IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v313y2022ics0306261922003117.html
   My bibliography  Save this article

Multi-aspect efficiency measurement of multi-objective energy planning model dealing with uncertainties

Author

Listed:
  • Ratanakuakangwan, Sudlop
  • Morita, Hiroshi

Abstract

In this study on energy planning, a combination of multi-objective optimization and efficiency measurement is proposed as a means for determining an efficient energy mix that considers the multi-dimensional nature of energy planning and its associated uncertainties. Various multi-objective functions are appended to the proposed optimization model to meet requirements related to energy need, cost, environmental impact, security, social impact, and social benefit. A slacks-based measure methodology is applied to determine the best energy mix from the alternatives produced by the appended model. The energy efficiency of each energy mix is measured from the linear combination of its defined inputs and outputs. The outputs to be maximized include total generated electricity, direct employment, and percentage of generated electricity from renewable energy, while the inputs to be minimized consist of total economic cost, carbon dioxide emission, total social cost, and power-plant-type dependence score. To demonstrate the applicability of the proposed model, a case study of Thailand’s power development plan is featured. Various types of power plants, both fossil fuel-fired and renewable energy-driven are considered in the empirical analysis. The results show that the proposed method can contribute significant improvements, including a reduction in total emissions and in the power-plant-type dependence score (by 31.41% and 25.59%, respectively). It also increases total employment and the proportion of generated electricity from renewable energy plants (by 25.73% and 47.39%, respectively), with marginal tradeoffs of total costs and total social costs (which increase by 8.94% and 13.89%, respectively). Quantitative results from the model could help policy makers efficiently determine an appropriate energy policy—one that optimizes all the various aspects, under a given set of constraints and scenarios of uncertainty.

