Xingning Han, Xinyu Chen, Michael B. McElroy, Shiwu Liao, Chris P. Nielsen, and Jinyu Wen. 2019. “Modeling Formulation and Validation for Accelerated Simulation and Flexibility Assessment on Large Scale Power Systems under Higher Renewable Penetrations.” Applied Energy, 237, Pp. 145-154. Publisher's VersionAbstract
Deploying high penetration of variable renewables represents a critical pathway for decarbonizing the power sector. Hydro power (including pumped-hydro), batteries, and fast responding thermal units are essential in providing system flexibility at elevated renewable penetration. How to quantify the merit of flexibility from these sources in accommodating variable renewables, and to evaluate the operational costs considering system flexibility constraints have been central challenges for future power system planning. This paper presents an improved linear formulation of the unit commitment model adopting unit grouping techniques to expedite evaluation of the curtailment of renewables and operational costs for large-scale power systems. All decision variables in this formulation are continuous, and all chronological constraints are formulated subsequently. Tested based on actual data from a regional power system in China, the computational speed of the model is more than 20,000 times faster than the rigorous unit commitment model, with less than 1% difference in results. Hourly simulation for an entire year takes less than 3 min. The results demonstrate strong potential to apply the proposed model to long term planning related issues, such as flexibility assessment, wind curtailment analysis, and operational cost evaluation, which could set a methodological foundation for evaluating the optimal combination of wind, solar and hydro investments.
2019 Mar 07

China and Asia in a Changing Climate: Natural Science for the Non-Scientist

12:15pm to 1:45pm


CGIS South S020, Belfer Case Study Room, 1730 Cambridge St., Cambridge, MA

Asia CenterPanelists:

  • Professor John Holdren, Teresa and John Heinz Professor of Environmental Policy, Harvard Kennedy School (HKS) and Department of Earth and Planetary Sciences, Harvard University; Co-Director of Science, Technology, and Public Policy Program, HKS; former Science Advisor to President Barack Obama and former Director of the White House Office of Science and Technology Policy
  • Professor Peter Huybers, Department of Earth and Planetary Sciences, Harvard University, and Harvard John A. Paulson School of Engineering and Applied Sciences
  • Professor Elsie SunderlandGordon McKay Professor of Environmental Chemistry, Harvard John A. Paulson School of Engineering and Applied Sciences and Harvard T.H. Chan School of Public Health
  • Professor Steve Wofsy, Abbott Lawrence Rotch Professor of Atmospheric and Environmental Science, Department of Earth and Planetary Sciences, Harvard University, and Harvard John A. Paulson School of Engineering and Applied Sciences

Chair: Professor Mike McElroy, Gilbert Butler Professor of Environmental Studies, Department of Earth and Planetary Sciences, Harvard University, and Harvard John A. Paulson School of Engineering and Applied Sciences; Chair, Harvard-China Project on Energy, Economy and Environment... Read more about China and Asia in a Changing Climate: Natural Science for the Non-Scientist

Xiaolin Guo, Mun Sing Ho, Liangzhi You, Jing Cao, Yu Fang, Taotao Tu, and Yang Hong. 2018. “Industrial Water Pollution Discharge Taxes in China: A Multi-Sector Dynamic Analysis.” Water, 10, 12, Pp. 1742. Publisher's VersionAbstract
We explore how water pollution policy reforms in China could reduce industrial wastewater pollution with minimum adverse impact on GDP growth. We use a multi-sector dynamic Computable General Equilibrium (CGE) model, jointly developed by Harvard University and Tsinghua University, to examine the long-term impact of pollution taxes. A firm-level dataset of wastewater and COD discharge is compiled and aggregated to provide COD-intensities for 22 industrial sectors. We simulated the impact of 4 different sets of Pigovian taxes on the output of these industrial sectors, where the tax rate depends on the COD-output intensity. In the baseline low rate of COD tax, COD discharge is projected to rise from 36 million tons in 2018 to 48 million in 2030, while GDP grows at 6.9% per year. We find that raising the COD tax by 8 times will lower COD discharge by 1.6% by 2030, while a high 20-times tax will cut it by 4.0%. The most COD-intensive sectors—textile goods, apparel, and food products—have the biggest reduction in output and emissions. The additional tax revenue is recycled by cutting existing taxes, including taxes on profits, leading to higher investment. This shift from consumption to investment leads to a slightly higher GDP over time.
Meng Gao, Gufran Beig, Shaojie Song, Hongliang Zhang, Jianlin Hu, Qi Ying, Fengchao Liang, Yang Liu, Haikun Wang, Xiao Lu, Tong Zhu, Gregory Carmichael, Chris P. Nielsen, and Michael B. McElroy. 2018. “The Impact of Power Generation Emissions on Ambient PM2.5 Pollution and Human Health in China and India.” Environment International, 121, Part 1, Pp. 250-259. Publisher's VersionAbstract

Emissions from power plants in China and India contain a myriad of fine particulate matter (PM2.5, PM≤2.5 micrometers in diameter) precursors, posing significant health risks among large, densely settled populations. Studies isolating the contributions of various source classes and geographic regions are limited in China and India, but such information could be helpful for policy makers attempting to identify efficient mitigation strategies. We quantified the impact of power generation emissions on annual mean PM2.5 concentrations using the state-of-the-art atmospheric chemistry model WRF-Chem (Weather Research Forecasting model coupled with Chemistry) in China and India. Evaluations using nationwide surface measurements show the model performs reasonably well. We calculated province-specific annual changes in mortality and life expectancy due to power generation emissions generated PM2.5 using the Integrated Exposure Response (IER) model, recently updated IER parameters from Global Burden of Disease (GBD) 2015, population data, and the World Health Organization (WHO) life tables for China and India. We estimate that 15 million (95% Confidence Interval (CI): 10 to 21 million) years of life lost can be avoided in China each year and 11 million (95% CI: 7 to 15 million) in India by eliminating power generation emissions. Priorities in upgrading existing power generating technologies should be given to Shandong, Henan, and Sichuan provinces in China, and Uttar Pradesh state in India due to their dominant contributions to the current health risks.



Hang Yu

Fellow, Harvard-China Project
Ph.D. Student, School of Economics, Peking University
Pierce Hall G2B, 29 Oxford St., Cambridge, MA