyse the trade-off between cost and emission reduction. • Carbon taxes are more cost-effective than emission caps for most Australian states.