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Comparison of electricity generating from natural gas and from other sources in selected EU member states and in the USA — A review

Metelska K., Biały R., Cieślik T., Szurlej A.

Transactions of the Institute of Fluid-Flow Machinery

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2017

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nr 137

105--121

EN

The analyses scale of natural gas use for power generation in selected countries have been analyzed. In the USA natural gas has strengthened its position as fuel in the electricity generation sector, while in the EU member states opposite tendencies were observed. One of the main reasons for these differences are significantly higher prices of natural gas in EU member states in comparison to prices in the USA. The article examines the relationship between gross domestic product and natural gas consumption. Moreover, the costs of electricity obtained from different sources were compared, with particular emphasis on technologies based on natural gas.

2

Próba zbudowania mapy drogowej klientów gazu ziemnego

Cieślik T., Narloch P., Kogut K., Metelska K.

Gaz, Woda i Technika Sanitarna

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2017

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Nr 4

123--127

PL

Obecnie w Polsce gaz ziemny rozprowadzany jest na terenie 1729 gmin z 2478 [16]. Dostęp do gazu posiada około 20,3 min mieszkańców z 38,5 min. Najwięcej gmin z dostępem do gazu ziemnego występuje w województwach: podkarpackim i małopolskim, natomiast najmniej gmin z dostępem do gazu zlokalizowanych jest na obszarze województw: warmińsko-mazurskiego oraz wielkopolskiego. W czasie kiedy następuje liberalizacja rynku gazu ziemnego, poznanie cech klienta jest ważne zarówno dla dostawcy jak i dla dystrybutora. Przedstawiamy udogodnienia operacyjne w postaci nowych modeli prognozowania zużycia gazu oraz matematycznych modeli klientów. Modele poboru gazu przez prywatnych odbiorców zbudowane są w zależności od temperatury oraz dodatkowo wyznaczony został wpływ danych sztucznych takich jak: dzień tygodnia, miesiąc, rodzaj zabudowy czy liczba kondygnacji.

EN

Currently in Poland, the natural gas is distributed in 1729 community from 2478 [16]. Access to gas has about 20.3 million from 38.5 million. In time as the liberalization of the gas market, know the characteristics of the client is important for both the supplier and the distributor. Viewing facilities operating in the form of new models of forecasting gas consumption and mathematical models of customers. Models of gas taken by the private customers are made according to the temperature and in addition data has been designated impact artificial data such as day of the week, month, or the number of the constructional floor.

3

Skuteczność prognozowania zużycia gazu z wykorzystaniem metod regresji i sztucznych sieci neuronowych

Cieślik T., Metelska K.

Czasopismo Inżynierii Lądowej, Środowiska i Architektury / Journal of Civil Engineering, Environment and Architecture

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2017

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z. 64, nr 1

133-141

PL

Na podstawie zgromadzonych danych takich jak: temperatura, siła wiatru oraz zużycie gazu w ciągu dnia na przestrzeni dwóch lat określono wpływ czynników atmosferycznych na konsumpcje gazu za pomocą regresji wielorakiej, funkcji potęgowych oraz funkcji użytkownika. Wyznaczono wpływ miesiąca oraz dnia (parametr sztuczny) na konsumpcje gazu. Zbudowano modele regresji liniowe, potęgowej oraz sztuczne sieci neuronowe służące do określania zużycia gazu. Starano się wyznaczyć jak najlepszy model regresji i porównywano go do modeli sieci neuronowych za pomocą MAPE (średni absolutny błąd procentowy).

EN

Based on the collected data, such as temperature, wind power and gas consumption during the day for over two years determine the effects of weathering on gas consumption by using multiple regression, power functions and user functions. We determine the impact of the month and day (artificial parameter) to consume gas. We build models of linear regression-in, power series and artificial neural networks for determining gas consumption. We are trying to determine how best regression model and compare it to the neural network models using MAPE (mean absolute percentage error).

4

Modeling of gas consumption in the city

Cieślik T., Metelska K.

AGH Drilling, Oil, Gas

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2017

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Vol. 34, no. 2

439--453

EN

Based on the data collected over a two year time period, which included temperature, wind speed and gas consumption during the day, the effects of weather factors on gas consumption in the city have been established with the use of multiple regression. The impact of a particular month, day (dummy variable) or holiday of a year on the gas consumption has also been determined. The models of linear regression and artificial neural networks have been constructed for determining the gas consumption. An attempt has been made to find the best regression models and compare them to the neural network models with the use of mean absolute percentage error (MAPE).

5

A review of currently available LNG facilities and means of transport used for supplying terminals in natural gas with a projection of LNG prices in the USA

Metelska K., Cieślik T.

AGH Drilling, Oil, Gas

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2016

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Vol. 33, no. 1

49--67

EN

LNG terminals allow for constant availability of fuel gas in insular countries (Japan) and in countries which do not have the option of transporting gas via inland pipelines due to their geopolitical location (South Korea). In many cases, possessing an LNG (liquefied natural gas) terminal increases energy independence, which translates first and foremost into avoiding energy blackmail (Lithuania). This paper presents currently existing LNG terminals together with an overview of the most widely used technologies for gas liquefaction, storage and long-distance transporting by LNG carriers. A comparison of natural gas liquefaction plants and LNG terminals with respect to their location, age and capacity is also given. In the final part of the paper we analyse LNG carriers in relation to age, capacity and shipowner. On the basis of data collected in the last 12 years, such as: price of LNG exported from USA, prices of imported and exported natural gas, consumption of gas, we have established the price of imported LNG using linear regression and power functions. In order to determine the price of LNG we have constructed mathematical models by means of the STATISTICA program. Our aim was to find the most accurate model. Three basic ex post methods have been used to compare regression models: ME (mean error), RMSE (root mean squared error), MAPE (mean absolute percentage error).