Techno-economic aspects of selecting optimal operating modes of gas transmission systems

With the rapid development of economy, energy demand is increasing in Hebei. Therefore, prediction of energy consumption and structure in Hebei province has importance of actual meaning significance. In this paper, total energy, coal, oil and natural gas consumption data are selected in Hebei province between 2001 and 2013. First, energy consumption and structure in Hebei province are analyzed. Second, GM(1,1)forecast model is established. Then, according to the established forecast model, energy consumption and structure between 2014 and 2021 in Hebei province is predicted. Last, related suggestions on energy optimization are put forward. The results are expected to provide important scientific basis for energy utilization and planning in Hebei province. Introduction Grey prediction is a method that can predict the systems containing uncertainties. To find the laws of system changes, original data is generating processed by identifying development trend of dissimilarity degree between system factors. Thus, data sequence with high regularity is generated. And then the corresponding differential equation model is established to predict future development trend of things. GM (1,1) prediction model with a variable and first-order differential is an important model of grey prediction. It is commonly used in energy and environment prediction because this model requires less modeling information, operates easily, forecasts precisely and is easy to test. In this paper, total energy, coal, oil and natural gas consumption data in Hebei province between 2001 and 2013 are selected as original sequence. GM (1, 1) model is constructed to predict energy consumption and structure in following 20 years in Hebei province. It hopes to provide reference and scientific basis for energy development strategy and the establishment of energy planning in Hebei. Analysis of energy consumption and structure in Hebei province The energy data in Hebei province between 2000 and 2012 are from China energy statistical yearbook. In this paper, all the energy consumption data have been converted into standard coal and the unit is ten thousand tons of standard coal. Table one shows the energy consumption and consumption structure in Hebei province. As shown in table 1, the total energy consumption in Hebei province seems to be increasing annually from 2000 to 2012 and its average annual growth rate is 7.95%. However, the speed of total energy consumption growth is different during the period and it has periodic growth characteristic. From the table, we can see that the growth speed is rapid from 2001 to 2007. Energy consumption structure in Hebei province is basically stable in recent years because of restriction on resources endowment and consumption structure of energy relying mainly on coal cannot be changed. Coal accounts for about 90 percent in energy consumption before 2011, but oil, gas and electricity such clean energy consumption occupies 10 percent of the total energy consumption. This shows that there is no variety in energy consumption structure in Hebei province and energy consumption has many defects. It depends heavily on coal which is non-renewable International Conference on Computational Science and Engineering (ICCSE 2015) © 2015. The authors Published by Atlantis Press 34 energy, so the renewable clean energy strengthened the large market demand, and the development of solar energy utilization technology has a broad prospect. Table.1 Energy consumption and consumption structure in Hebei province Year Total energy consumption Coal Oil Natural gas Electric power Total Proportion Total Proportion Total Proportion Total Proportion 2001 11195.71 10181.38 90.94 914.69 8.17 94.04 0.84 5.60 0.05 2002 12114.29 11125.76 91.84 898.88 7.42 84.80 0.70 4.85 0.04 2003 13404.53 12214.21 91.12 1092.47 8.15 93.83 0.70 4.02 0.03 2004 15297.89 14193.38 92.78 992.83 6.49 100.97 0.66 10.71 0.07 2005 17347.79 15810.78 91.14 1389.56 8.01 130.11 0.75 17.35 0.1

<div class="htmlview paragraph">The United States total transportation energy consumption represents 25% of our domestic consumption and 55% of our petroleum consumption, and it is expected to continue in these proportions in the foreseeable future. We currently have a petroleum import requirement that is projected to increase by the mid-1980s to 50% of the total petroleum consumption, comparable to the projected total transportation energy consumption at that time. The paper discusses the current structure of our transportation services and energy consumption, with particular emphasis on the public transit modes: bus, taxi, rapid transit, trolley, and commuter rail. Data are drawn from various sources and integrated to form an overall view of urban transit impact on energy consumption. Several option examples for conserving public and private transit energy are identified and evaluated for comparison purposes. Comments are included regarding other impacts concerning the optional examples selected.</div>

