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A Stochastic Optimization Model for Carbon Mitigation Path under Demand Uncertainty of the Power Sector in Shenzhen, China
- Source :
- Sustainability; Volume 9; Issue 11; Pages: 1942, Sustainability, Vol 9, Iss 11, p 1942 (2017)
- Publication Year :
- 2017
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2017.
-
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.
- Subjects :
- Power station
020209 energy
Geography, Planning and Development
TJ807-830
02 engineering and technology
Management, Monitoring, Policy and Law
TD194-195
Renewable energy sources
0202 electrical engineering, electronic engineering, information engineering
Economics
Coal
GE1-350
stochastic optimization
carbon mitigation path
the power sector
Environmental effects of industries and plants
Renewable Energy, Sustainability and the Environment
business.industry
Photovoltaic system
Environmental engineering
Environmental economics
Power (physics)
Environmental sciences
Climate change mitigation
Base load power plant
Greenhouse gas
Stochastic optimization
business
Subjects
Details
- Language :
- English
- ISSN :
- 20711050
- Database :
- OpenAIRE
- Journal :
- Sustainability; Volume 9; Issue 11; Pages: 1942
- Accession number :
- edsair.doi.dedup.....45158f6e6a1528b1ff793a3b7ae7f49f
- Full Text :
- https://doi.org/10.3390/su9111942