纯火电电网节能潜力分析

建立以火电机组煤耗特性为基础的纯火电电网能耗计算模型.利用这个模型计算某电网在不同运行条件下的电网供电煤耗率;分析计算结果,总结出电网供电侧的各种节能潜力及其对电网供电煤耗率的影响.     

火电机组节能减排在线监测系统的研究与应用

介绍了火电机组节能减排在线监测系统的研究与应用。利用节能减排在线监管系统实现火电机组污染物减排的实时监控,为火电厂污染物运行优化提供参考。系统基于火电厂生产实时数据采集、数据压缩存储原理、数据校验,按有关标准进行能耗和污染物指标的在线计算。实现机组的煤耗分析排序和污染指标的统计分析,为机组节能减排提供基础数据,同时展示生产数据及分析指标,报表系统实现分析指标的统计。通过实际比较,系统计算准确,监测内容全面,满足分析和统计要求。     

600 MW太阳能协同燃煤发电机组的变工况性能分析

利用Aspen Plus建立了600 MW超临界汽轮机系统的模型,在此基础上构建了利用太阳能集热场生产蒸汽取代某一段高压加热器抽汽的协同燃煤发电模型,并模拟了4种不同负荷工况下机组的协同发电运行情况,分析了3种太阳能协同方案在变工况下的能耗情况及汽轮机通流量等参数的变化规律.结果表明:所构建的太阳能协同发电方案可在保证汽轮机蒸汽质量流量变化较小的情况下,显著降低600 MW燃煤机组的发电煤耗率,3种协同发电方案均适合对在运火电机组进行改造,是实现火电机组节能减排和太阳能稳定发电的有效途径之一.     

超超临界机组的能耗分布与节能潜力研究

随着我国节能减排政策的推进,降低火力发电机组的煤耗已成为电力企业节能工作的重要目标。近年来超超临界火电机组以其高效率、低能耗、低污染的优点得到了快速发展。对典型超超临界机组的能耗进行详细的分析,得出机组能耗分布的规律,对机组节能具有一定的指导意义。本文依据单耗分析理论,通过对某2×1000MW超超临界火电机组的设计工况进行热力计算,得到整个机组的能耗分布,反映出各设备能耗占整个机组能耗的比例,并以此为依据找出各设备的节能潜力。     

火电机组能耗分布与节能潜力分析研究

基于单耗分析法对600MW超临界火电机组进行热力计算,得到整个机组在不同工况下的能耗分布,反映了能耗分布状况及其比例。对某一实际运行工况进行节能潜力分析,找到影响机组能耗的主要部位和具体节能潜力的大小,对指导火电机组的节能降耗具有重要意义。     

燃煤发电机组超低排放改造增加能耗模型研究

随着国内环境压力加大,火电环保要求逐步提高,全国范围燃煤机组进行超低改造,超低改造实现减排的同时增加了机组能耗,给火电节能工作带来更大压力。通过对超低改造脱硫、脱硝、除尘增加厂用电率的设计值和实际运行值进行数据分析,归纳出超低改造系统增加厂用电率计算模型,为测算超低改造系统对机组能耗水平的影响提供理论依据,为下一步环保系统节能奠定基础。     

火电机组煤耗考核评价分析方法实践述评

在国家和北京市“十一五”规划节能减排约束性指标大背景下,2008年,北京市在电力工业推行“上大压小、节能减排”。其中,由政府部门出面针对电厂组织火电机组煤耗考核在全国为首创。本文以火电机组煤耗考核评价分析方法为对象,系统分析了北京2008年火电机组煤耗考核的优点与不足,对2009年北京市火电机组煤耗考核工作提出了完整、实用、可行的评价分析改进建议。研究成果可用于指导2009年北京市煤耗考核工作,对其他兄弟省份的同类型考核实践也可起到很好的借鉴作用。     

火电机组主要可控参数变化的耗差分析

耗差分析是指分析机组运行中某些参数偏离目标工况时对机组热经济性的影响。对火电机组可控参数变化的煤耗分析,其目的是确定可控参数对机组性能的影响程度,为提高机组的经济运行水平提供科学依据,是实现火电机组节能降耗的必备的基础理论工作之一。通过对火力发电机组重要可控运行参数变化与煤耗的关系的研究,提出了火电机组重要运行参数偏离目标值所引起的煤耗偏差计算模型,此模型可应用于机组运行热经济性在线监测系统。     

