IDEAS home Printed from https://ideas.repec.org/a/pal/jorapm/v20y2021i2d10.1057_s41272-021-00294-2.html
   My bibliography  Save this article

Revenue optimization modeling for renewable energy resource mix for sustainable development

Author

Listed:
  • Neha Gupta

    (Amity University Uttar Pradesh)

  • Mohini Agarwal

    (Amity University Uttar Pradesh)

  • Pratibha Garg

    (Amity University Uttar Pradesh)

  • Manoj Bansal

    (Amity University Uttar Pradesh)

Abstract

The adoption and attainment of sustainable development goals have diverted developing nations like India towards the use of renewable resource for meeting the growing need for electricity. With the advancement in technology for generating electricity, the concern is to develop such renewable energy mix that can help satisfy the electricity demand and be environment friendly. In this paper, one such problem wherein the three conflicting criterion such as maximization of energy savings, maximization of efficiency and minimization of the cost of installation has been considered for designing a multi-objective optimization model to meet the growing demand of electricity. Interactive fuzzy goal programming technique with three different functional forms of membership function namely linear, exponential, and hyperbolic have been used to solve the proposed problem. The results have shown substantial adequacy in meeting the desired load demand and at the same time reduction in installation cost has been seen which in turn will impact the revenue generation.

Suggested Citation

  • Neha Gupta & Mohini Agarwal & Pratibha Garg & Manoj Bansal, 2021. "Revenue optimization modeling for renewable energy resource mix for sustainable development," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 20(2), pages 108-115, April.
    • Handle: RePEc:pal:jorapm:v:20:y:2021:i:2:d:10.1057_s41272-021-00294-2
    DOI: 10.1057/s41272-021-00294-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41272-021-00294-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1057/s41272-021-00294-2?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. R. E. Bellman & L. A. Zadeh, 1970. "Decision-Making in a Fuzzy Environment," Management Science, INFORMS, vol. 17(4), pages 141-164, December.
    2. Rajper, Samina & Amin, Imran J., 2012. "Optimization of wind turbine micrositing: A comparative study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(8), pages 5485-5492.
    3. Shabani, Nazanin & Akhtari, Shaghaygh & Sowlati, Taraneh, 2013. "Value chain optimization of forest biomass for bioenergy production: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 299-311.
    4. Fadaee, M. & Radzi, M.A.M., 2012. "Multi-objective optimization of a stand-alone hybrid renewable energy system by using evolutionary algorithms: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 3364-3369.
    5. Ahn, Joongha & Woo, JongRoul & Lee, Jongsu, 2015. "Optimal allocation of energy sources for sustainable development in South Korea: Focus on the electric power generation industry," Energy Policy, Elsevier, vol. 78(C), pages 78-90.
    6. Ogunjuyigbe, A.S.O. & Ayodele, T.R. & Akinola, O.A., 2016. "Optimal allocation and sizing of PV/Wind/Split-diesel/Battery hybrid energy system for minimizing life cycle cost, carbon emission and dump energy of remote residential building," Applied Energy, Elsevier, vol. 171(C), pages 153-171.
    7. Sharma, Naveen & Varun, & Siddhartha,, 2012. "Stochastic techniques used for optimization in solar systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(3), pages 1399-1411.
    8. Tan, Wen-Shan & Hassan, Mohammad Yusri & Majid, Md Shah & Abdul Rahman, Hasimah, 2013. "Optimal distributed renewable generation planning: A review of different approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 18(C), pages 626-645.
    9. Erdinc, O. & Uzunoglu, M., 2012. "Optimum design of hybrid renewable energy systems: Overview of different approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(3), pages 1412-1425.
    10. Iqbal, M. & Azam, M. & Naeem, M. & Khwaja, A.S. & Anpalagan, A., 2014. "Optimization classification, algorithms and tools for renewable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 640-654.
    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. Iddrisu Awudu & William Wilson & George Baah & Vinay Gonela & Mariama Yakubu, 2024. "Revenue maximization and pricing: an ethanol supply chain and logistical strategy perspectives," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 23(1), pages 62-75, February.

