Spatial and temporal uncertainty in the technological pathway towards a low-carbon power industry: A case study of China

摘要

The low-carbon transition of power industry plays a vital role in China's energy system revolution. Both policy support and cost reductions have greatly driven the development of renewable energy technologies, especially wind and solar power generation technologies. Considering the cost uncertainty of renewables, we developed a National Energy Technology-Power model to assess the possible low-carbon transition pathways for six regional power industries using four renewable energy cost change scenarios. Resource endowments and technology developments trends were also considered to achieve an effective and coordinated utilization of various resources. The results show that declining renewable energy costs have a great impact on the spatial and temporal development of power generation technologies, and on the interregional clean power transmission. If the investment costs of renewable energy technologies continue to decline at a high speed and the renewables could be dramatically developed, the CO2 emissions of China's power industry is expected to peak at 3.12 GtCO(2) in 2026. Accordingly, the capacity share of renewable energy technologies in regional power industries would exceed 50% except in East China, and the total installed coal-fired technology capacity would fall to 760.2 GW in 2050. In addition, to promote the optimal allocation of resources, the total amounts of interregional clean power transmission are suggested to be 416 TWh in 2035 and 587 TWh in 2050, i.e., 4.9% and 5.5% of the total amount of power generation, respectively. 106 TWh of wind power is expected to be exported from Northwest to Center and East regions in 2050; and 112 TWh of solar power is suggested to be exported from North to Center, East and South regions. The Northwest region is the largest exporter of clean power while the East region is the main importer. These conclusions could support the regional plan of power transmission network. (C) 2019 Elsevier Ltd. All rights reserved.