火电机组能耗指标体系构建与经济性诊断研究

数据挖掘技术已广泛应用于传统火电行业的节能优化运行与管理,但目前基于数据挖掘的机组运行优化方法往往缺乏普遍性和实操性,针对少量典型工况挖掘得到的参数目标值,无法给予实际运行机组全面的指导。本文在分析锅炉和汽轮机等主要子系统性能指标影响因素的前提下,建立基于层次划分的火电机组能耗指标体系,提出了数据挖掘与火电机组能耗机理分析耦合的经济性诊断模型,有效提高了输出的鲁棒性。以供电煤耗为例,基于历史数据挖掘的火电机组经济性诊断模型和流程,准确分析出能耗水平高于报警阈70%是由于实时排烟氧量偏离基准值造成的,并给出符合实际工况的调节建议。     

电厂锅炉运行中节能降耗策略研究

火电燃煤机组煤耗约占全国总煤耗的30%,随着我国能源消费革命的不断深化,火电机组采取有效的节能措施,降低能源消耗成为这场能源革命中关键的一环。在此背景下,通过对北京巴布科克·威尔科克斯有限公司设计和制造的350 MW直流炉运行情况进行分析研究,站在运行调整的角度,对锅炉的节能减排技术措施进行分析讨论,为火电厂锅炉节能工作提供参考性建议。     

主蒸汽压力变化对机组煤耗率的影响分析

主蒸汽压力是机组实际运行中必须密切监视和调节的主要参数之一,在机组实际运行中会不可避免的偏离设计值,从而影响机组的发电标准煤耗率,影响机组的热经济性和安全性.在火电厂热经济性统一物理模型和数学模型的基础上,根据多元扰动下的热力系统能效分析模型得到主汽压变化对煤耗率影响的计算模型,并以某电厂660 MW机组为例进行验证,并计算了此机组不同工况下主汽压变化对机组煤耗的影响,并绘制成图,对其规律进行了分析.此计算模型得到的计算结果误差较小,足以满足工程实际的需要.     

抽水蓄能电站调峰填谷节煤计算及评价

仅以抽水蓄能电站抽水耗煤指标作为其调峰填谷的能量转换特征指标是不全面和不完整的，因为在调峰填谷中，除以发电煤经形式表征的抽水耗煤指标外，还有填谷节煤和率和调峰填谷中，     

600MW机组锅炉掺烧褐煤试验及燃料成本计算研究

在机组各负荷工况下进行不同比例的褐煤掺烧试验。试验结果表明,随着褐煤掺烧比例的增加,锅炉热效率呈下降趋势,供电煤耗、厂用电率呈上升趋势。建立燃料成本计算模型,利用掺烧煤种比例、主烧煤种与掺烧煤种价格表达燃料成本,计算得出不同负荷下的最佳掺烧比例。实现对电厂配煤掺烧经济性的理论指导,并可用于辅助优化电厂燃料采购决策。     

ARIMA forecasting of China’s coal consumption,price and investment by 2030

Forecasting of energy consumption, price, and investment in coal industry is one of the most important proactive approaches and policy instruments used by decision-makers in China. Due to the richer information on time-related changes than the other methods, autoregressive integrated moving average (ARIMA) is applied to estimate the further coal price, consumption, and investment of China from 2016 to 2030. The best-fitted models for coal price, consumption, and investment at each predicted step are selected. The empirical results show that the annual average rate of coal consumption and investment will decrease between 2016 and 2030 except for coal price, which exhibits fluctuant behavior in the forecast period. The annual droop rate of coal consumption from 2016 to 2030 will be rather big, nearly the same with the annual growth rate from 2000 to 2015. The coal investment has the similar result with coal consumption.     

