IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v188y2023ics1364032123006986.html
   My bibliography  Save this article

Data-driven stochastic robust optimization of sustainable utility system

Author

Listed:
  • Wang, Qipeng
  • Zhao, Liang

Abstract

Sustainable utility systems reduce reliance on fossil fuels by using renewable energy sources. Multi-scale uncertainties associated with renewable energy and utility systems pose challenges to the modeling and optimization of sustainable utility systems. This study proposes a sustainable retrofit framework for utility systems based on a data-driven stochastic robust optimization approach. Kernel density estimation and fuzzy clustering were employed to capture the uncertainty features in a holistic framework. A life cycle assessment approach was used to calculate the global warming potential (GWP) of the sustainable utility system, and a multi-objective environmental and economic optimization model was developed. A nested decomposition-based algorithm was proposed to solve a large-scale mixed-integer nonlinear programming problem. Finally, a case study of an industrial utility system was conducted to demonstrate the effectiveness of the proposed method. The optimization results show that the proposed method reduces GWP by 9 % after introducing renewable energy and achieves a balance between economic and environmental performance.

Suggested Citation

  • Wang, Qipeng & Zhao, Liang, 2023. "Data-driven stochastic robust optimization of sustainable utility system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    • Handle: RePEc:eee:rensus:v:188:y:2023:i:c:s1364032123006986
    DOI: 10.1016/j.rser.2023.113841
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032123006986
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2023.113841?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. Ahmad, Tanveer & Madonski, Rafal & Zhang, Dongdong & Huang, Chao & Mujeeb, Asad, 2022. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    2. Ahmadi, Bahman & Ceylan, Oguzhan & Ozdemir, Aydogan & Fotuhi-Firuzabad, Mahmoud, 2022. "A multi-objective framework for distributed energy resources planning and storage management," Applied Energy, Elsevier, vol. 314(C).
    3. Chen, Jincheng & Wang, Feng & Stelson, Kim A., 2018. "A mathematical approach to minimizing the cost of energy for large utility wind turbines," Applied Energy, Elsevier, vol. 228(C), pages 1413-1422.
    4. Al-Alili, A. & Islam, M.D. & Kubo, I. & Hwang, Y. & Radermacher, R., 2012. "Modeling of a solar powered absorption cycle for Abu Dhabi," Applied Energy, Elsevier, vol. 93(C), pages 160-167.
    5. Yang, Xiaolei & Pakula, Maggie & Sotiropoulos, Fotis, 2018. "Large-eddy simulation of a utility-scale wind farm in complex terrain," Applied Energy, Elsevier, vol. 229(C), pages 767-777.
    6. Lasemi, Mohammad Ali & Arabkoohsar, Ahmad & Hajizadeh, Amin & Mohammadi-ivatloo, Behnam, 2022. "A comprehensive review on optimization challenges of smart energy hubs under uncertainty factors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    7. Zhao, Liang & You, Fengqi, 2019. "A data-driven approach for industrial utility systems optimization under uncertainty," Energy, Elsevier, vol. 182(C), pages 559-569.
    8. Zhao, Ning & You, Fengqi, 2022. "Sustainable power systems operations under renewable energy induced disjunctive uncertainties via machine learning-based robust optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    9. Zheng, Ji-Lu & Zhu, Ya-Hong & Su, Hong-Yu & Sun, Guo-Tao & Kang, Fu-Ren & Zhu, Ming-Qiang, 2022. "Life cycle assessment and techno-economic analysis of fuel ethanol production via bio-oil fermentation based on a centralized-distribution model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    10. A. Ben-Tal & A. Nemirovski, 1998. "Robust Convex Optimization," Mathematics of Operations Research, INFORMS, vol. 23(4), pages 769-805, November.
    11. Mansouri, Seyed Amir & Nematbakhsh, Emad & Ahmarinejad, Amir & Jordehi, Ahmad Rezaee & Javadi, Mohammad Sadegh & Marzband, Mousa, 2022. "A hierarchical scheduling framework for resilience enhancement of decentralized renewable-based microgrids considering proactive actions and mobile units," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    12. Hou, Tianfeng & Nuyens, Dirk & Roels, Staf & Janssen, Hans, 2019. "Quasi-Monte Carlo based uncertainty analysis: Sampling efficiency and error estimation in engineering applications," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    13. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    14. Torkayesh, Ali Ebadi & Rajaeifar, Mohammad Ali & Rostom, Madona & Malmir, Behnam & Yazdani, Morteza & Suh, Sangwon & Heidrich, Oliver, 2022. "Integrating life cycle assessment and multi criteria decision making for sustainable waste management: Key issues and recommendations for future studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    15. A. Charnes & W. W. Cooper, 1959. "Chance-Constrained Programming," Management Science, INFORMS, vol. 6(1), pages 73-79, October.
    16. Shunichi Ohmori, 2021. "A Predictive Prescription Using Minimum Volume k -Nearest Neighbor Enclosing Ellipsoid and Robust Optimization," Mathematics, MDPI, vol. 9(2), pages 1-16, January.
    17. Shen, Feifei & Zhao, Liang & Wang, Meihong & Du, Wenli & Qian, Feng, 2022. "Data-driven adaptive robust optimization for energy systems in ethylene plant under demand uncertainty," Applied Energy, Elsevier, vol. 307(C).