    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. Iqbal, M. & Azam, M. & Naeem, M. & Khwaja, A.S. & Anpalagan, A., 2014. "Optimization classification, algorithms and tools for renewable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 640-654.
    2. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    3. Tezer, Tuba & Yaman, Ramazan & Yaman, Gülşen, 2017. "Evaluation of approaches used for optimization of stand-alone hybrid renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 840-853.
    4. Amrollahi, Mohammad Hossein & Bathaee, Seyyed Mohammad Taghi, 2017. "Techno-economic optimization of hybrid photovoltaic/wind generation together with energy storage system in a stand-alone micro-grid subjected to demand response," Applied Energy, Elsevier, vol. 202(C), pages 66-77.
    5. Bundhoo, Zumar M.A., 2018. "Renewable energy exploitation in the small island developing state of Mauritius: Current practice and future potential," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2029-2038.
    6. Perera, A.T.D. & Nik, Vahid M. & Mauree, Dasaraden & Scartezzini, Jean-Louis, 2017. "Electrical hubs: An effective way to integrate non-dispatchable renewable energy sources with minimum impact to the grid," Applied Energy, Elsevier, vol. 190(C), pages 232-248.
    7. Perera, A.T.D. & Nik, Vahid M. & Mauree, Dasaraden & Scartezzini, Jean-Louis, 2017. "An integrated approach to design site specific distributed electrical hubs combining optimization, multi-criterion assessment and decision making," Energy, Elsevier, vol. 134(C), pages 103-120.
    8. Khatib, Tamer & Mohamed, Azah & Sopian, K., 2013. "A review of photovoltaic systems size optimization techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 454-465.
    9. Siddaiah, Rajanna & Saini, R.P., 2016. "A review on planning, configurations, modeling and optimization techniques of hybrid renewable energy systems for off grid applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 376-396.
    10. Nadjemi, O. & Nacer, T. & Hamidat, A. & Salhi, H., 2017. "Optimal hybrid PV/wind energy system sizing: Application of cuckoo search algorithm for Algerian dairy farms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1352-1365.
    11. Al Busaidi, Ahmed Said & Kazem, Hussein A & Al-Badi, Abdullah H & Farooq Khan, Mohammad, 2016. "A review of optimum sizing of hybrid PV–Wind renewable energy systems in oman," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 185-193.
    12. Mayer, Martin János & Szilágyi, Artúr & Gróf, Gyula, 2020. "Environmental and economic multi-objective optimization of a household level hybrid renewable energy system by genetic algorithm," Applied Energy, Elsevier, vol. 269(C).
    13. Abo-Elyousr, Farag K. & Elnozahy, Ahmed, 2018. "Bi-objective economic feasibility of hybrid micro-grid systems with multiple fuel options for islanded areas in Egypt," Renewable Energy, Elsevier, vol. 128(PA), pages 37-56.
    14. Yang, Yuqing & Bremner, Stephen & Menictas, Chris & Kay, Merlinde, 2018. "Battery energy storage system size determination in renewable energy systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 109-125.
    15. Seyfettin Vadi & Sanjeevikumar Padmanaban & Ramazan Bayindir & Frede Blaabjerg & Lucian Mihet-Popa, 2019. "A Review on Optimization and Control Methods Used to Provide Transient Stability in Microgrids," Energies, MDPI, vol. 12(18), pages 1-20, September.
    16. Gamarra, Carlos & Guerrero, Josep M., 2015. "Computational optimization techniques applied to microgrids planning: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 413-424.
    17. Perera, A.T.D. & Attalage, R.A. & Perera, K.K.C.K. & Dassanayake, V.P.C., 2013. "Designing standalone hybrid energy systems minimizing initial investment, life cycle cost and pollutant emission," Energy, Elsevier, vol. 54(C), pages 220-230.
    18. Ajagekar, Akshay & You, Fengqi, 2022. "Quantum computing and quantum artificial intelligence for renewable and sustainable energy: A emerging prospect towards climate neutrality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    19. Destro, Nicola & Benato, Alberto & Stoppato, Anna & Mirandola, Alberto, 2016. "Components design and daily operation optimization of a hybrid system with energy storages," Energy, Elsevier, vol. 117(P2), pages 569-577.
    20. Myeong Jin Ko & Yong Shik Kim & Min Hee Chung & Hung Chan Jeon, 2015. "Multi-Objective Optimization Design for a Hybrid Energy System Using the Genetic Algorithm," Energies, MDPI, vol. 8(4), pages 1-26, April.

    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:pal:jorapm:v:20:y:2021:i:2:d:10.1057_s41272-021-00294-2. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave.com .

    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.