基于分支限界法的火电机组负荷分配研究

针对大规模新能源并网调峰问题,提出不同调峰阶段火电机组负荷分配方法：分析火电机组调峰能力、调峰成本及二者之间的关系;以总煤耗成本、机组启停成本之和最小为目标,建立不同调峰阶段火电机组负荷分配优化模型;根据火电机组爬坡率、滑坡率,提出参与负荷分配机组的组合策略,并使用分支限界法对负荷分配优化模型求解。算例表明,随着火电机组调峰深度的增加,机组煤耗成本和启停成本减少,深度调峰运行下附加煤耗成本和机组损耗成本增加。     

When will fossil fuel reserves be diminished?

Crude oil, coal and gas are the main resources for world energy supply. The size of fossil fuel reserves and the dilemma that “when non-renewable energy will be diminished” is a fundamental and doubtful question that needs to be answered. This paper presents a new formula for calculating when fossil fuel reserves are likely to be depleted and develops an econometrics model to demonstrate the relationship between fossil fuel reserves and some main variables. The new formula is modified from the Klass model and thus assumes a continuous compound rate and computes fossil fuel reserve depletion times for oil, coal and gas of approximately 35, 107 and 37 years, respectively. This means that coal reserves are available up to 2112, and will be the only fossil fuel remaining after 2042. In the Econometrics model, the main exogenous variables affecting oil, coal and gas reserve trends are their consumption and respective prices between 1980 and 2006. The models for oil and gas reserves unexpectedly show a positive and significant relationship with consumption, while presenting a negative and significant relationship with price. The econometrics model for coal reserves, however, expectedly illustrates a negative and significant relationship with consumption and a positive and significant relationship with price. Consequently, huge reserves of coal and low-level coal prices in comparison to oil and gas make coal one of the main energy substitutions for oil and gas in the future, under the assumption of coal as a clean energy source.     

New experimental technique to determine coal self-ignition duration

An artificial neural network (ANN) model was adopted to simulate the relationship between self-ignition duration and sulfur content, ash content, oxygen consumption rate, carbon monoxide as well as carbon dioxide generation rate of coal at different temperatures of self heating process. The data from spontaneous combustion experiments were used for ANN training to obtain the connection strength between nerve cells. An oil-bath programmed temperature experiment device was designed and the experimental condition and the size of the test tube were determined for testing the oxygen consumption and the gases generation rate of coal during self-heating process. The sulfur content, the ash content and the data from the oil-bath experiment were taken as ANN inputs to calculate the experiment self-ignition duration of coal. Compared with spontaneous combustion experiment, less than 1% of coal sample and 10% of time are required with an error of less than 3 days to test self-ignition duration of coal.     

两种不同入炉煤颗粒度下210MW循环流化床机组的性能比较

在两种不同入炉煤颗粒度下,对某国产化210 MW循环流化床机组进行各项运行参数详细测定和锅炉效率、灰/渣份额、供电煤耗等比较试验.结果表明,入炉煤中细颗粒增多将使锅炉含碳量升高,飞灰分额增大,从而使锅炉效率降低、机组供电煤耗增加;经过耗差分析,飞灰份额增大对该机组供电煤耗增加的影响最大,可为运行和设计部门提供参考.     

China’s farewell to coal: A forecast of coal consumption through 2020

In recent decades, China has encountered serious environmental problem, especially severe air pollution that has affected eastern and northern China frequently. Because most air pollutants in China are closely related to coal combustion, the restriction of coal consumption is critical to the improvement of the environment in China. In this study, a panel of 29 Chinese provinces from 1995 to 2012 is utilized to predict China’s coal consumption through 2020. After controlling for the spatial correlation of coal consumption among neighboring provinces, an inverted U-shaped Environmental Kuznets Curve (EKC) between coal consumption per capita and GDP per capita in China is detected. Furthermore, based on the estimation results and reasonable predictions of key control variables, China’s provincial and national coal consumption through 2020 is forecasted. Specifically, under the benchmark scenario, consumption is expected to continue growing at a decreasing rate until 2020, when China’s coal consumption would be approximately 4.43 billion tons. However, if China can maintain relatively high growth rate (an annual growth rate of 7.8 percent), the turning point in total coal consumption would occur in 2019, with projected consumption peaking at 4.16 billion tons.