Power Structure Optimization And Emissions Reduction Potential Evaluation With Learning Curve

With the growth of economy, energy problem has become increasingly prominent. Continued development of the world economy, is likely to result in a rapid growth in the demand for energy accompanying with other things which are likely to coincide with the timing of energy supply shortages. Excessive consumption of fossil fuel causes at the same time an excessive level of green-house gas?GHG? emissions, which over several decades has had significant impacts on climate change and environmental health. Together these pose a serious threat to human survival as well as low carbon of the national economy. In order to mitigate climate change, reducing GHG emissions has become a common pursuit of various countries in the world. Electricity, as the most important form of secondary energy in China, is the main source of CO₂ emissions.In 2012, coal accounts for 71.60 % of the total installed electricity generation capacity, and hydropower,nuclear power, renewable energy respectively account for 21.77%, 1.12 % and 5.51 %. From this we can find that the unreasonable structure of power generation is not only detrimental to the sustainability of electric power development, but also will cause serious environmental pollution problems, therefore, it is necessary to develop and promote alternative energy sources that ensure energy security of China without increasing environmental impacts.Based on the power generation structure of China, this research collected relative cost and emission data and modeled the power generation structure of China, and established the power system model of China and the renewable power generation cost model by corporating the learning curve. The power system of China was established with the objective to optimize the power generation structure by minimizing the overall cost to meet energy consumption needs CO₂ emission. The renewable power generation cost model used the learning curve to specify the trend by which the power generation cost drops with policy change and technological progress. The paper set some scenarios and analyzed China's future energy mix and emissions reduction potential. The result shows that coal-based plants are the dominant electricity generator followed by hydro power from 2010 to 2040. The introduction of the CO₂ emission reduction targets and carbon taxes both shift energy production technologies away from high carbon content fossil-fuels towards low carbon content fossil-based and renewable energies. In the CO₂ emission reduction targets scenarios, IGCC plants, IGCC plants with CCS, SC plants and USC plants are better exploited, but in the scenarios with carbon tax, the renewable energies have better development. In the scenario of shale gas and applicaition of IGCC and IGCC with CCS, the two tachnologies get better development respectively. Perhaps unsurprisingly, all options result in a net increase in total production costs.The policy of CO₂ emission reduction targets turns out to be more effective in the early years, while the carbon taxes will be more effective after it becomes technologically mature in the later periods. So the government may implement the policies for emission reductions or carbon taxes according to their targets for promoting specific technologies in order to promote the development of new technology and renewable power, and then reduce CO₂ emissions.