IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v134y2017icp968-983.html
   My bibliography  Save this article

Dynamic optimization of natural gas networks under customer demand uncertainties

Author

Listed:
  • Ahmadian Behrooz, Hesam
  • Boozarjomehry, R. Bozorgmehry

Abstract

In natural gas transmission networks, the efficiency of daily operation is strongly dependent on our knowledge about the customer future demands. Unavailability of accurate demand forecasts makes it more important to be able to characterize the loads and quantify the corresponding uncertainty.

Suggested Citation

  • Ahmadian Behrooz, Hesam & Boozarjomehry, R. Bozorgmehry, 2017. "Dynamic optimization of natural gas networks under customer demand uncertainties," Energy, Elsevier, vol. 134(C), pages 968-983.
    • Handle: RePEc:eee:energy:v:134:y:2017:i:c:p:968-983
    DOI: 10.1016/j.energy.2017.06.087
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544217310812
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2017.06.087?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ferruzzi, Gabriella & Cervone, Guido & Delle Monache, Luca & Graditi, Giorgio & Jacobone, Francesca, 2016. "Optimal bidding in a Day-Ahead energy market for Micro Grid under uncertainty in renewable energy production," Energy, Elsevier, vol. 106(C), pages 194-202.
    2. Sanchez-Ubeda, Eugenio Fco. & Berzosa, Ana, 2007. "Modeling and forecasting industrial end-use natural gas consumption," Energy Economics, Elsevier, vol. 29(4), pages 710-742, July.
    3. Crestaux, Thierry & Le Maıˆtre, Olivier & Martinez, Jean-Marc, 2009. "Polynomial chaos expansion for sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 94(7), pages 1161-1172.
    4. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    5. Azadeh, A. & Asadzadeh, S.M. & Ghanbari, A., 2010. "An adaptive network-based fuzzy inference system for short-term natural gas demand estimation: Uncertain and complex environments," Energy Policy, Elsevier, vol. 38(3), pages 1529-1536, March.
    6. Debora Mahlke & Alexander Martin & Susanne Moritz, 2007. "A simulated annealing algorithm for transient optimization in gas networks," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 66(1), pages 99-115, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zarei, Javad & Amin-Naseri, Mohammad Reza, 2019. "An integrated optimization model for natural gas supply chain," Energy, Elsevier, vol. 185(C), pages 1114-1130.
    2. Hong, Bingyuan & Qiao, Dan & Li, Yichen & Sun, Xiaoqing & Yang, Baolong & Li, Li & Gong, Jing & Wen, Kai, 2023. "Supply-demand balance of natural gas pipeline network integrating hydraulic and thermal characteristics, energy conservation and carbon reduction," Energy, Elsevier, vol. 283(C).
    3. Pantula, Priyanka D. & Mitra, Kishalay, 2019. "A data-driven approach towards finding closer estimates of optimal solutions under uncertainty for an energy efficient steel casting process," Energy, Elsevier, vol. 189(C).
    4. Olfati, Mohammad & Bahiraei, Mehdi & Veysi, Farzad, 2019. "A novel modification on preheating process of natural gas in pressure reduction stations to improve energy consumption, exergy destruction and CO2 emission: Preheating based on real demand," Energy, Elsevier, vol. 173(C), pages 598-609.
    5. Noor Yusuf & Tareq Al-Ansari, 2023. "Current and Future Role of Natural Gas Supply Chains in the Transition to a Low-Carbon Hydrogen Economy: A Comprehensive Review on Integrated Natural Gas Supply Chain Optimisation Models," Energies, MDPI, vol. 16(22), pages 1-33, November.
