China has become the “world’s factory” and lifted up several millions of people out of poverty through decades of economic reforms. However, this socio-economic development, fueled by coal, has heavily impacted the energy landscape and the environment. Today, China is both the global largest polluter and largest developer of clean energy systems. The unprecedented extent of the country’s power sector challenges and opportunities requires to refine current capacity planning models to better inform policy making. Focusing on climate urgency, this study contributes to filling this need by proposing new modeling frameworks. These models offer a fine-grain analysis of China’s power sector decarbonization potential, by grounding long-term electricity mix expansion planning into inter-sectoral development. Using large datasets of technical, economic and social factors, levers of the clean power transition are analyzed by quantifying and comparing their decarbonization potential, technical feasibility, and cost efficiency over time and across regions and resources. The first lever lies in the coordinated expansion of a low-carbon electricity grid, resilient to renewable energy intermittency and disparities in space and time between energy resources and demand. The high-resolution model SWITCH-China is used to explore optimal capacity expansion pathways for China’s electricity mix under various low-carbon policies. Results show that, while natural gas can become a bridge between the current coal-dominated grid and a future clean electricity mix, a deep decarbonization scenario mostly relies on the concomitant deployment of nuclear—inland, encouraged by advanced nuclear technology development—, wind, solar, energy storage, coal with carbon capture and storage, and an expanded transmission network. Meeting this long-term decarbonization goal increases electricity costs significantly compared to an unconstrained scenario. A modular approach, adaptable to traditional cost-minimizing tools used by planning agencies, is proposed to reduce uncertainty of future technological costs as well as costs resulting from electricity generation intermittency. The model finds that there exist alternatives to the least-cost strategy, which present slightly higher overall electricity costs, but much lower risks. In particular, given the predominance of fossil fuel in the current electricity mix, the deployment of low-carbon systems decreases the overall risk on future costs through diversification, even by accounting for increased operational complexity resulting from renewable energy intermittency. The second lever is the integration of linkages between power and water supply into the optimization framework, as China currently faces both severe water shortages and severe water pollution. Results show that, while total costs of the South to North Water Transfer Project are 2.5 times lower than nuclear desalination, the latter emits six times less CO2. In addition, fresh water from nuclear desalination is shown to be affordable even to the poorest households. Challenges posed by the construction and operation of interregional water diversion suggest that it should only be used in dry inland areas, while low-carbon desalination could be developed at larger scale near coastal economic centers. The DESEC model is developed to explore controllable desalination, powered by nuclear, wind, or solar energy, as a mean to alleviate water scarcity in coastal regions while enabling the wide deployment of a low-curtailment clean power base. Desalination, used as a deferrable load, can transform two low-value products, seawater and excess power from non-dispatchable energies, in a high-value product, fresh water. The DESEC model finds that the North China Grid region’s entire water deficit—61.4 billion m3—can be met entirely by deferrable desalination with a total cost of about $1.5/m3, less than the global average water price. Analyses conclude that there exist local alternatives to current national-scale power and water diversion projects, more adapted to regional characteristics, less prone to risks and disruptions. The third lever is the electrification of urban passenger cars, as the sector is poised for massive expansion in the next decade. Findings show that the near-term deployment of electric vehicles cannot be enabled by technology cost decrease alone. If the current impact from favorable policy is maintained but not amplified, CO2 emissions from urban passenger cars will peak around 2040, or ten years past the official 2030 carbon peak target. In fact, although it demonstrates higher costs in the near term, large-scale vehicle electrification is the least-cost, least-CO2-emission pathway for China’s urban transportation expansion in the long run. Generally, model results reveal that massive investment is needed in R&D and infrastructure capacity deployment, and that utilities and institutions must be reformed to manage resources rationally, hand-in-hand. Many of the studied decarbonization options have not reached a mature stage or have not been deployed at utility-scale today, and future work is required to better account for modeling and data uncertainty. Yet, models developed, used and presented in this study all reveal the existence of viable, cost-efficient options to enable meaningful decarbonization while alleviating water scarcity and air pollution. These strategies can only be achieved by combining central-scale coordination with local-scale implementation, to take advantage of China’s diverse and rich territory while minimizing risks. This dissertation provides new approaches for identifying realistic, affordable capacity expansion pathways, rigorously designed by mathematical optimization and large datasets, to reduce CO2 emissions related to the power sector in line with climate targets. The impact that China will have on climate change and its ability to ensure its long-term sustainability will depend on actual decisions and actions undertaken by governmental and private entities. The success of the clean energy transition is contingent on the country’s ability to encourage technological, economic and institutional innovation.

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Published on 01/01/2018

Volume 2018, 2018
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