    18. Sakki, G.K. & Tsoukalas, I. & Kossieris, P. & Makropoulos, C. & Efstratiadis, A., 2022. "Stochastic simulation-optimization framework for the design and assessment of renewable energy systems under uncertainty," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    19. Thirunavukkarasu, M. & Sawle, Yashwant & Lala, Himadri, 2023. "A comprehensive review on optimization of hybrid renewable energy systems using various optimization techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 176(C).
    20. Yang, Qing & Huang, Tianyue & Chen, Fuying & Uche, Javier & Wang, Yuxuan & Yuan, Peng & Zhang, Yinya & Li, Jianlan, 2022. "Water saving potential for large-scale photovoltaic power generation in China: Based on life cycle assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    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. Li, Ruiqi & Ren, Hongbo & Wu, Qiong & Li, Qifen & Gao, Weijun, 2024. "Cooperative economic dispatch of EV-HV coupled electric-hydrogen integrated energy system considering V2G response and carbon trading," Renewable Energy, Elsevier, vol. 227(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. Shen, Feifei & Zhao, Liang & Wang, Meihong & Du, Wenli & Qian, Feng, 2022. "Data-driven adaptive robust optimization for energy systems in ethylene plant under demand uncertainty," Applied Energy, Elsevier, vol. 307(C).
    2. L. Jeff Hong & Zhiyuan Huang & Henry Lam, 2021. "Learning-Based Robust Optimization: Procedures and Statistical Guarantees," Management Science, INFORMS, vol. 67(6), pages 3447-3467, June.
    3. Evers, L. & Glorie, K.M. & van der Ster, S. & Barros, A.I. & Monsuur, H., 2012. "The Orienteering Problem under Uncertainty Stochastic Programming and Robust Optimization compared," Econometric Institute Research Papers EI 2012-21, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    4. Hatami-Marbini, Adel & Arabmaldar, Aliasghar, 2021. "Robustness of Farrell cost efficiency measurement under data perturbations: Evidence from a US manufacturing application," European Journal of Operational Research, Elsevier, vol. 295(2), pages 604-620.
    5. Marla, Lavanya & Rikun, Alexander & Stauffer, Gautier & Pratsini, Eleni, 2020. "Robust modeling and planning: Insights from three industrial applications," Operations Research Perspectives, Elsevier, vol. 7(C).
    6. Algo Carè & Simone Garatti & Marco C. Campi, 2019. "The wait-and-judge scenario approach applied to antenna array design," Computational Management Science, Springer, vol. 16(3), pages 481-499, July.
    7. Baron, Opher & Berman, Oded & Fazel-Zarandi, Mohammad M. & Roshanaei, Vahid, 2019. "Almost Robust Discrete Optimization," European Journal of Operational Research, Elsevier, vol. 276(2), pages 451-465.
    8. Han, Biao & Shang, Chao & Huang, Dexian, 2021. "Multiple kernel learning-aided robust optimization: Learning algorithm, computational tractability, and usage in multi-stage decision-making," European Journal of Operational Research, Elsevier, vol. 292(3), pages 1004-1018.
    9. Wenqing Chen & Melvyn Sim & Jie Sun & Chung-Piaw Teo, 2010. "From CVaR to Uncertainty Set: Implications in Joint Chance-Constrained Optimization," Operations Research, INFORMS, vol. 58(2), pages 470-485, April.
    10. Stefan Mišković, 2017. "A VNS-LP algorithm for the robust dynamic maximal covering location problem," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(4), pages 1011-1033, October.
    11. Chassein, André & Dokka, Trivikram & Goerigk, Marc, 2019. "Algorithms and uncertainty sets for data-driven robust shortest path problems," European Journal of Operational Research, Elsevier, vol. 274(2), pages 671-686.
    12. Beck, Yasmine & Ljubić, Ivana & Schmidt, Martin, 2023. "A survey on bilevel optimization under uncertainty," European Journal of Operational Research, Elsevier, vol. 311(2), pages 401-426.
    13. Antonio G. Martín & Manuel Díaz-Madroñero & Josefa Mula, 2020. "Master production schedule using robust optimization approaches in an automobile second-tier supplier," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(1), pages 143-166, March.
    14. Hamed Mamani & Shima Nassiri & Michael R. Wagner, 2017. "Closed-Form Solutions for Robust Inventory Management," Management Science, INFORMS, vol. 63(5), pages 1625-1643, May.
    15. Bilsel, R. Ufuk & Ravindran, A., 2011. "A multiobjective chance constrained programming model for supplier selection under uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 45(8), pages 1284-1300, September.
    16. Steve Zymler & Daniel Kuhn & Berç Rustem, 2013. "Worst-Case Value at Risk of Nonlinear Portfolios," Management Science, INFORMS, vol. 59(1), pages 172-188, July.
    17. Shunichi Ohmori, 2021. "A Predictive Prescription Using Minimum Volume k -Nearest Neighbor Enclosing Ellipsoid and Robust Optimization," Mathematics, MDPI, vol. 9(2), pages 1-16, January.
    18. Walid Ben-Ameur & Adam Ouorou & Guanglei Wang & Mateusz Żotkiewicz, 2018. "Multipolar robust optimization," EURO Journal on Computational Optimization, Springer;EURO - The Association of European Operational Research Societies, vol. 6(4), pages 395-434, December.
    19. Roberto Gomes de Mattos & Fabricio Oliveira & Adriana Leiras & Abdon Baptista de Paula Filho & Paulo Gonçalves, 2019. "Robust optimization of the insecticide-treated bed nets procurement and distribution planning under uncertainty for malaria prevention and control," Annals of Operations Research, Springer, vol. 283(1), pages 1045-1078, December.
    20. Soyster, A.L. & Murphy, F.H., 2013. "A unifying framework for duality and modeling in robust linear programs," Omega, Elsevier, vol. 41(6), pages 984-997.

    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:rensus:v:188:y:2023:i:c:s1364032123006986. 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.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.