It is very significant for us to predict future energy consumption accurately. As for China’s energy consumption annual time series, the sample size is relatively small. This paper combines the traditional auto-regressive model with group method of data handling (GMDH) suitable for small sample prediction, and proposes a novel GMDH based auto-regressive (GAR) model. This model can finish the modeling process in self-organized manner, including finding the optimal complexity model, determining the optimal auto-regressive order and estimating model parameters. Further, four different external criteria are proposed and the corresponding four GAR models are constructed. The authors conduct empirical analysis on three energy consumption time series, including the total energy consumption, the total petroleum consumption and the total gas consumption. The results show that AS-GAR model has the best forecasting performance among the four GAR models, and it outperforms ARIMA model, BP neural network model, support vector regression model and GM (1, 1) model. Finally, the authors give the out of sample prediction of China’s energy consumption from 2014 to 2020 by AS-GAR model.

ISHS International Symposium on Advanced Technologies and Management Towards Sustainable Greenhouse Ecosystems: Greensys2011 NEW GREENHOUSE CONCEPT WITH HIGH INSULATING DOUBLE GLASS AND NEW CLIMATE CONTROL STRATEGIES - MODELLING AND FIRST RESULTS FROM A CUCUMBER EXPERIMENT

It is very significant for us to predict future energy consumption accurately. As for China's energy consumption annual time series, the sample size is relatively small. This study combines the traditional auto-regressive model with group method of data handling (GMDH) suitable for small sample prediction, and proposes a novel GMDH based auto-regressive (GAR) model. This model can finish the modeling process in self-organized manner, including finding the optimal complexity model, determining the optimal auto-regressive order and estimating model parameters. Further, four different GAR models, AS-GAR, MR-GAR, SRMSE-GAR and SMAPE-GAR, are constructed according to different external criteria. We conduct empirical analysis on three energy consumption time series, including the total energy consumption, the total petroleum consumption and the total gas consumption. The results show that AS-GAR model has the best forecasting performance among the four GAR models, and it outperforms ARIMA model, BP neural network model, SVM regression model and GM (1, 1) model. Finally, we give the out of sample prediction from 2014 to 2020 by GAR model.

Energy consumption and economic activities in Iran

This article examines the short- and long-run association among carbon emissions, energy consumption and economic growth through deploying the environmental Kuznets curve (EKC) using combined (aggregated) and separated (disaggregated) energy consumption data for Zimbabwe from 1980 to 2014. The ARDL bounds tests and Johansen cointegration tests found long-run relationships among the variables. In the long-run, total energy consumption and primary coal consumption produce statistically significant positive relationships with carbon emissions. However, petroleum consumption demonstrates a statistically significant negative association with carbon emissions. The results show the validity of the EKC in total energy and primary coal consumption in the long-run but are invalid for petroleum consumption. In the short run, the findings reveal that total energy, primary coal and petroleum consumption have statistically significant positive relationships with carbon emissions. Furthermore, in the short run, the EKC is evident in petroleum consumption but invalid in both total energy and primary coal consumption. The short- and long-run Granger causality tests results based on the VECM are also discussed. The article concludes that, if carbon emissions are to be reduced in developing economies, alternative energy sources in the form of green technologies should be adopted as substitutes for coal and petroleum.

This article examines the short- and long-run association among carbon emissions, energy consumption and economic growth through deploying the environmental Kuznets curve (EKC) using combined (aggregated) and separated (disaggregated) energy consumption data for Zimbabwe from 1980 to 2014. The ARDL bounds tests and Johansen cointegration tests found long-run relationships among the variables. In the long-run, total energy consumption and primary coal consumption produce statistically significant positive relationships with carbon emissions. However, petroleum consumption demonstrates a statistically significant negative association with carbon emissions. The results show the validity of the EKC in total energy and primary coal consumption in the long-run but are invalid for petroleum consumption. In the short run, the findings reveal that total energy, primary coal and petroleum consumption have statistically significant positive relationships with carbon emissions. Furthermore, in the short run, the EKC is evident in petroleum consumption but invalid in both total energy and primary coal consumption. The short- and long-run Granger causality tests results based on the VECM are also discussed. The article concludes that, if carbon emissions are to be reduced in developing economies, alternative energy sources in the form of green technologies should be adopted as substitutes for coal and petroleum.

