IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v203y2020ics036054422030877x.html
   My bibliography  Save this article

On the optimization of water-energy nexus in shale gas network under price uncertainties

Author

Listed:
  • Oke, Doris
  • Mukherjee, Rajib
  • Sengupta, Debalina
  • Majozi, Thokozani
  • El-Halwagi, Mahmoud

Abstract

This study develops a framework for water-energy nexus optimization in shale gas production and distribution network under uncertainty. Sustainable design of the network is achieved through treatment of wastewater using thermal membrane distillation, whereby a comprehensive design model is integrated within the network to account for energy requirement of the unit. The proposed model also accounts for the problem of scheduling hydraulic fracturing using a continuous time formulation. Various uncertainties are associated with the network. Among different uncertain variables, uncertainties associated with price and demand are crucial as they can affect the optimal configuration significantly. Incorporation of uncertainty in the model seeks to meet the demand of natural gas consumers whilst accounting for the uncertainties associated with the price of final products. Uncertainties are modelled via randomly generated scenarios using beta distribution of the price generated using historical data. The stochastic model is applied to a case study through the maximization of net profit. Three different scenarios are considered for analysis. Results from the three different scenarios show 14.39%, 11.49%, and 12.34% increase in profit respectively as compared to the deterministic approach. Solving all the scenarios together gives an ensemble-average solution with 13.74% increase in expected profit. Savings in the freshwater requirement for fracturing and the energy associated with water management amounted to 23.2% and 42.7%, respectively.

Suggested Citation

  • Oke, Doris & Mukherjee, Rajib & Sengupta, Debalina & Majozi, Thokozani & El-Halwagi, Mahmoud, 2020. "On the optimization of water-energy nexus in shale gas network under price uncertainties," Energy, Elsevier, vol. 203(C).
  • Handle: RePEc:eee:energy:v:203:y:2020:i:c:s036054422030877x
    DOI: 10.1016/j.energy.2020.117770
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422030877X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2020.117770?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. Fadhil Y. Al-Aboosi & Mahmoud M. El-Halwagi, 2019. "A Stochastic Optimization Approach to the Design of Shale Gas/Oil Wastewater Treatment Systems with Multiple Energy Sources under Uncertainty," Sustainability, MDPI, vol. 11(18), pages 1-39, September.
    2. Jorge Chebeir & Aryan Geraili & Jose Romagnoli, 2017. "Development of Shale Gas Supply Chain Network under Market Uncertainties," Energies, MDPI, vol. 10(2), pages 1-31, February.
    3. James B. McDonald, 2008. "Some Generalized Functions for the Size Distribution of Income," Economic Studies in Inequality, Social Exclusion, and Well-Being, in: Duangkamon Chotikapanich (ed.), Modeling Income Distributions and Lorenz Curves, chapter 3, pages 37-55, Springer.
    4. Jiang, Zhiqiang & Li, Rongbo & Li, Anqiang & Ji, Changming, 2018. "Runoff forecast uncertainty considered load adjustment model of cascade hydropower stations and its application," Energy, Elsevier, vol. 158(C), pages 693-708.
    5. Oke, Doris & Mukherjee, Rajib & Sengupta, Debalina & Majozi, Thokozani & El-Halwagi, Mahmoud M., 2019. "Optimization of water-energy nexus in shale gas exploration: From production to transmission," Energy, Elsevier, vol. 183(C), pages 651-669.
    6. J. David Hughes, 2013. "A reality check on the shale revolution," Nature, Nature, vol. 494(7437), pages 307-308, February.
    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. Haider, Md Alquma & Chaturvedi, Nitin Dutt, 2023. "A mathematical formulation for robust targeting in heat integrated water allocation network," Energy, Elsevier, vol. 264(C).
    2. Huang, Shanshan & Suo, Cai & Guo, Junhong & Lv, Jing & Jing, Rui & Yu, Lei & Fan, Yurui & Ding, Yanming, 2023. "Balancing the water-energy dilemma in nexus system planning with bi-level and multi-uncertainty," Energy, Elsevier, vol. 278(C).
    3. Mu, Yunfei & Wang, Congshan & Cao, Yan & Jia, Hongjie & Zhang, Qingzhu & Yu, Xiaodan, 2022. "A CVaR-based risk assessment method for park-level integrated energy system considering the uncertainties and correlation of energy prices," Energy, Elsevier, vol. 247(C).

