【摘要】：為改善能源結(jié)構(gòu)和治理環(huán)境污染,作為清潔能源的天然氣需求量逐年大幅增加,天然氣供應(yīng)也出現(xiàn)了諸多新問(wèn)題,如何預(yù)測(cè)和規(guī)劃天然氣用量,保障平穩(wěn)供氣,提高供氣方案的可靠性和經(jīng)濟(jì)性等均是實(shí)際工程中經(jīng)常遇到的難題。論文主要對(duì)天然氣的負(fù)荷預(yù)測(cè)、調(diào)峰方案及調(diào)壓站優(yōu)化配置進(jìn)行研究。首先利用相關(guān)分析確定天然氣短期用氣負(fù)荷的影響因子,借助SPSS和MATLAB軟件,用回歸分析法和人工神經(jīng)網(wǎng)絡(luò)法分別對(duì)天然氣短期負(fù)荷進(jìn)行預(yù)測(cè),結(jié)果表明采用BP人工神經(jīng)網(wǎng)絡(luò)的預(yù)測(cè)值和實(shí)際天然氣負(fù)荷變化趨勢(shì)基本一致。其次,基于天然氣中期負(fù)荷的特點(diǎn),以某市冬季高峰供氣階段為例,利用BP人工神經(jīng)網(wǎng)絡(luò)建立預(yù)測(cè)模型,進(jìn)行中期負(fù)荷的預(yù)測(cè),預(yù)測(cè)誤差在10%以?xún)?nèi),對(duì)今后工程應(yīng)用上有指導(dǎo)作用。論文對(duì)天然氣的長(zhǎng)期負(fù)荷預(yù)測(cè)進(jìn)行了研究,考慮天然氣用戶(hù)的用氣特點(diǎn)和和用戶(hù)等級(jí),結(jié)合用氣結(jié)構(gòu)的優(yōu)化調(diào)整,創(chuàng)新性的提出新的用戶(hù)分類(lèi)方法。利用分類(lèi)用戶(hù)的特點(diǎn)及相應(yīng)的預(yù)測(cè)方法,分別對(duì)某市的居民用戶(hù)、商業(yè)用戶(hù)、采暖用戶(hù)、汽車(chē)用戶(hù)和工業(yè)用戶(hù)的長(zhǎng)期負(fù)荷進(jìn)行預(yù)測(cè),與實(shí)際年負(fù)荷相比誤差為9.3%,屬于高精度預(yù)測(cè),表明分類(lèi)預(yù)測(cè)方法適用于長(zhǎng)期負(fù)荷預(yù)測(cè)。最后對(duì)天然氣供氣方案中的調(diào)峰儲(chǔ)氣和調(diào)壓站優(yōu)化配置做了研究,提出采用單位調(diào)峰量年作用成本進(jìn)行經(jīng)濟(jì)分析,分析對(duì)比多種調(diào)峰方式,并計(jì)算某市的調(diào)峰量,提出適合該市的近遠(yuǎn)期調(diào)峰方案。對(duì)高中壓調(diào)壓站的優(yōu)化配置建立數(shù)學(xué)模型,確定了最佳配置數(shù)量和作用半徑,并以某市新建片區(qū)為例,該數(shù)學(xué)模型可為今后的工程規(guī)劃提供理論依據(jù)。
[Abstract]:In order to improve the energy structure and control environmental pollution, the demand for natural gas as clean energy has increased greatly year by year, and many new problems have emerged in the supply of natural gas. How to predict and plan the amount of natural gas to ensure the stable gas supply. Improving the reliability and economy of gas supply scheme is a common problem in practical engineering. In this paper, the load forecasting, peak shaving scheme and optimal configuration of voltage shunting station are studied. Firstly, the influencing factors of short-term gas load of natural gas are determined by correlation analysis, and the short-term load of natural gas is predicted by regression analysis and artificial neural network with the help of SPSS and MATLAB software. The results show that the predicted value of BP artificial neural network is basically consistent with the change trend of actual natural gas load. Secondly, based on the characteristics of medium-term load of natural gas, taking the peak gas supply stage in winter as an example, the prediction model is established by using BP artificial neural network to predict the medium-term load, and the prediction error is less than 10%. It has a guiding role in engineering application in the future. In this paper, the long-term load forecasting of natural gas is studied. Considering the characteristics and user grade of natural gas users, combined with the optimization and adjustment of gas structure, a new user classification method is proposed innovatively. By using the characteristics of classified users and the corresponding prediction methods, the long-term loads of resident users, commercial users, heating users, automobile users and industrial users in a city are predicted respectively, and the error is 9.3% compared with the actual annual load. It belongs to high precision prediction, which shows that the classification forecasting method is suitable for long-term load forecasting. Finally, the optimal configuration of peak-shaving gas storage and voltage regulating station in natural gas supply scheme is studied, and the economic analysis is carried out by using the annual action cost of unit peak-shaving quantity, and various peak-shaving methods are analyzed and compared, and the peak-shaving quantity of a city is calculated. A short-term and long-term peaking scheme suitable for the city is put forward. A mathematical model is established for the optimal allocation of high and high pressure regulating stations, and the optimal allocation quantity and action radius are determined. Taking a newly built area of a city as an example, the mathematical model can provide a theoretical basis for future engineering planning.
【學(xué)位授予單位】：南京理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：TU996

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本文編號(hào)：2485603