Suggested Citation

  • Ratanakuakangwan, Sudlop & Morita, Hiroshi, 2022. "Multi-aspect efficiency measurement of multi-objective energy planning model dealing with uncertainties," Applied Energy, Elsevier, vol. 313(C).
  • Handle: RePEc:eee:appene:v:313:y:2022:i:c:s0306261922003117
    DOI: 10.1016/j.apenergy.2022.118883
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922003117
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2022.118883?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. van Beuzekom, Iris & Hodge, Bri-Mathias & Slootweg, Han, 2021. "Framework for optimization of long-term, multi-period investment planning of integrated urban energy systems," Applied Energy, Elsevier, vol. 292(C).
    2. Jing, Rui & Lin, Yufeng & Khanna, Nina & Chen, Xiang & Wang, Meng & Liu, Jiahui & Lin, Jianyi, 2021. "Balancing the Energy Trilemma in energy system planning of coastal cities," Applied Energy, Elsevier, vol. 283(C).
    3. Bi, Gong-Bing & Song, Wen & Zhou, P. & Liang, Liang, 2014. "Does environmental regulation affect energy efficiency in China's thermal power generation? Empirical evidence from a slacks-based DEA model," Energy Policy, Elsevier, vol. 66(C), pages 537-546.
    4. Ioannou, Anastasia & Fuzuli, Gulistiani & Brennan, Feargal & Yudha, Satya Widya & Angus, Andrew, 2019. "Multi-stage stochastic optimization framework for power generation system planning integrating hybrid uncertainty modelling," Energy Economics, Elsevier, vol. 80(C), pages 760-776.
    5. Cartelle Barros, Juan José & Lara Coira, Manuel & de la Cruz López, María Pilar & del Caño Gochi, Alfredo, 2017. "Comparative analysis of direct employment generated by renewable and non-renewable power plants," Energy, Elsevier, vol. 139(C), pages 542-554.
    6. Gu, Chenjia & Zhang, Yao & Wang, Jianxue & Li, Qingtao, 2021. "Joint planning of electrical storage and gas storage in power-gas distribution network considering high-penetration electric vehicle and gas vehicle," Applied Energy, Elsevier, vol. 301(C).
    7. Kaya, Tolga & Kahraman, Cengiz, 2010. "Multicriteria renewable energy planning using an integrated fuzzy VIKOR & AHP methodology: The case of Istanbul," Energy, Elsevier, vol. 35(6), pages 2517-2527.
    8. Xie, Y.L. & Huang, G.H. & Li, W. & Ji, L., 2014. "Carbon and air pollutants constrained energy planning for clean power generation with a robust optimization model—A case study of Jining City, China," Applied Energy, Elsevier, vol. 136(C), pages 150-167.
    9. Shrivastava, Naveen & Sharma, Seema & Chauhan, Kavita, 2012. "Efficiency assessment and benchmarking of thermal power plants in India," Energy Policy, Elsevier, vol. 40(C), pages 159-176.
    10. Xin-gang, Zhao & Zhen, Wei, 2019. "The technical efficiency of China's wind power list enterprises: An estimation based on DEA method and micro-data," Renewable Energy, Elsevier, vol. 133(C), pages 470-479.
    11. Yu, Jiah & Ryu, Jun-Hyung & Lee, In-beum, 2019. "A stochastic optimization approach to the design and operation planning of a hybrid renewable energy system," Applied Energy, Elsevier, vol. 247(C), pages 212-220.
    12. Sueyoshi, Toshiyuki & Goto, Mika, 2013. "Returns to scale vs. damages to scale in data envelopment analysis: An impact of U.S. clean air act on coal-fired power plants," Omega, Elsevier, vol. 41(2), pages 164-175.
    13. Zeng, Yuan & Guo, Waiying & Wang, Hongmei & Zhang, Fengbin, 2020. "A two-stage evaluation and optimization method for renewable energy development based on data envelopment analysis," Applied Energy, Elsevier, vol. 262(C).
    14. Thangavelu, Sundar Raj & Khambadkone, Ashwin M. & Karimi, Iftekhar A., 2015. "Long-term optimal energy mix planning towards high energy security and low GHG emission," Applied Energy, Elsevier, vol. 154(C), pages 959-969.
    15. Ratanakuakangwan, Sudlop & Morita, Hiroshi, 2021. "Hybrid stochastic robust optimization and robust optimization for energy planning – A social impact-constrained case study," Applied Energy, Elsevier, vol. 298(C).
    16. George Battese & D. Rao & Christopher O'Donnell, 2004. "A Metafrontier Production Function for Estimation of Technical Efficiencies and Technology Gaps for Firms Operating Under Different Technologies," Journal of Productivity Analysis, Springer, vol. 21(1), pages 91-103, January.
    17. Joe Zhu, 2009. "Quantitative Models for Performance Evaluation and Benchmarking," International Series in Operations Research and Management Science, Springer, number 978-0-387-85982-8, December.
    18. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    19. Li, Chengzhou & Wang, Ningling & Wang, Zhuo & Dou, Xiaoxiao & Zhang, Yumeng & Yang, Zhiping & Maréchal, François & Wang, Ligang & Yang, Yongping, 2022. "Energy hub-based optimal planning framework for user-level integrated energy systems: Considering synergistic effects under multiple uncertainties," Applied Energy, Elsevier, vol. 307(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. Pan, Yue & Qin, Jianjun, 2022. "A novel probabilistic modeling framework for wind speed with highlight of extremes under data discrepancy and uncertainty," Applied Energy, Elsevier, vol. 326(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. Ratanakuakangwan, Sudlop & Morita, Hiroshi, 2022. "An efficient energy planning model optimizing cost, emission, and social impact with different carbon tax scenarios," Applied Energy, Elsevier, vol. 325(C).
