基于LEAP模型的长沙市能源需求预测及对策研究

doi:10.18402/resci.2017.03.10

摘要/Abstract

摘要：

能源是城市运行和可持续发展的重要因素,根据城市特点及其实际发展需要进行能源需求预测具有重要意义。本文采用LEAP模型预测了不同情景下湖南长沙市2015-2020年的能源需求,讨论了GDP增速、产业结构和节能目标对未来能源需求的影响。研究结果表明,GDP增速对能源消费总量和能源强度影响显著,对分部门能源消费结构影响较小;产业结构对分部门能源消费结构影响较显著,对能源消费总量和能源强度影响较小。节能情景(单位GDP能耗2020年较2015年降低15%)下,全市能源消费总量达4014万tce,较基础情景减少317万tce。该情景预测了工业、建筑、交通及居民生活部门实施节能政策的效果,其中第三产业相对节能率最高为13.4%;工业部门其次为6.2%,交通和居民生活部门相对节能率也均超过4%。

关键词: 能流图, 能源需求预测, LEAP模型, 情景分析, 能源政策, 长沙市

Abstract:

Because energy plays an important role in ensuring the safe operation and sustainable development of cities,it is crucial to forecast energy demands according to actual urban development needs and characteristics. Here,we used LEAP model to predict the total amount of energy demand in Changsha from 2015 to 2020 based on historical economic development and energy consumption data. We used scenario analysis to design five types of scenarios to discuss the impact of GDP growth rate,industrial structure and energy-saving object on future energy demand. These scenarios included baseline scenario,different GDP growth rate scenario,different industrial structure scenario,energy saving scenario and comprehensive scenario. The results showed a significant influence of GDP growth rate on total amount of energy consumption and energy intensity and non-significant effect on the energy consumption structure of sectors. Industrial structure had an impact on the energy consumption structure of sectors and not on the total amount of energy consumption and energy intensity. Under an energy saving scenario (a goal of energy intensity reduction by 15% in 2020 compared to 2015), energy consumption reached 40 140 thousand ton coal equivalent (tce)and less 3170 thousand tce than the baseline scenario. This scenario also predicted the effect of energy-saving policies implemented in the industry,building,transportation and residential sectors. When compared with the baseline scenario,the tertiary industry accounted for the largest proportion of energy-saving with a relative energy-saving rate of 13.4% and industry had a lower energy-saving proportion with a relative energy-saving rate of 6.2%. At the same time,the energy-saving proportion of transportation and residential sector also exceeded 4%.

Key words: energy flow diagram, energy demand prediction, LEAP model, scenario analysis, energy policy, Changsha City

陈睿, 饶政华, 刘继雄, 谌盈盈, 廖胜明. 基于LEAP模型的长沙市能源需求预测及对策研究[J]. 资源科学, 2017, 39(3): 482-489.

Rui CHEN, Zhenghua RAO, Jixiong LIU, Yingying CHEN, Shengming LIAO. Prediction of energy demand and policy analysis of Changsha based on LEAP Model[J]. Resources Science, 2017, 39(3): 482-489.

