Regional power system optimization based on analysis of levying taxes on CO2 and atmospheric pollutants emission
WANG Lin-rui, CHAI Miao, WANG Ling-zhi, LI Wei
Ministry of Education Key Laboratory of Regional Energy and Environmental Systems Optimization, North China Electric Power University, Beijing 102206, China
In order to optimize the power system and promote energy conservation and emission reduction, an inexact optimization method was combined with the regional power system model for the power system planning: to minimize the system cost as the objective function; to set up the resource quantity, the power supply and demand balance, and the emission caps as constraints refer to the relevant national policies and standards; to optimize the power generation quantity, purchased electricity, the total amount of CO2 and air pollutant emission; in order to explore the effect of emission reductions and the feasibility of levying taxes, the corresponding scenario analysis for levying emission taxes on air pollutants and carbon would be set up. The research took Zibo City as a case study, with the increasing probability of violating system constraints, the optimized thermal power generation quantity would increase, the renewable power generation quantity would achieve the goals of 10% total electricity consumption in 3scenarios. Moreover, the purchased electricity would be the complementary power form for the power system; the emission caps of SO2, NOx and PM under the default probability of 0 in scenario 1 would be the best among all scenarios. It denotes that the current standard implemented by Zibo would obtain better results, and the results of total cost in scenarios 2 and 3 would increase significantly. Therefore, based on the case study of Zibo City, levying emission taxes on power system maybe unnecessary.
王琳瑞, 柴淼, 王灵志, 李薇. 基于CO2和大气污染物排放征税分析的区域电力系统优化[J]. 中国环境科学, 2017, 37(3): 1179-1187.
WANG Lin-rui, CHAI Miao, WANG Ling-zhi, LI Wei. Regional power system optimization based on analysis of levying taxes on CO2 and atmospheric pollutants emission. CHINA ENVIRONMENTAL SCIENCECE, 2017, 37(3): 1179-1187.
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