    2. Ratanakuakangwan, Sudlop & Morita, Hiroshi, 2021. "Hybrid stochastic robust optimization and robust optimization for energy planning – A social impact-constrained case study," Applied Energy, Elsevier, vol. 298(C).
    3. Liu, John S. & Lu, Louis Y.Y. & Lu, Wen-Min & Lin, Bruce J.Y., 2013. "A survey of DEA applications," Omega, Elsevier, vol. 41(5), pages 893-902.
    4. Jindal, Abhinav & Nilakantan, Rahul, 2021. "Falling efficiency levels of Indian coal-fired power plants: A slacks-based analysis," Energy Economics, Elsevier, vol. 93(C).
    5. Ping Wang & Bangzhu Zhu & Xueping Tao & Rui Xie, 2017. "Measuring regional energy efficiencies in China: a meta-frontier SBM-Undesirable approach," 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. 85(2), pages 793-809, January.
    6. Hwai-Shuh Shieh & Jin-Li Hu & Yong-Ze Ang, 2020. "Efficiency of Life Insurance Companies: An Empirical Study in Mainland China and Taiwan," SAGE Open, , vol. 10(1), pages 21582440209, February.
    7. Tao Xu & Jianxin You & Hui Li & Luning Shao, 2020. "Energy Efficiency Evaluation Based on Data Envelopment Analysis: A Literature Review," Energies, MDPI, vol. 13(14), pages 1-20, July.
    8. Yongqi Feng & Haolin Zhang & Yung-ho Chiu & Tzu-Han Chang, 2021. "Innovation efficiency and the impact of the institutional quality: a cross-country analysis using the two-stage meta-frontier dynamic network DEA model," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3091-3129, April.
    9. Irawan, Chandra Ade & Jones, Dylan & Hofman, Peter S. & Zhang, Lina, 2023. "Integrated strategic energy mix and energy generation planning with multiple sustainability criteria and hierarchical stakeholders," European Journal of Operational Research, Elsevier, vol. 308(2), pages 864-883.
    10. Chin‐wei Huang & Hsiao‐Yin Chen, 2023. "Using nonradial metafrontier data envelopment analysis to evaluate the metatechnology and metafactor ratios for the Taiwanese hotel industry," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(4), pages 1904-1919, June.
    11. Hongli Liu & Xiaoyu Yan & Jinhua Cheng & Jun Zhang & Yan Bu, 2021. "Driving Factors for the Spatiotemporal Heterogeneity in Technical Efficiency of China’s New Energy Industry," Energies, MDPI, vol. 14(14), pages 1-21, July.
    12. Wang, Yubin & Dong, Wei & Yang, Qiang, 2022. "Multi-stage optimal energy management of multi-energy microgrid in deregulated electricity markets," Applied Energy, Elsevier, vol. 310(C).
    13. Frederick, Joshua D. & Fung, Derrick W.H. & Yang, Charles C. & Yeh, Jason J.H., 2022. "Individual health insurance reforms in the U.S.: Expanding interstate markets, Medicare for all, or Medicaid for all?," European Journal of Operational Research, Elsevier, vol. 297(2), pages 753-765.
    14. Sueyoshi, Toshiyuki & Goto, Mika, 2015. "Environmental assessment on coal-fired power plants in U.S. north-east region by DEA non-radial measurement," Energy Economics, Elsevier, vol. 50(C), pages 125-139.
    15. Fan, Jing-Li & Zhang, Hao & Zhang, Xian, 2020. "Unified efficiency measurement of coal-fired power plants in China considering group heterogeneity and technological gaps," Energy Economics, Elsevier, vol. 88(C).
    16. Zhang, Ning & Zhao, Yu & Wang, Na, 2022. "Is China's energy policy effective for power plants? Evidence from the 12th Five-Year Plan energy saving targets," Energy Economics, Elsevier, vol. 112(C).
    17. Ying Li & Tai‐Yu Lin & Yung‐ho Chiu & Shu‐Ning Lin & Tzu‐Han Chang, 2021. "Impact of alliances and delay rate on airline performance," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(6), pages 1607-1618, September.
    18. Taleb, Mushtaq & Khalid, Ruzelan & Ramli, Razamin & Ghasemi, Mohammad Reza & Ignatius, Joshua, 2022. "An integrated bi-objective data envelopment analysis model for measuring returns to scale," European Journal of Operational Research, Elsevier, vol. 296(3), pages 967-979.
    19. Meiling Wang & Silu Pang & Ikram Hmani & Ilham Hmani & Cunfang Li & Zhengxia He, 2021. "Towards sustainable development: How does technological innovation drive the increase in green total factor productivity?," Sustainable Development, John Wiley & Sons, Ltd., vol. 29(1), pages 217-227, January.
    20. Li, Feng & Zhang, Danlu & Zhang, Jinyu & Kou, Gang, 2022. "Measuring the energy production and utilization efficiency of Chinese thermal power industry with the fixed-sum carbon emission constraint," International Journal of Production Economics, Elsevier, vol. 252(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:appene:v:313:y:2022:i:c:s0306261922003117. 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/405891/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.