Análisis de insumo-producto de energía y observaciones sobre el desarrollo sustentable, caso mexicano 1970-2010

For industrial consumers with a simple dependence (or close to it) of energy consumption on the volume of output, the possibility of assessing the conditional- constant component of costs using the one-factor model “consumed energy resource-volume of output” is considered. The article highlights the concept of general-plant conditional-constant energy consumption and technological one, which is associated with the heating and maintenance of technological equipment in a working condition. For natural gas used in both the technology of cement production and glass production, the technological conditional-constant component is determined by the cost of energy to maintain the furnaces in a working condition. Using the example of a modern enterprise for the production of nitrogen fertilizers, an assessment of the conditional-constant component of electricity consumption for various industries was made in conditions of changing the production program. For a group of 88 industrial electricity consumers and 32 industrial consumers using either fuel (natural gas) or thermal energy in the technological process, the weight of the conditional-constant component of the consumption was investigated according to the production load. It is shown that industries with a higher weight of the conventional-constant component in the total energy consumption also have a greater horizontal regulation capacity for energy efficiency, i.e. the ability to change the total and specific energy consumption while changing production volumes.KeywordsEnergy efficiencyIndustrial consumers

The global total natural gas consumption has risen to 3.67 trillion cubic meters in 2017 according to BP statistics. It is predicted that natural gas will be “the first one” in all primary energy before 2040. The global natural gas demand is expected to reach 4.9 trillion cubic meters respectively in 2040, accounting for 26.9% in the total primary energy consumption. The natural gas consumption analysis shows that the total consumption in the top twelve natural gas consumption countries accounts for 64.5% of the global. The natural gas consumption in America and Russia exceeded one third of the world in 2015. The gas consumption in BRICS (Brazil, Russia, India, and China and South Africa) has risen dramatically since 1980s. After 1990s, the consumption in the Asia-Pacific region has been on the rise, and the cumulative increment took the first place from 1975 to 2015. The Asia-Pacific region is expected to be the new global consumption center after Europe and America. From the point of per capita consumption, North America, the Middle East and former Soviet Union with rich natural gas resources have exceeded to 2 tons of oil equivalent, nevertheless, the less-developed regions such as Asia-Africa-Latin America are below 0.5 ton of oil equivalent. The gas natural consumption in none-OECD countries exceeded that of OECD in 2007, and the gap has increased year by year. However, the per capita consumption of natural gas in OECD is 5.2 times of Non-OECD. The consumption pattern is mainly influenced by economy development, if natural gas production pattern by resource endowment.

This article examined the relationships involving carbon emissions, economic growth and energy consumption by employing the environmental Kuznets curve (EKC) in South Africa from 1980 to 2014. The auto regressive distributed lag approach and Johansen cointegration tests proved that the variables are cointegrated. The article findings show that combined (total energy consumption) and hydrocarbon gas and petroleum consumption demonstrates evidence of EKC in the long‐run. Other separated data (primary coal, secondary coal, and electricity consumption) show no evidence of the EKC in the long‐run. Primary coal, secondary coal, electricity and hydrocarbon gas consumption develop positive and statistically significant relationships with carbon emissions in the long‐run but the case of total energy and petroleum consumption was negative and statistically significant. The short‐run results illustrate that combined (total energy consumption) and hydrocarbon gas consumption indicate evidence of EKC. Other separated data (primary coal, secondary coal, electricity, and petroleum consumption) show no evidence of the EKC in the short‐run. Short‐run results also indicated that total energy, primary coal, secondary coal, and electricity consumption report positive and statistically significant relationship with carbon emissions but hydrocarbon gas and petroleum consumption indicate positive but insignificant associations. Granger causality test based on vector error correction method (VECM) are also presented to ascertain causality. © 2018 American Institute of Chemical Engineers Environ Prog, 38: 30–46, 2019 Highlights The EKC hypothesis was examined in South Africa by employing energy combined and separated data. The EKC is supported in energy combined data in both short and long‐run but varies in separated data. Primary coal, secondary coal, electricity and hydrocarbon gas consumption develop positive and statistically significant relationships with carbon emissions in the long‐run. Total energy and petroleum consumption generate negative and statistically significant associations with carbon emissions in the long‐run. Total energy, primary coal, secondary coal and electricity show positive and statistically significant relationship with carbon emissions in the short‐run. Hydrocarbon gas and petroleum consumption indicate positive but insignificant association with carbon emissions in the short‐run. Granger causality tests based on VECM are also discussed.

