Inherent spatiotemporal uncertainty of renewable power in China.

Solar and wind resources are vital for the sustainable energy transition. Although renewable potentials have been widely assessed in existing literature, few studies have examined the statistical characteristics of the inherent renewable uncertainties arising from natural randomness, which is inevitable in stochastic-aware research and applications. Here we develop a rule-of-thumb statistical learning model for wind and solar power prediction and generate a year-long dataset of hourly prediction errors of 30 provinces in China. We reveal diversified spatiotemporal distribution patterns of prediction errors, indicating that over 60% of wind prediction errors and 50% of solar prediction errors arise from scenarios with high utilization rates. The first-order difference and peak ratio of generation series are two primary indicators explaining the uncertainty distribution. Additionally, we analyze the seasonal distributions of the provincial prediction errors that reveal a consistent law in China. Finally, policies including incentive improvements and interprovincial scheduling are suggested.

Towards carbon neutrality: A study on China's long-term low-carbon transition pathways and strategies.

As the world's biggest carbon dioxide (CO2) emitter and the largest developing country, China faces daunting challenges to peak its emissions before 2030 and achieve carbon neutrality within 40 years. This study fully considered the carbon-neutrality goal and the temperature rise constraints required by the Paris Agreement, by developing six long-term development scenarios, and conducting a quantitative evaluation on the carbon emissions pathways, energy transformation, technology, policy and investment demand for each scenario. This study combined both bottom-up and top-down methodologies, including simulations and analyses of energy consumption of end-use and power sectors (bottom-up), as well as scenario analysis, investment demand and technology evaluation at the macro level (top-down). This study demonstrates that achieving carbon neutrality before 2060 translates to significant efforts and overwhelming challenges for China. To comply with the target, a high rate of an average annual reduction of CO2 emissions by 9.3% from 2030 to 2050 is a necessity, which requires a huge investment demand. For example, in the 1.5 °C scenario, an investment in energy infrastructure alone equivalent to 2.6% of that year's GDP will be necessary. The technological pathway towards carbon neutrality will rely highly on both conventional emission reduction technologies and breakthrough technologies. China needs to balance a long-term development strategy of lower greenhouse gas emissions that meets both the Paris Agreement and the long-term goals for domestic economic and social development, with a phased implementation for both its five-year and long-term plans.

Cost increase in the electricity supply to achieve carbon neutrality in China.

The Chinese government has set long-term carbon neutrality and renewable energy (RE) development goals for the power sector. Despite a precipitous decline in the costs of RE technologies, the external costs of renewable intermittency and the massive investments in new RE capacities would increase electricity costs. Here, we develop a power system expansion model to comprehensively evaluate changes in the electricity supply costs over a 30-year transition to carbon neutrality. RE supply curves, operating security constraints, and the characteristics of various generation units are modelled in detail to assess the cost variations accurately. According to our results, approximately 5.8 TW of wind and solar photovoltaic capacity would be required to achieve carbon neutrality in the power system by 2050. The electricity supply costs would increase by 9.6 CNY¢/kWh. The major cost shift would result from the substantial investments in RE capacities, flexible generation resources, and network expansion.