Long-term prediction of underground gas storage user gas flow nominations
Abstract
Many companies operating on the natural gas market use natural gas storage to balance production and transport capacities with major variations in gas demand. This paper presents an approach to predicting users' gas flow nomination in underground gas storage by different users. A one-year prediction horizon is considered with weekly data resolution. Basic models show that whereas for the great majority of users we can predict nomination based only on weather data and technical parameters, for some users additional macro-economic data significantly improved prediction accuracy. Various modeling techniques such as linear regression, autoregressive exogenous model and Artificial Neural Network were used to develop prediction models. Results show that for most users an Artificial Neural Network provides optimal accuracy, indicating the non-linearity of the relationship between input and output variables. The models developed are intended to be used as support for facility operation decisions and gas storage product portfolio modifications.References
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[2] Ding Guosheng, Li Chun, Wang Jieming, Xu Hongcheng, Zheng Yali, Wanyan Qiqi, Zhao Yanjie. The status quo and technical development direction of underground gas storages in China. Natural Gas Industry B 2015; 2(6):535-541.
[3] Christoph Budny, Reinhard Madlener, Christoph Hilgers. Economic feasibility of pipe storage and underground reservoir storage options for power-to-gas load balancing. Energy Conversion and Management 2015;102: 258–266.
[4] Esther Neomi Escobar, Gerardo Raul Arteaga Mora, Alexander George Kemp. Underground Natural Gas Storage in the UK: Business Feasibility. Case Study. SPE EUROPEC/EAGE Annual Conference and Exhibition, 2011, Vienna, Austria.
[5] Bojan Žlender, Stojan Kravanja. Cost optimization of the underground gas storage. Engineering Structures 2011; 33(9):2554–2562.
[6] Roman Danel, Lukáš Otte, Vladislav Vancura, Michal Řepka. Monitoring and Balance of Gas Flow in Underground Gas Storage. Procedia Earth and Planetary Science 2013; 6:485-491.
[7] K. Wojdan, B. Ruszczycki, D. Michalk, K. Swirski. Method for Simulation and Optimization of Underground Gas Storage Performance. Oil & Gas Science and Technology–Revue d’IFP Energies nouvelles 2014; 69(7): 1237-1249.
[9] Rainer Kurz, Matt Lubomirsky, Klaus Brun. Gas Compressor Station Economic Optimization. International Journal of Rotating Machinery 2011; 2012.
[10] Tang Ligen1, Wang Jieming, Ding Guosheng, Sun Shasha1, Zhao Kai, Sun Junchang, Guo Kai, Bai Fengjuan. Downhole inflow-performance forecast for underground gas storage based on gas reservoir development data. Petroleum Exploration and Development 2016, 43(1): 138–142.
[11] Suat Bagaci, E.Ozturk. Performance Prediction of Underground Gas Storage in Salt Caverns. Energy Sources Part B 2007; 2(2):155 165.
[12] Haydar Aras, Nil Aras. Forecasting Residential Natural Gas Demand. Energy Source 2004; 26(5): 463-472.
[13] F. B. Gorucu. Artificial Neural Network Modeling for Forecasting Gas Consumption. Energy Sources 2004; 26(3).
[14] Gumrah F, Katircioglu D, Aykan Y, Okumus S, Kilincer N. Modeling of gas demand using degree-day concept: case study for Ankara. Energy Sources 2001; 23(2): 101-114.
[15] Eugenio Fco, Sánchez-Úbeda, Ana Berzosa. Modeling and forecasting industrial end-use natural gas consumption. Energy Economics 2007; 29(4) :710–742.
[16] R. Gutiérrez, A. Nafidi, R. Gutiérrez Sánchez. Forecasting total natural-gas consumption in Spain by using the stochastic Gompertz innovation diffusion model. Applied Energy 2005; 80(2): 115-124.
[17] Zia Waduda, Himadri S. Deyb, Md. Ashfanoor Kabira, Shahidul I. Khana. Modeling and forecasting natural gas demand in Bangladesh. Energy Policy 2001; 39(11): 7372-7380.
[18] Lon-Mu Liu, Maw-Wen Lin. Forecasting residential consumption of natural gas using monthly and quarterly time series. International Journal of Forecasting 1991; 7(1)
Published
2020-01-17
How to Cite
KUREK, Teresa; WOJDAN, Konrad; ŚWIRSKI, Konrad.
Long-term prediction of underground gas storage user gas flow nominations.
Journal of Power Technologies, [S.l.], v. 99, n. 4, p. 272–280, jan. 2020.
ISSN 2083-4195.
Available at: <https://papers.itc.pw.edu.pl/index.php/JPT/article/view/1598>. Date accessed: 22 dec. 2024.
Issue
Section
Energy Conversion and Storage
Keywords
underground gas storage, long term forecasting, artificial neural networks, prediction models, demand forecasting
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