图/表 9

图1

表1

表2

表3

图2

图3

图4

表4

图5

参考文献 24

[1]	刘晓,熊文,朱永彬,等. 经济平稳增长下的湖南省能源消费量及碳排放量预测[J]. 热带地理,2011,31(3):310-315.
	[Liu X,Xiong W,Zhu Y B,et al.Prediction on energy consumption and carbon emission based on steady economic growth of Hunan province[J]. Tropical Geography,2011,31(3):310-315.]
[2]	丁辉. 城市能源系统分析模型研究[M]. 北京:科学出版社,2012.
	[Ding H.Analysis Model of Urban Energy System[M]. Beijing:Science Press,2012.]
[3]	LEAP. User Guide of Long Range Energy Alternative Planning System[M]. Boston:Stockholm Environment Institute,2011.
[4]	Wang Y,Gu A,Zhang A.Recent development of energy supply and demand in China,and energy sector prospects through 2030[J]. Energy Policy,2011,39(11):6745-6759.
[5]	Huang Y,Bor Y J,Peng C Y.The long-term forecast of Taiwan’s energy supply and demand:LEAP model application[J]. Energy Policy,2011,39(11):6790-6803.
[6]	Ates S A,Lund H,Kaiser M J.Energy efficiency and CO2 mitigation potential of the Turkish iron and steel industry using the LEAP(long-range energy alternatives planning )system[J]. Energy,2015,90(1):417-428.
[7]	Perwez U,Sohail A,Hassan S F,et al.The long-term forecast of Pakistan's electricity supply and demand:An application of long range energy alternatives planning[J]. Energy,2015,93:2423-2435.
[8]	黄建. 基于LEAP的中国电力需求情景及其不确定性分析[J]. 资源科学,2012,34(11):2124-2132.
	[Huang J.Scenario analysis of Chinese power demands and uncertainty assessment based on the LEAP model[J]. Resource Science,2012,34(11):2124-2132.]
[9]	Kadian R,Dahiya R P,Garg H P.Energy-related emissions and mitigation opportunities from the household sector in Delhi[J]. Energy Policy,2007,35(12):6195-6211.
[10]	周健,崔胜辉,林剑艺,等. 基于LEAP模型的厦门交通能耗及大气污染物排放分析[J]. 环境科学与技术,2011,34(11):164-170.
	[Zhou J,Cui S H,Lin J Y,et al.LEAP based analysis of transport energy consumption and air pollutants emission in Xiamen city[J]. Environmental Science and Technology,2011,34(11):164-170.]
[11]	Kumar S,Madlener R.CO2 emission reduction potential assess-ment using renewable energy in India[J]. Energy,2016,97:273-282.
[12]	常征,潘克西. 基于LEAP模型的上海长期能源消耗及碳排放分析[J]. 当代财经,2014,(1):98-106.
	[Chang Z,Pan K X.An analysis of Shanghai’s long-term energy consumption and carbon emission based on LEAP model[J]. Contemporary Finance and Economics,2014,(1):98-106.]
[13]	贾彦鹏,刘仁志. 基于LEAP 模型的城市能源规划与 CO2 减排研究——以景德镇为例[J]. 应用基础与工程科学学报,2010,18(S1):75-83.
	[Jia Y P,Liu R Z.Forecasting urban energy and CO2 emission using LEAP model:A case study in Jingdezhen city,China[J]. Journal of Basic Science and Engineering,2010,18(S1):75-83.]
[14]	长沙市统计局.长沙统计年鉴[M]. 北京:中国统计出版社,2015.
	[Statistic Bureau of Changsha. Changsha Statistical Year-book 2015[M]. Beijing:Chinese Statistical Yearbook Press,2015.]
[15]	湖南省统计局.湖南统计年鉴[M]. 北京:中国统计出版社,2015.
	[Statistic Bureau of Hunan Province. Hunan Statistical Yearbook 2015[M]. Beijing:Chinese Statistical Yearbook Press,2015.]
[16]	国家质量监督检验检疫总局. GB/T 2589-2008,综合能耗计算通则[EB/OL]. (2008-06-01)[2016-08-01]. .
	[General Administration of Quality Supervision,Inspection and Quarantine of the People's Republic of China. GB/T 2589-2008,General Principles for Calculation of the Comprehensive Energy Consumption[EB/OL].(2008-06-01)[2016-08-01]. .]
[17]	吴希,陶芳芳. 千万级人口大都市款步而来[N]. 湖南日报,2014-08-06(01).
	[Wu X,Tao F F. Ten Million Population and Walk to [N].Hunan Daily,2014-08-06(01).]
[18]	文波,苏毅,周小华. 奋力谱写长沙市“十三五”发展新篇章[N]. 长沙晚报,2016-01-09(01).
	[Wen B,Su Y,Zhou X H. Changsha, Struggling to Write a New Chapter in the Develop-ment of "13th Five-Year"[N]. Changsha Evening News,2016-01-09(01).]
[19]	周小华. 未来长沙能量更大实力更强城乡更美民生更爽[N]. 长沙晚报,2016-01-22(02).
	[Zhou X H. The Future of Changsha Having a More Powerful Energy,a More Stronger Strength,a More Beautiful Urban and Rural Areas and Living Hood[N]. Changsha Evening News,2016-01-22(02).]
[20]	舒微. 研究市人大常委会工作讨论政府工作报告和“十三五”规划纲要[N]. 长沙晚报,2016-01-12(01).
	[Shu W. Meeting of Study on the Municipal People's Congress Micro Work to Discuss the Government Work Report and the "13th Five-Year" Plan[N]. Changsha Evening News,2016-01-09(01).]
[21]	习近平. 关于《中共中央关于制定国民经济和社会发展第十三个五年规划的建议》的说明[J]. 实践(思想理论版),2015,(11):17-22.
	[Xi J P.Explanation on the proposal of the central com-mittee of the communist party of China on the development of the 13th five-year plan for national economic and social deve-lopment[J]. Practice(Theory of Thought),2015,(11):17-22.]
[22]	杨苗苗. 国务院发布能源发展战略行动计划[N]. 中国电力报,2014-11-20(01).
	[Yang M M. The State Council Issued the Energy Development Strategy Action Plan[N]. China Electric News,2014-11-20(01).]
[23]	Feng Y Y,Zhang L X.Scenario analysis of urban energy saving and carbon abatement policies:A case study of Beijing city,China[J]. Procedia Environmental Sciences,2012,13(10):632-644.
[24]	向青,廖胜明. 基于VAR模型的湖南省能源需求影响因素分析[J]. 经济论坛,2015,(11):18-23.
	[Xiang Q,Liao S M.An analysis of Hunan’s energy demand impact factors based on VAR model[J]. Economic Forum,2015,(11):18-23.]

