火电厂大气排放监测大数据分析及政策影响研究
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摘要
火电行业作为我国能源消耗和大气环境污染物排放的重点行业,既是承担国家减排目标责任的主力军,也是体现减排成效的突破口。为进一步研究和分析国家大气污染防治的政策效果,特别是分析促进火电企业减排的主要因素,本文以2006—2015年湖南省300 MW及以上大型火电企业燃煤机组发电过程中烟气排放的SO2、NOx等连续监测大数据为基础,对主要污染物年、月、日、区域的变化及差异进行挖掘,并对各排放特征的形成原因和影响因素进行分析。研究结果总体上反映了大气污染治理政策体系的整体演进特征和规制效果,以及火电厂对中央政策的响应速度和程度。具体而言,除了政策的影响之外,月度变化特征跟区域的能源结构、气候环境紧密相关;日变化特征跟企业的社会责任意识差异及选择的环保设施运行工况有关。根据在线排放特征影响因素分析,提出了火电厂大气排放监测大数据绿色调度智能化和环境信用评价应用的建议。研究结果说明,基于监测数据的政策影响研究对以计量经济学模型为主的政策效果评估方法是一个补充。此外,对于政府提高污染治理的精细化管理水平,进一步完善大气污染治理政策也有重要意义。
The thermal power industry is a major contributor to China's energy consumption and emission of atmospheric environmental pollutants. It is not only the main force shouldering the national objective of emission reduction,but also the breakthrough point of the emission reduction effect. As a further study and analysis on the effects of national atmospheric pollution prevention and control policies,especially the factors that promote the emission reduction of thermal power enterprises,based on the continuous monitoring of SO2 and NOx emissions from coal-fired units in the power generation process of large thermal power enterprises of 300 MW and above in Hunan Province from 2006 to 2015,this paper conducts an in-depth analysis of the annual,monthly,daily and regional changes and differences of major pollutants as well as the formation causes and influencing factors of each emission characteristic,which generally reflects the overall evolution characteristics and regulatory effects of the air pollution control policy system as well as the speed and extent of thermal power plants' response to central policies. Specifically,in addition to the impact of policies,the characteristics of monthly changes are closely related to the regional energy structure and climate environment; the characteristics of daily changes are related to the differences in corporate social responsibility awareness and the operating conditions of environmental protection facilities selected by the enterprises. Based on the above analysis on the factors that affect the online emission characteristics,suggestions for the application of intelligent green dispatching of atmospheric emission monitoring big data and environmental credit evaluation of thermal power plants are proposed. The research results indicate that the policy impact research based on monitoring data is a great supplement to the policy effect evaluation method with a focus on the econometric model. In addition,it is of great significance for the government to improve the level of detailed management of pollution control and further improve the air pollution control policies.
The thermal power industry is a major contributor to China's energy consumption and emission of atmospheric environmental pollutants. It is not only the main force shouldering the national objective of emission reduction,but also the breakthrough point of the emission reduction effect. As a further study and analysis on the effects of national atmospheric pollution prevention and control policies,especially the factors that promote the emission reduction of thermal power enterprises,based on the continuous monitoring of SO2 and NOx emissions from coal-fired units in the power generation process of large thermal power enterprises of 300 MW and above in Hunan Province from 2006 to 2015,this paper conducts an in-depth analysis of the annual,monthly,daily and regional changes and differences of major pollutants as well as the formation causes and influencing factors of each emission characteristic,which generally reflects the overall evolution characteristics and regulatory effects of the air pollution control policy system as well as the speed and extent of thermal power plants' response to central policies. Specifically,in addition to the impact of policies,the characteristics of monthly changes are closely related to the regional energy structure and climate environment; the characteristics of daily changes are related to the differences in corporate social responsibility awareness and the operating conditions of environmental protection facilities selected by the enterprises. Based on the above analysis on the factors that affect the online emission characteristics,suggestions for the application of intelligent green dispatching of atmospheric emission monitoring big data and environmental credit evaluation of thermal power plants are proposed. The research results indicate that the policy impact research based on monitoring data is a great supplement to the policy effect evaluation method with a focus on the econometric model. In addition,it is of great significance for the government to improve the level of detailed management of pollution control and further improve the air pollution control policies.
