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不确定性电力能源与环境系统优化模型及其应用
Inexact Electric-Energy and Environment System Optimization Model and Its Application

DOI: 10.12677/SD.2019.92018, PP. 129-137

Keywords: 电力能源,污染物减排,区间优化方法,成本分析,不确定性
Electric Power System
, Air-Pollutants Mitigation, Interval Optimization Method, Cost Analysis, Uncertainties

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Abstract:

本研究充分考虑电力能源、环境及经济系统中的不确定性,基于区间不确定优化方法,构建不确定性电力能源与环境系统优化模型(UEEEOM)。所构建的模型可以有效处理系统中表现为区间的不确定信息。本模型在考虑环境约束的基础上,以系统成本最小化为目标,通过对模型求解可以得到资源配置、发电方案;同时,可以定量评估系统的运维、燃料配置、电力扩容以及环境治理成本。模型结果表明:1) 规划期内电力能源结构逐步优化,煤电占比逐步降低,可再生能源比例稳步上升;2) 规划期内,因发电产生的污染物显著下降;3) 系统总成本达到[8.91, 8.69] × 1012元;其中燃料成本达到[3.26, 4.01] × 1012元,电力投资成本[4.51, 4.65] × 1012元,环境治理成本为[192.28, 221.08] × 108元。
Uncertainties information of electric-energy and environment systems was fully considered in this paper. In this study, electric energy and environment decision support-interval optimization method of uncertainty (UEEEOM) was developed based on interval optimization method, which could effectively tackle the uncertainties expressed as interval. The proposed model aimed to achieve a minimized system cost with considering the environment constraints; the results of UEEEOM could generate energy resources allocation, electricity generation schemes, and quantitatively evaluate the costs for energy purchase, maintenance and operation, capacity expansion and environment control. From results of proposed UEEEOM, several finding could be revealed as follows: i) the electric-energy structure would be optimized. The proportion for coal-fired would be decreased and renewable energy power would increase during the planning horizon; ii) the air-pollutants emissions would obviously decease over the planning horizon; iii) the system cost would reach to ¥ [8.91, 8.69] × 1012. Cost for energy purchase, electric capacity expansion and environment treatment was ¥ [3.26, 4.01] × 1012, ¥ [4.51, 4.65] × 1012, ¥ [192.28, 221.08] × 108, respectively.

References

[1]  中国电力年鉴编辑委员会. 中国电力年鉴[M]. 北京: 中国电力出版社, 2015.
[2]  环境保护部. 环境统计年报[M]. 北京: 中国环境科学出版社, 2014.
[3]  赵东阳, 靳雅娜, 张世秋. 燃煤电厂污染减排成本有效性分析及超低排放政策讨论[J]. 中国环境科学, 2016, 36(9): 2841-2848.
[4]  Zhao, Y., Wang, S.X., Nielsen, C.P., Li, X.H. and Hao, J.M. (2010) Establishment of a Data-base of Emission Factors for Atmospheric Pollutants from Chinese Coal-Fired Power Plants. Atmospheric Environment, 44, 1515-1523.
https://doi.org/10.1016/j.atmosenv.2010.01.017
[5]  王姗姗, 徐吉辉, 邱长溶. 能源消费与环境污染的边限协整分析[J]. 中国人口.资源与环境, 2010, 20(4): 69-73.
[6]  Huang, G.H., Baetz, B.W., Patry, G.G., et al. (1994) A Grey Dynamic Programming for Waste-Management Planning under Uncertainty. Journal of Urban Planning and Development, 120, 132-156.
https://doi.org/10.1061/(ASCE)0733-9488(1994)120:3(132)
[7]  国务院办公厅. 能源发展战略行动计划(2014-2020) [R]. 北京: 国务院办公厅, 2014-06-07.
[8]  国家发展和改革委员会能源研究所. IEA.《中国风电发展路线图2050》[R]. 北京: 国家发展和改革委员会能源研究所, 2014.
[9]  Chen, C., Li, Y.P. and Huang, G.H. (2016) Interval-Fuzzy Municipal-Scale Energy Model for Identification of Optimal Strategies for Energy Management—A Case Study of Tianjin, China. Renewable Energy, 86, 1161-1177.
https://doi.org/10.1016/j.renene.2015.09.040
[10]  国方媛. 北京能源需求及环境综合模型研究与应用[D]: [硕士学位论文]. 北京: 华北电力大学, 2014.
[11]  Chen, C., Li, Y.P., Huang, G.H. and Zhu, Y. (2012) An Inexact Robust Nonlinear Optimization Method for Energy Systems Planning under Uncertainty. Renewable Energy, 47, 55-66.
https://doi.org/10.1016/j.renene.2012.04.007

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