    6. Zhou, Dengji & Jia, Xingyun & Ma, Shixi & Shao, Tiemin & Huang, Dawen & Hao, Jiarui & Li, Taotao, 2022. "Dynamic simulation of natural gas pipeline network based on interpretable machine learning model," Energy, Elsevier, vol. 253(C).
    7. Sukharev, Mikhail G. & Kulalaeva, Maria A., 2021. "Identification of model flow parameters and model coefficients with the help of integrated measurements of pipeline system operation parameters," Energy, Elsevier, vol. 232(C).
    8. Wen, Kai & Jiao, Jianfeng & Zhao, Kang & Yin, Xiong & Liu, Yuan & Gong, Jing & Li, Cuicui & Hong, Bingyuan, 2023. "Rapid transient operation control method of natural gas pipeline networks based on user demand prediction," Energy, Elsevier, vol. 264(C).
    9. Chen, Ying & Koch, Thorsten & Zakiyeva, Nazgul & Zhu, Bangzhu, 2020. "Modeling and forecasting the dynamics of the natural gas transmission network in Germany with the demand and supply balance constraint," Applied Energy, Elsevier, vol. 278(C).
    10. Corrado lo Storto, 2019. "An SNA-DEA Prioritization Framework to Identify Critical Nodes of Gas Networks: The Case of the US Interstate Gas Infrastructure," Energies, MDPI, vol. 12(23), pages 1-18, December.
    11. Sukharev, Mikhail G. & Kosova, Ksenia O. & Popov, Ruslan V., 2019. "Mathematical and computer models for identification and optimal control of large-scale gas supply systems," Energy, Elsevier, vol. 184(C), pages 113-122.
    12. Yu, Weichao & Gong, Jing & Song, Shangfei & Huang, Weihe & Li, Yichen & Zhang, Jie & Hong, Bingyuan & Zhang, Ye & Wen, Kai & Duan, Xu, 2019. "Gas supply reliability analysis of a natural gas pipeline system considering the effects of underground gas storages," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    13. Yu, Weichao & Song, Shangfei & Li, Yichen & Min, Yuan & Huang, Weihe & Wen, Kai & Gong, Jing, 2018. "Gas supply reliability assessment of natural gas transmission pipeline systems," Energy, Elsevier, vol. 162(C), pages 853-870.
    14. Dong, Kangyin & Li, Jiaman & Zhang, Haoran, 2023. "LNG point supply of villages and towns in China: Challenges and countermeasures," Applied Energy, Elsevier, vol. 334(C).
    15. He, Chuan & Wu, Lei & Liu, Tianqi & Wei, Wei & Wang, Cheng, 2018. "Co-optimization scheduling of interdependent power and gas systems with electricity and gas uncertainties," Energy, Elsevier, vol. 159(C), pages 1003-1015.
    16. Shukla, Gaurav & Chaturvedi, Nitin Dutt, 2023. "Targeting compression work in hydrogen allocation network with parametric uncertainties," Energy, Elsevier, vol. 262(PA).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Potočnik, Primož & Soldo, Božidar & Šimunović, Goran & Šarić, Tomislav & Jeromen, Andrej & Govekar, Edvard, 2014. "Comparison of static and adaptive models for short-term residential natural gas forecasting in Croatia," Applied Energy, Elsevier, vol. 129(C), pages 94-103.
    2. Azadeh, A. & Asadzadeh, S.M. & Mirseraji, G.H. & Saberi, M., 2015. "An emotional learning-neuro-fuzzy inference approach for optimum training and forecasting of gas consumption estimation models with cognitive data," Technological Forecasting and Social Change, Elsevier, vol. 91(C), pages 47-63.
    3. Jean Gaston Tamba & Salom Ndjakomo Essiane & Emmanuel Flavian Sapnken & Francis Djanna Koffi & Jean Luc Nsouand l & Bozidar Soldo & Donatien Njomo, 2018. "Forecasting Natural Gas: A Literature Survey," International Journal of Energy Economics and Policy, Econjournals, vol. 8(3), pages 216-249.
