China's electricity system is responsible for about half of the country's energy-related CO2 emissions, making its decarbonization crucial for global climate goals. Under the Paris Agreement, China committed to peaking its CO2 emissions and supplying 20% of its energy demand from non-fossil sources by 2030. However, many studies have underestimated the dramatic cost decrease of renewable energy and storage. This study incorporates recent cost trends to explore more ambitious pathways to decarbonizing China's power system by 2030, investigating how these trends can reshape the power system. The rapid cost reductions in solar photovoltaics (PV), wind, and battery storage (77%, 35%, and 85% respectively between 2010 and 2018) present new possibilities for widespread renewable energy adoption and significant decarbonization. The research addresses key questions: How would China's power system change with rapid renewable cost decreases under stricter CO2 emission targets? What are the costs of these changes? How would these changes affect China's regional power development and transmission patterns? This paper aims to reveal the implications of these cost decreases on power systems and offer new perspectives on the clean power transition.

Existing literature outlining strategies for China to achieve high non-emitting generation by 2050 has often not adequately captured the dramatic decrease in the costs of renewable energy and storage. Reports like the World Energy Outlook and the International Energy Outlook have underestimated the development of renewables. This study aims to address this gap by incorporating the recent downward trends in renewable energy costs into power sector models.

The study updated the SWITCH-China model, a capacity expansion model optimizing electricity production and delivery costs. Four scenarios were developed for 2030: (1) Business as Usual (BAU), assuming continued current policies and moderate cost decreases; (2) Low-cost Renewables (R), assuming continued rapid cost decreases for renewables and storage; (3) Carbon Constraints (C50), imposing a 50% reduction in carbon emissions from 2015 levels; and (4) Deep Carbon Constraints (C80), aiming for an 80% reduction in carbon emissions. The model uses hourly data on a provincial scale, optimizing long-term investment and short-term grid operation, considering reliability, operational constraints, resource availability, and climate policies. Cost projections from LBNL and NREL were used, with capital costs for solar, wind, and storage following NREL's ATB projections. Other costs followed the original SWITCH-China assumptions. CO2 accounting methods and electricity demand projections are detailed in the supplementary materials.

The modeling analysis revealed that if renewable cost trends continue, 62% of China's electricity could come from non-fossil sources by 2030 under the R scenario, at a cost 11% lower than the BAU scenario. A 50% reduction in 2015 carbon emissions (C50 scenario) is achievable at a cost 6% lower than BAU. An 80% reduction (C80 scenario) is technically feasible but would cost 21% more than BAU. The R scenario would significantly increase wind and solar capacity, reduce natural gas needs, and moderately decrease coal capacity. The C50 and C80 scenarios would lead to much greater coal capacity reductions, offset by substantial increases in solar and wind capacity. Coal-based generation would decrease by 30% under R, nearly 50% under C50, and to about 10% under C80. The R and C50 scenarios demonstrate operational manageability in meeting demands with additional battery storage and natural gas generation. The R scenario would reduce carbon emissions by 30%, resulting in lower power costs compared to BAU. The C50 scenario would halve 2015 carbon emissions while maintaining lower power costs than BAU. The R scenario would shift the power system cost structure from fuel-intensive to capital-intensive. Regional disparities exist; solar capacities would concentrate in northwestern provinces, while wind capacity would be more widely distributed. Significant new transmission infrastructure would be necessary to transfer renewable energy from resource-rich areas to demand centers. Sensitivity analyses showed consistent structural transformation even with 20% increases in demand or renewable capital costs.

The findings demonstrate that accelerated decarbonization of China's power sector is both technically feasible and economically beneficial. The dramatic decrease in renewable energy costs creates pathways to significantly reduce carbon emissions while lowering overall power costs. The study highlights the importance of policy interventions, such as target setting and cost reduction mechanisms, to further enhance the transition. Reforming the power market to facilitate interregional power trading and improved renewable energy integration can mitigate the challenges associated with variable renewable energy sources. The consistent findings across sensitivity analyses suggest the robustness of the results. This research contributes significant insights into China’s power sector transition, offering a valuable model for other countries aiming for rapid decarbonization.

This study demonstrates the significant potential for accelerating the decarbonization of China's power system by leveraging the rapid cost reductions in renewable energy technologies. The findings suggest that ambitious decarbonization goals are achievable at competitive costs, highlighting the economic and environmental benefits of a rapid transition. Future research could explore the impact of offshore wind and demand response technologies, particularly on the Eastern grid's reliance on imports, and further investigate the challenges and opportunities associated with large-scale storage deployment.

The study relies on modeling assumptions regarding future technology costs, electricity demand, and policy implementation. Uncertainties in these factors could influence the exact outcomes. The model's accuracy depends on the underlying data and assumptions used in the SWITCH-China model. The large-scale deployment of storage technologies presents significant logistical and economic challenges that warrant further investigation.