The analysis of energy consumption in freight transportation is almost always approached by disaggregating overall energy consumption by mode, which then provides a basis for understanding trends and underlying factors that influence them and for developing conservation policies. Another important approach is to disaggregate by commodity, because it is commodity flows that generate the modal vehicle flows and hence the modal energy consumption in transportation. Thus changes in energy use by commodity are important factors in understanding trends in energy consumption and may provide a basis for energy conservation policies centered on industries using transportation. Total freight energy consumption is estimated for a range of commodity groups using an activitybased approach to energy consumption, where total freight activity is decomposed into components by mode and by commodity group, and then each component is multiplied by an intensity estimate to calculate total energy use for that commodity group. Two important findings are discussed: ( a) commodity groups with high energy growth between 1972 and 1993 had a combination of substantial ton-mile growth and modal shift to truck, and ( b) commodity groups of finished products with a high average value per ton in general have a much higher average freight energy intensity than raw materials with a low average value per ton.

Daylighting design is not only dimensioning glazed surfaces to provide sufficient natural light to an occupied space but also a method of analyzing how this can be achieved without unwanted effects, such as gains and losses of heat, glare, and variations in daylighting intensity at various indoor distances and levels. The case study presented in this paper highlights the energy consumed due to a group of windows with a large glazed area in an existing building located in a temperate continental climate area. The energy consumption results from supplementary artificial lighting required to maintain adequate illumination for indoor activities and to counterbalance heat loss during colder periods are evaluated. The analysis performed by modifying the glazed surface led to the identification of an optimum value of window dimensions for minimum energy consumption. The results of the case study highlight the fact that the evolution of the total energy consumption, evaluated as the sum of the energy consumption due to additional heating/cooling and the artificial lighting required to compensate for the reduction in natural light, is strongly influenced by the dimensions of the glazed surfaces, as well as the minimum level of lighting imposed by the regime of activities carried out in the building. Thus, the outcomes obtained in the research show that at lighting values below 500 lx, the total energy consumption is directly proportional to the glazed surface. From values of 500 lx for the illuminance level, the total energy consumption drops from 2730 kWh/year for a window height of 230 cm to 2399 kWh/year for a height of 110 cm, after which it starts to rise again, reaching a value of 2786 kWh/year for a height of 30 cm. This phenomenon is also found at values higher than 500 lx; accordingly, for an imposed lighting of 1000 lx, the minimum total consumption is identified at a window height of 150 cm. The case study presented in this paper clearly highlights a complex relationship between the height of the glazed surface and the energy consumption required to compensate for heating or cooling and the reduction in natural lighting. Lower window heights reduce heat loss or gain but also correspondingly increase the energy consumption of artificial lighting.

In recent years, China's economic development has attracted worldwide attention, but economic growth has been at the expense of the excessive consumption of energy.Per unit of GDP's energy consumption in China is not only much higher than the United States, Japan and other developed countries that are far higher than the world average.On basis of simple analysis on the cause of excessive energy consumption in our country, this paper uses the econometrics method to study the various factors in the economic growth and how they affect the level of the energy consumption.Finally, this paper puts forward the proposal of optimize the industrial structure in our country, as well as development of new energy and so on.Only in this way can we at the same time in the guarantee of our country's economic growth and reduce energy consumption, which improve the effect of energy efficiency achieved particularly remarkable.