Optimization of China's electric power sector targeting water stress and carbon emissions. (1st August 2020) - Zhang, Yiyi; Wang, Jiaqi; Zhang, Linmei; Liu, Jiefeng; Zheng, Hanbo; Fang, Jiake; Hou, Shengren; Chen, Shaoqing
Highlights: A multi-objective optimization model is established for electric power sector. Water consumption and carbon emissions from power generation are quantified. Tradeoffs between water and carbon in electricity mix are evaluated. Strategies for optimizing electricity mix and tasks distribution are proposed. Abstract: Electric power sector is a significant water consumer and a major source of carbon emissions in China. Optimizing electricity mix is critical for maintaining a stable energy supply while addressing water stress and carbon emissions associated with power generation. In this study, we developed an optimization model with the dual objectives of mitigating the risk of water scarcity and cutting carbon emissions, by adjusting the electricity mix and power generation tasks of provincial grids. Using the weighted sum method, the optimization of grids is simulated based on a scenario analysis within specified equality and boundary constraints. We quantified the impact of the spatial distribution of thermal power and power generated from clean energies (hydropower, wind power, nuclear power, and solar power) on virtual water consumption and carbon emissions at a provincial level in China. The results suggested that transferring part of power generation tasks from water-deficient areas (e.g., Shandong and Jiangsu) to water-sufficient areas (e.g., Yunnan and Guangdong) could significantly mitigate water scarcity. Water consumption of power generation could be reduced by 34.7% in highly water-deficient areas if a water-prioritized scenario is implemented. Carbon emissions related to power generation could be reduced nationwide under a carbon-prioritized scenario and a water-carbon-balanced scenario. This model could provide a synergistic perspective for improving regional water and carbon performances based on changes in electricity mix.