    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. Fadhil Y. Al-Aboosi & Mahmoud M. El-Halwagi, 2019. "A Stochastic Optimization Approach to the Design of Shale Gas/Oil Wastewater Treatment Systems with Multiple Energy Sources under Uncertainty," Sustainability, MDPI, vol. 11(18), pages 1-39, September.
    2. Dickens, Richard & Machin, Stephen & Manning, Alan, 1998. "Estimating the effect of minimum wages on employment from the distribution of wages: A critical view," Labour Economics, Elsevier, vol. 5(2), pages 109-134, June.
    3. Schweri, Juerg & Hartog, Joop & Wolter, Stefan C., 2011. "Do students expect compensation for wage risk?," Economics of Education Review, Elsevier, vol. 30(2), pages 215-227, April.
    4. Yang Lu, 2019. "Flexible (panel) regression models for bivariate count–continuous data with an insurance application," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(4), pages 1503-1521, October.
    5. Timofeeva, Anastasiia, 2015. "On endogeneity of consumer expenditures in the estimation of households demand system," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 37(1), pages 87-106.
    6. Meya, Jasper N. & Drupp, Moritz A. & Hanley, Nick, 2021. "Testing structural benefit transfer: The role of income inequality," Resource and Energy Economics, Elsevier, vol. 64(C).
    7. Jie Zhang & Xizhe Li & Weijun Shen & Shusheng Gao & Huaxun Liu & Liyou Ye & Feifei Fang, 2020. "Study of the Effect of Movable Water Saturation on Gas Production in Tight Sandstone Gas Reservoirs," Energies, MDPI, vol. 13(18), pages 1-14, September.
    8. Flachaire, Emmanuel & Nunez, Olivier, 2007. "Estimation of the income distribution and detection of subpopulations: An explanatory model," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3368-3380, April.
    9. Chotikapanich, Duangkamon & Griffiths, William E, 2002. "Estimating Lorenz Curves Using a Dirichlet Distribution," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 290-295, April.
    10. Abbring, Jaap H. & van den Berg, Gerard J., 2003. "A Simple Procedure for the Evaluation of Treatment Effects on Duration Variables," IZA Discussion Papers 810, Institute of Labor Economics (IZA).
    11. Chotikapanich, Duangkamon & Griffiths, William E. & Rao, D.S. Prasada & Karunarathne, Wasana, 2014. "Income Distributions, Inequality, and Poverty in Asia, 1992–2010," ADBI Working Papers 468, Asian Development Bank Institute.
    12. Schluter, Christian & van Garderen, Kees Jan, 2009. "Edgeworth expansions and normalizing transforms for inequality measures," Journal of Econometrics, Elsevier, vol. 150(1), pages 16-29, May.
    13. van den Berg, Gerard J., 2007. "On the uniqueness of optimal prices set by monopolistic sellers," Journal of Econometrics, Elsevier, vol. 141(2), pages 482-491, December.
    14. Mathias Silva, 2023. "Parametric models of income distributions integrating misreporting and non-response mechanisms," AMSE Working Papers 2311, Aix-Marseille School of Economics, France.
    15. Vladimir Hlasny & Paolo Verme, 2022. "The Impact of Top Incomes Biases on the Measurement of Inequality in the United States," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(4), pages 749-788, August.
    16. Hajargasht, Gholamreza & Griffiths, William E., 2013. "Pareto–lognormal distributions: Inequality, poverty, and estimation from grouped income data," Economic Modelling, Elsevier, vol. 33(C), pages 593-604.
    17. Denis Beninger & François Laisney, 2006. "On the performance of unitary models of household labor supply estimated on “collective” data with taxation," Cahiers d'Economie et Sociologie Rurales, INRA Department of Economics, vol. 81, pages 5-36.
    18. Oded Stark & Wiktor Budzinski & Grzegorz Kosiorowski, 2019. "The pure effect of social preferences on regional location choices: The evolving dynamics of convergence to a steady state population distribution," Journal of Regional Science, Wiley Blackwell, vol. 59(5), pages 883-909, November.
    19. Saissi Hassani, Samir & Dionne, Georges, 2021. "The New International Regulation of Market Risk: Roles of VaR and CVaR in Model Validation," Working Papers 21-1, HEC Montreal, Canada Research Chair in Risk Management.
    20. Michael McAleer & Hang K. Ryu & Daniel J. Slottje, 2019. "A New Inequality Measure that is Sensitive to Extreme Values and Asymmetries," Advances in Decision Sciences, Asia University, Taiwan, vol. 23(1), pages 31-61, March.

    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:203:y:2020:i:c:s036054422030877x. 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.