Integrated Planning for Regional Electric Power System Management with Risk Measure and Carbon Emission Constraints: A Case Study of the Xinjiang Uygur Autonomous Region, China
With the carbon reduction targets being set in the Paris Agreement on Climate Change, China is facing great pressure to meet its emission reduction commitment. The electric power industry as the major source of carbon emissions needs to be a focus. However, the uncertainty of power systems, the risk...
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| Published in | Energies (Basel) Vol. 12; no. 4; p. 601 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Basel
MDPI AG
14.02.2019
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1996-1073 1996-1073 |
| DOI | 10.3390/en12040601 |
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| Abstract | With the carbon reduction targets being set in the Paris Agreement on Climate Change, China is facing great pressure to meet its emission reduction commitment. The electric power industry as the major source of carbon emissions needs to be a focus. However, the uncertainty of power systems, the risk of reducing emissions and the fuzziness of carbon capture technology popularization rate and carbon reduction targets makes previous planning methods unsatisfactory for current planning. This paper establishes an interval fuzzy programming with a risk measure model which takes carbon capture technology and carbon reduction targets into account, to ensure that the complex electric management system achieves the best developmental state. It was concluded that in order to reduce carbon emissions, wind power and hydropower would be the best choices, and coal-fired power would be the suboptimal choice, and solar power would play a complementary role. Besides, decision makers should put much more effort into promoting and improving carbon capture technology instead of simply setting emission reduction targets. The non-synchronism of the downward trend in carbon emissions per unit of electricity generation and electric power industry total carbon emissions need to be taken seriously. |
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| AbstractList | With the carbon reduction targets being set in the Paris Agreement on Climate Change, China is facing great pressure to meet its emission reduction commitment. The electric power industry as the major source of carbon emissions needs to be a focus. However, the uncertainty of power systems, the risk of reducing emissions and the fuzziness of carbon capture technology popularization rate and carbon reduction targets makes previous planning methods unsatisfactory for current planning. This paper establishes an interval fuzzy programming with a risk measure model which takes carbon capture technology and carbon reduction targets into account, to ensure that the complex electric management system achieves the best developmental state. It was concluded that in order to reduce carbon emissions, wind power and hydropower would be the best choices, and coal-fired power would be the suboptimal choice, and solar power would play a complementary role. Besides, decision makers should put much more effort into promoting and improving carbon capture technology instead of simply setting emission reduction targets. The non-synchronism of the downward trend in carbon emissions per unit of electricity generation and electric power industry total carbon emissions need to be taken seriously. Though these routes can reduce carbon emissions to some extent, some problems also exist such as the expensive carbon capture reagents and the insufficient quantity of energy resource [10,11]. [...]the choice between the methods of generating electricity and carbon capture technology is very important to balance and relieve the great conflict between the goal of carbon reduction and the growing electricity demands, in order to get the optimal reduction target and economy. Specifically, the flexibility of constraint conditions and the fuzziness of system objective would be denoted by fuzzy number set. [λ±] as the degree of membership associated with the degree of satisfaction would represent the “fuzzy constraint” or “fuzzy object”. λ=min{μG,μC1,μC2,^μCm} denotes the membership level. [...]the interval fuzzy programming model would be converted as follows: maxλ± subject to: C± X±≤λ± f++(1−λ±)f− A± X±≥B−+(1−λ±)(B+−B−) X±≥0 0≤λ±≤1 λ± denotes the control variable of fuzzy satisfactory degree in fuzzy constraint or fuzzy object. f+ and f− represent the upper and lower bounds of the objective of expectations value set by decision makers. 2.2. [...]of being landlocked, the study area has a temperate continental arid climate which has the characteristics of low rainfall, evaporation, and large temperature differences, and the annual natural precipitation in Xinjiang is less than 150 mm. Though the weather conditions are poor, Xinjiang is rich in energy and mineral resources, and also has abundant renewable energy resources, particularly wind and solar energy [27]. [...]a more refined model, including nuclear power, offshore wind power, refuse-burning power and so on, will be established in the future study. |
| Author | Huang, Guohe Xia, Dehong Xie, Yulei Wang, Han Lu, Wentao Fu, Zhenghui |
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| Title | Integrated Planning for Regional Electric Power System Management with Risk Measure and Carbon Emission Constraints: A Case Study of the Xinjiang Uygur Autonomous Region, China |
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