    4. Sen, Doruk & Günay, M. Erdem & Tunç, K.M. Murat, 2019. "Forecasting annual natural gas consumption using socio-economic indicators for making future policies," Energy, Elsevier, vol. 173(C), pages 1106-1118.
    5. Rehman, Aniqa & Zhu, Jun-Jie & Segovia, Javier & Anderson, Paul R., 2022. "Assessment of deep learning and classical statistical methods on forecasting hourly natural gas demand at multiple sites in Spain," Energy, Elsevier, vol. 244(PA).
    6. Soldo, Božidar, 2012. "Forecasting natural gas consumption," Applied Energy, Elsevier, vol. 92(C), pages 26-37.
    7. Panapakidis, Ioannis P. & Dagoumas, Athanasios S., 2017. "Day-ahead natural gas demand forecasting based on the combination of wavelet transform and ANFIS/genetic algorithm/neural network model," Energy, Elsevier, vol. 118(C), pages 231-245.
    8. Ríos-Mercado, Roger Z. & Borraz-Sánchez, Conrado, 2015. "Optimization problems in natural gas transportation systems: A state-of-the-art review," Applied Energy, Elsevier, vol. 147(C), pages 536-555.
    9. Azadeh, A. & Asadzadeh, S.M. & Saberi, M. & Nadimi, V. & Tajvidi, A. & Sheikalishahi, M., 2011. "A Neuro-fuzzy-stochastic frontier analysis approach for long-term natural gas consumption forecasting and behavior analysis: The cases of Bahrain, Saudi Arabia, Syria, and UAE," Applied Energy, Elsevier, vol. 88(11), pages 3850-3859.
    10. Mustafa Akpinar & M. Fatih Adak & Nejat Yumusak, 2017. "Day-Ahead Natural Gas Demand Forecasting Using Optimized ABC-Based Neural Network with Sliding Window Technique: The Case Study of Regional Basis in Turkey," Energies, MDPI, vol. 10(6), pages 1-20, June.
    11. Nahapetyan Yervand, 2019. "The benefits of the Velvet Revolution in Armenia: Estimation of the short-term economic gains using deep neural networks," Central European Economic Journal, Sciendo, vol. 6(53), pages 286-303, January.
    12. Hayashi, Masayoshi, 2014. "Forecasting welfare caseloads: The case of the Japanese public assistance program," Socio-Economic Planning Sciences, Elsevier, vol. 48(2), pages 105-114.
    13. Guelpa, Elisa & Bischi, Aldo & Verda, Vittorio & Chertkov, Michael & Lund, Henrik, 2019. "Towards future infrastructures for sustainable multi-energy systems: A review," Energy, Elsevier, vol. 184(C), pages 2-21.
    14. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    15. Man Li & Tao Ye & Peijun Shi & Jian Fang, 2015. "Impacts of the global economic crisis and Tohoku earthquake on Sino–Japan trade: a comparative perspective," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(1), pages 541-556, January.
    16. Caglar, Abdullah Emre & Daştan, Muhammet & Avci, Salih Bortecine, 2024. "Persistence of disaggregate energy RD&D expenditures in top-five economies: Evidence from artificial neural network approach," Applied Energy, Elsevier, vol. 365(C).
    17. Schlereth, Christian & Stepanchuk, Tanja & Skiera, Bernd, 2010. "Optimization and analysis of the profitability of tariff structures with two-part tariffs," European Journal of Operational Research, Elsevier, vol. 206(3), pages 691-701, November.
    18. Anna Staszewska-Bystrova & Peter Winker, 2016. "Improved bootstrap prediction intervals for SETAR models," Statistical Papers, Springer, vol. 57(1), pages 89-98, March.
    19. Döpke, Jörg & Fritsche, Ulrich & Müller, Karsten, 2019. "Has macroeconomic forecasting changed after the Great Recession? Panel-based evidence on forecast accuracy and forecaster behavior from Germany," Journal of Macroeconomics, Elsevier, vol. 62(C).
    20. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:134:y:2017:i:c:p:968-983. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.