输入参数	基础情景	不同经济增速		不同产业结构		节能情景	综合情景
输入参数	BS	LGDP	HGDP	LIS	HIS	ES	MBS	MSS
2015年GDP增长率/%	9.8	9.8	9.8	9.8	9.8	9.8	9.8	9.8
2016-2020年GDP年均增长率/%	9.0	6.5	12.0	9.0	9.0	9.0	12.0	6.5
2020年产业结构/一产:二产:三产	3:50:47	3:50:47	3:50:47	3:45:52	3:56:41	3:50:46	3:56:41	3:45:52
2020年人口/万	1 000^[17]	1 000	1 000	1 000	1 000	1 000	1 000	1 000
第一产业能源强度年均下降率/%	0.5	0.5	0.5	0.5	0.5	0.5	0.5	0.5
第二产业能源强度年均下降率/%	3.0	3.0	3.0	3.0	3.0	4.0	3.0	4.0
第三产业能源强度年均下降率/%	1.0	1.0	5.0	1.0	5.0	3.6	1.0	3.6
居民生活能源强度年均增长率/%	5.0	1.0	5.0	1.0	5.0	4.6	5.0	4.6

	政策或措施	效果量化
工业部门	淘汰落后产能,工业清洁生产	2020年规模工业万元增加值能耗下降10%,规模下工业万元增加至能耗下降15%。
建筑部门	建筑能源管理及节能改造利用绿色建材及节能新技术	2020年基本实现高耗能建筑节能改造;节能设计标准实施率100%;能耗监测节能10%,大型公共建筑单位面积能耗降低18%,用电设备效率提高30%。
交通部门	发展公共交通及轨道交通推广节能与新能源汽车	2020年公交出行分担率达42%;新能源和清洁能源公共交通车辆比率达65%以上; 2020年燃料效率提升15%,年均行驶里程同比提高12%,燃料经济性提高20%^[23]。
居民生活部门	推广节能生活器具居民住宅围护结构改造	2020年节能电器占家用电器50%以上,家用用电设备效率提高35%以上; 居民采暖、制冷能耗降低10%^[23]。

部门	基年2014	LGDP2020	BS2020	HGDP2020	LIS2020	HIS2020	ES2020	MBS2020	MSS2020
第一产业	1.62	1.61	1.64	1.68	1.64	1.65	1.77	1.63	2.03
第二产业	49.67	40.06	40.92	41.84	36.77	45.92	41.51	51.48	41.41
第三产业	33.40	39.16	40.00	40.91	44.18	34.96	38.76	35.39	36.96
居民生活	15.00	19.17	17.44	15.57	17.41	17.47	17.96	11.51	19.60

部门	节能情景能源需求/万tce	基础情景能源需求/万tce	相对节能率 /%
第一产业	71.0	71.0	0
工业	1 568.2	1 672.3	6.2
建筑业	98.0	100.1	2.1
交通运输等	573.2	597.6	4.1
商业及其他	982.8	1 134.7	13.4
居民生活	720.8	755.3	4.6