The SWITCH-China Model

The SWITCH-China Model was first developed in the Renewable and Appropriate Energy Laboratory at UC Berkeley as Dr. Gang He's Ph.D. dissertation. SWITCH-China is introduced to address the challenges facing China's clean power transition: soaring variable renewable energy penetration and curtailment; pressing air pollution, water stress, climate change, and human health challenges by coal consumption. 

SWITCH, which is a loose acronym for investment in solar, wind, hydro, and conventional technologies, is an optimization model that has the objective function of minimizing the cost of producing and delivering electricity based on projected demand through the construction and retirement of various power generation, storage, and transmission options available currently and at future target dates. The SWITCH-China model provides high resolution in both the temporal and spatial dimensions, to simulate the effect of the dramatically decreasing cost for incorporating renewable energy into the power grid. SWITCH-China runs on a provincial scale and utilizes hourly data to simulate and optimize power system planning based on operational constraints. SWITCH optimizes both the long-term investment and short-term operation of the grid. The model incorporates a combination of current and advanced grid assets. Optimization is subject to reliability, constraints on operations, and resource availability, as well as on current and potential climate policies and environmental regulations.

SWITCH-China: Modeling the Transition of the World's Largest Power Sector

Core Publication

Gang He, Anne-Perrine Avrin, James H. Nelson, Josiah Johnston, Ana Mileva, Jianwei Tian, and Daniel M. Kammen. 2016. SWITCH-China: A Systems Approach to Decarbonizing China’s Power System. Environmental Science and Technology. 50(11):5467–5473. doi: 10.1021/acs.est.6b01345. [pdf] [Supporting Information] [Poster]

He, Gang, Jiang Lin, Froylan Sifuentes, Xu Liu, Nikit Abhyankar, and Amol Phadke. 2020. Rapid Cost Decrease of Renewables and Storage Accelerates the Decarbonization of China’s Power System. Nature Communications 11 (1): 2486.  doi: 10.1038/s41467-020-16184-x [pdf] [SBU News] [LBL News] [CarbonBrief Headline] [InsideClimateNews] [E&E News/ClimateWire] [Forbes] [Nature News]

Zhang, Chao, Gang He, Josiah Johnston, and Lijin Zhong. 2021.  Long-Term Transition of China’s Power Sector under Carbon Neutrality Target and Water Withdrawal Constraint. Journal of Cleaner Production 329: 129765. doi: 10.1016/j.jclepro.2021.12976

Peng, Liqun, Denise L. Mauzerall, Yaofeng D. Zhong, and Gang He. 2023. Heterogeneous Effects of Battery Storage Deployment Strategies on Decarbonization of Provincial Power Systems in China. Nature Communications 14 (1): 4858. https://doi.org/10.1038/s41467-023-40337-3.

Related Publication

Peng, Liqun, Yang Guo, Shangwei Liu, Gang He, and Denise L. Mauzerall*. 2024. Subsidizing Grid-Based Electrolytic Hydrogen Will Increase Greenhouse Gas Emissions in Coal Dominated Power Systems. Environmental Science & Technology. 58(12): 5187–5195. doi: 10.1021/acs.est.3c03045. [pdf]

Luo, Qian, Fernando Garcia-Menendez, Haozhe Yang, Ranjit Deshmukh, Gang He, Jiang Lin*, and Jeremiah X. Johnson. 2023. The Health and Climate Benefits of Economic Dispatch in China’s Power System. Environmental Science & Technology 57(7):  2898–2906. doi: 10.1021/acs.est.2c05663. [pdf]

Li, Bo, Ziming Ma, Patricia Hidalgo-Gonzalez, Alex Lathem, Natalie Fedorova, Gang He, Haiwang Zhong, Minyou Chen, and Daniel M. Kammen. 2021. Modeling the Impact of EVs in the Chinese Power System: Pathways for Implementing Emissions Reduction Commitments in the Power and Transportation Sectors. Energy Policy 149: 111962. doi: 10.1016/j.enpol.2020.111962. [pdf]

Jianlin Hu, Lin Huang, Mindong Chen, Gang He, Hongliang Zhang. 2017. Impacts of Power Generation on Air Quality in China - Part II: Future Scenarios. Resources, Conservation and Recycling. 121:115–127. doi:10.1016/j.resconrec.2016.04.011 [pdf]

Gang He, Daniel M. Kammen. 2016. Where, when and how much solar is available? A provincial-scale solar resource assessment for China. Renewable Energy. 85:74-82. doi: 10.1016/j.renene.2015.06.027. [pdf]

Gang He, Daniel M. Kammen. 2014. Where, when and how much wind is available? A provincial-scale wind resource assessment for China. Energy Policy. 74:116-122. doi: 10.1016/j.enpol.2014.07.003. [pdf]

Data

Provincial solar potential data

Provincial wind potential data

Electricity demand projections by province, 2020, 2030, and 2050

Hydro-power big plants

Ultra-high voltage transmission lines

Please cite corresponding papers to use the data.

GIS data

Contributors

Gang He leads the efforts to aggregate the data, develop the model, and write the paper. Gang is working with collaborators to integrate water and other nexus constraints into and technological capabilities into SWITCH-China model. Anne-Perrine Avrin contributed to the nuclear module, data processing, and paper writing. James H. Nelson, Josiah Johnston, Ana Mileva are the core developer of SWITCH-WECC model. James Nelson contributed significantly to an earlier version of SWITCH-China. Josiah Johnston facilitates greatly the data management and processing. Jianwei Tian developed an Excel-based 2020 China Power model while visiting RAEL which is a good resource of SWITCH-China. Daniel M. Kammen is the director of RAEL at UC Berkeley, who envisions, supervises, and supports the umbrella SWITCH model family at RAEL.

Collaborators

Wenjia Cai, Huibin Du, Bo Li, Jiang Lin,  Qian Luo, Froylan Sifuentes, Liqun Peng, Hongliang Zhang, Chao Zhang, Hongyang Zou.

Acknowledgment

Dr. Matthias Fripp is pioneering in creating the SWITCH model. Xuxuan Xie, Hongyou Lu, Daniel Sanchez, Diego Barido, Shiyu Huang, Kate Yu, Shuyu Yang, Xiao Su, Yu Chen, Hua Yuan, and Nan Yuan for their help with some of the data and charts used in the model. We thank Dr. Zhaoguang Hu and Prof. Shiqiu Zhang for the advice. We thank the Karsten Family Foundation Endowment for the support of the Renewable and Appropriate Energy Laboratory. We also thank 3TIER for the data support.