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Planning carbon dioxide mitigation of Qingdao's electric power systems under dual uncertainties
- Yu, L., Li, Y.P., Huang, G.H., Li, Y.F., Nie, S.
- Journal of cleaner production 2016 v.139 pp. 473-487
- Monte Carlo method, carbon, carbon dioxide, climate change, coal, electric power, electricity, greenhouse gas emissions, greenhouses, planning, prediction, uncertainty, urbanization, China
- Coupling with rapid economic development and continual urban expansion, CO2 as the dominant contributor to the greenhouse has aggravated the global climate change, such that achieving the joint goal of increasing electricity demand and mitigating CO2 emission become crucial to plan electric power systems (EPS). Various complexities and uncertainties exist in the real-world EPS problems, which can affect the optimization processes as well as the generated decision schemes. In this study, a two-stage interval-possibilistic programming (TIPP) method is developed for planning carbon emission trading (CET) in the EPS of Qingdao (China), where dual uncertainties expressed as interval-random variables and interval-possibilistic parameters can be handled. Techniques of support vector regression (SVR) and Monte Carlo simulation are used for predicting electricity demand and CO2 emission. Four scenarios corresponding to different CO2-emission permits and CO2-mitigation levels have been analyzed. Results reveal that coal-fired power is the primary CO2-emission emitter, and it tends to the transition to renewable energy-dominated power with the CO2-mitigation levels from 5% to 30% (e.g., contributing to 0.6% increment of renewable energies and [30.8, 33.1] % reduction of treated CO2 emissions). Compared to without CET scheme, CO2 emissions can be reduced about 5% under the CET, demonstrating that CET can help to promote the cleaner production of the local electricity. The findings can help decision makers reallocate carbon permits among different emitters effectively, provide appropriate mitigation plan for CO2-emission, as well as to improve environmental and sustainable EPS planning.