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Sustainability 2017, 9(11), 1942; doi:10.3390/su9111942

A Stochastic Optimization Model for Carbon Mitigation Path under Demand Uncertainty of the Power Sector in Shenzhen, China

1
Key Laboratory for Urban Habitat Environmental Science and Technology, School of Environment and Energy, Peking University Shenzhen Graduate School, Shenzhen 518055, China
2
College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
*
Author to whom correspondence should be addressed.
Received: 20 September 2017 / Revised: 14 October 2017 / Accepted: 19 October 2017 / Published: 26 October 2017
(This article belongs to the Special Issue Power System and Sustainability)
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Abstract

In order to solve problems caused by climate change, countries around the world should work together to reduce GHG (greenhouse gas) emissions, especially CO2 emissions. Power demand takes up the largest proportion of final energy demand in China, so the key to achieve its goal of energy-saving and emission reduction is to reduce the carbon emissions in the power sector. Taking Shenzhen as an example, this paper proposed a stochastic optimization model incorporating power demand uncertainty to plan the carbon mitigation path of power sector between 2015 and 2030. The results show that, in order to achieve the optimal path in Shenzhen’s power sector, the carbon mitigation technologies of existing coal and gas-fired power plants will be 100% implemented. Two-thirds and remaining one-third of coal-fired power plant capacities are going to be decommissioned in 2023 and 2028, respectively. Gas-fired power, distributed photovoltaic power, waste-to-energy power and CCHP (Combined Cooling, Heating, and Power) are going to expand their capacities gradually. View Full-Text
Keywords: stochastic optimization; carbon mitigation path; the power sector stochastic optimization; carbon mitigation path; the power sector
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Hu, G.; Ma, X.; Ji, J. A Stochastic Optimization Model for Carbon Mitigation Path under Demand Uncertainty of the Power Sector in Shenzhen, China. Sustainability 2017, 9, 1942.

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