引文
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[2]中国电力企业联合会.中国电力行业年度发展报告2016[R].2016.
[3]莫华,朱法华,王圣.火电行业大气污染物排放对PM2. 5的贡献及减排对策[J].中国电力,2013(8):1-6.
[4]CHEN X H,TANG X B. Spatial and temporal distribution of PM2. 5pollution and control strategy in Chang-Zhu-Tan city cluster[J].Frontiers of engineering management,2016(2):171-181.
[5]TANG X B,CHEN X H,TIAN Y. Chemical composition and source apportionment of PM2. 5:a case study from one year continuous sampling in the Chang-Zhu-Tan urban agglomeration[J].Atmospheric pollution research,2017,8(5):885-899.
[6]陈诗一,陈登科.雾霾污染、政府治理与经济高质量发展[J].经济研究,2018,53(2):20-34.
[7]邓传华.电力大数据的应用前景与面临的挑战研究[J].河北省科学院学报,2015(3):66-69.
[8]吴凯峰,刘万涛,李彦虎,等.基于云计算的电力大数据分析技术与应用[J].中国电力,2015,48(2):111-117.
[9]揭骏仁.智慧电厂的物联网环境监测平台[D].北京:北京交通大学,2013.
[10]崔立真,史玉良,刘磊,等.面向智能电网的电力大数据存储与分析应用[J].大数据,2017,3(6):42-54.
[11]ANEIROS G,VILAR J,RAA P. Short-term forecast of daily curves of electricity demand and price[J]. International journal of electrical power and energy systems,2016,80:96-108.
[12]ORTIZ M,UKAR O,AZEVEDO F,et al. Price forecasting and validation in the Spanish electricity market using forecasts as input data[J]. International journal of electrical power and energy system,2016,77:123-127.
[13]ZHOU K L, YANG S L. Understanding household energy consumption behavior:the contribution of energy big data analytics[J]. Renewable and sustainable energy reviews,2016,56:810-819.
[14]DIAMANTOULAKIS P D,KAPINAS V M,KARAGIANNIDIS G K. Big data analytics for dynamic energy management in smart grids[J]. Big data research,2015,2(3):94-101.
[15]ZHOU K L,FU C,YANG S L. Big data driven smart energy management:from big data to big insights[J]. Renewable and sustainable energy reviews,2016,56:215-225.
[16]HE X,AI Q,QIU R C,et al. A big data architecture design for smart grids based on random matrix theory[J]. IEEE transactions on smart grid,2017,8(2):674-686.
[17]姜玲,叶选挺,张伟.差异与协同:京津冀及周边地区大气污染治理政策量化研究[J].中国行政管理,2017(8):126-132.
[18]ZHANG Z G,JIN X C,YANG Q X,et al. An empirical study on the institutional factors of energy conservation and emissions reduction:evidence from listed companies in China[J]. Energy policy,2013,57:36-42.
[19]孟凡蓉,王焕,陈子韬.基于扎根理论的大气治理政策执行影响因素及机制研究[J].软科学,2017,31(6):34-37.
[20]邵帅,李欣,曹建华,等.中国雾霾污染治理的经济政策选择——基于空间溢出效应的视角[J].经济研究,2016,51(9):73-88.
[21]魏巍贤,王月红.跨界大气污染治理体系和政策措施——欧洲经验及对中国的启示[J].中国人口·资源与环境,2017,27(9):6-14.
[22]卓子涵,李万新,白重恩.火电企业SO2排放的地区差异性分析[J].中国人口·资源与环境,2013,23(11):96-100.
[23]ZHOU K,YANG S L,SHEN C,et al. Energy conservation and emission reduction of China’s electric power industry[J].Renewable and sustainable energy reviews,2015,45:10-19.