IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i18p5694-d632724.html
   My bibliography  Save this article

Two-Stage Stochastic Model to Invest in Distributed Generation Considering the Long-Term Uncertainties

Author

Listed:
  • Jorge Luis Angarita-Márquez

    (Faculty of Engineering and Informatics, University of Bradford, Bradford BD7 1DP, UK)

  • Geev Mokryani

    (Faculty of Engineering and Informatics, University of Bradford, Bradford BD7 1DP, UK)

  • Jorge Martínez-Crespo

    (Electrical Engineering Department, Universidad Carlos III de Madrid, 28911 Madrid, Spain)

Abstract

This paper used different risk management indicators applied to the investment optimization performed by consumers in Distributed Generation (DG). The objective function is the total cost incurred by the consumer including the energy and capacity payments, the savings, and the revenues from the installation of DG, alongside the operation and maintenance (O&M) and investment costs. Probability density function (PDF) was used to model the price volatility in the long-term. The mathematical model uses a two-stage stochastic approach: investment and operational stages. The investment decisions are included in the first stage and which do not change with the scenarios of the uncertainty. The operation variables are in the second stage and, therefore, take different values with every realization. Three risk indicators were used to assess the uncertainty risk: Value-at-Risk ( VaR ), Conditional Value-at-Risk ( CVaR ), and Expected Value ( EV ). The results showed the importance of migration from deterministic models to stochastic ones and, most importantly, the understanding of the ramifications of every risk indicator.

Suggested Citation

  • Jorge Luis Angarita-Márquez & Geev Mokryani & Jorge Martínez-Crespo, 2021. "Two-Stage Stochastic Model to Invest in Distributed Generation Considering the Long-Term Uncertainties," Energies, MDPI, vol. 14(18), pages 1-12, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5694-:d:632724
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/18/5694/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/18/5694/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gamal Alkawsi & Yahia Baashar & Ammar Ahmed Alkahtani & Chin Wai Lim & Sieh Kiong Tiong & Mohammad Khudari, 2021. "Viability Assessment of Small-Scale On-Grid Wind Energy Generator for Households in Malaysia," Energies, MDPI, vol. 14(12), pages 1-18, June.
    2. Castagneto Gissey, Giorgio & Zakeri, Behnam & Dodds, Paul E. & Subkhankulova, Dina, 2021. "Evaluating consumer investments in distributed energy technologies," Energy Policy, Elsevier, vol. 149(C).
    3. Siddiqui, Afzal S. & Marnay, Chris, 2008. "Distributed generation investment by a microgrid under uncertainty," Energy, Elsevier, vol. 33(12), pages 1729-1737.
    4. Adriana Mar & Pedro Pereira & João Martins, 2021. "Energy Community Flexibility Solutions to Improve Users’ Wellbeing," Energies, MDPI, vol. 14(12), pages 1-22, June.
    Full references (including those not matched with items on IDEAS)

    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. John Foster & Liam Wagner & Phil Wild & Junhua Zhao & Lucas Skoofa & Craig Froome, 2011. "Market and Economic Modelling of the Intelligent Grid: End of Year Report 2009," Energy Economics and Management Group Working Papers 09, School of Economics, University of Queensland, Australia.
    2. Arvesen, Ø. & Medbø, V. & Fleten, S.-E. & Tomasgard, A. & Westgaard, S., 2013. "Linepack storage valuation under price uncertainty," Energy, Elsevier, vol. 52(C), pages 155-164.
    3. Chen, Yen-Haw & Lu, Su-Ying & Chang, Yung-Ruei & Lee, Ta-Tung & Hu, Ming-Che, 2013. "Economic analysis and optimal energy management models for microgrid systems: A case study in Taiwan," Applied Energy, Elsevier, vol. 103(C), pages 145-154.
    4. Lin, Tyrone T. & Huang, Shio-Ling, 2011. "Application of the modified Tobin's q to an uncertain energy-saving project with the real options concept," Energy Policy, Elsevier, vol. 39(1), pages 408-420, January.
    5. Naraharisetti, Pavan Kumar & Karimi, I.A. & Anand, Abhay & Lee, Dong-Yup, 2011. "A linear diversity constraint – Application to scheduling in microgrids," Energy, Elsevier, vol. 36(7), pages 4235-4243.
    6. Ravindra, Kumudhini & Iyer, Parameshwar P., 2014. "Decentralized demand–supply matching using community microgrids and consumer demand response: A scenario analysis," Energy, Elsevier, vol. 76(C), pages 32-41.
    7. Franz Harke & Philipp Otto, 2023. "Solar Self-Sufficient Households as a Driving Factor for Sustainability Transformation," Sustainability, MDPI, vol. 15(3), pages 1-20, February.
    8. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
    9. Bracco, Stefano & Delfino, Federico & Pampararo, Fabio & Robba, Michela & Rossi, Mansueto, 2014. "A mathematical model for the optimal operation of the University of Genoa Smart Polygeneration Microgrid: Evaluation of technical, economic and environmental performance indicators," Energy, Elsevier, vol. 64(C), pages 912-922.
    10. Muhammad Aqil Afham Rahmat & Ag Sufiyan Abd Hamid & Yuanshen Lu & Muhammad Amir Aziat Ishak & Shaikh Zishan Suheel & Ahmad Fazlizan & Adnan Ibrahim, 2022. "An Analysis of Renewable Energy Technology Integration Investments in Malaysia Using HOMER Pro," Sustainability, MDPI, vol. 14(20), pages 1-24, October.
    11. Rui Ai & Yuhang Zheng & Serhat Yüksel & Hasan Dinçer, 2023. "Investigating the components of fintech ecosystem for distributed energy investments with an integrated quantum spherical decision support system," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-28, December.
    12. Paul Cristian Andrei & Horia Andrei, 2022. "Power Systems’ Connectivity and Resiliency: Modeling, Simulation and Analysis," Energies, MDPI, vol. 15(8), pages 1-3, April.
    13. Carpaneto, Enrico & Chicco, Gianfranco & Mancarella, Pierluigi & Russo, Angela, 2011. "Cogeneration planning under uncertainty: Part I: Multiple time frame approach," Applied Energy, Elsevier, vol. 88(4), pages 1059-1067, April.
    14. Safaei, Amir & Freire, Fausto & Antunes, Carlos Henggeler, 2013. "A model for optimal energy planning of a commercial building integrating solar and cogeneration systems," Energy, Elsevier, vol. 61(C), pages 211-223.
    15. Rezvan, A. Taghipour & Gharneh, N. Shams & Gharehpetian, G.B., 2012. "Robust optimization of distributed generation investment in buildings," Energy, Elsevier, vol. 48(1), pages 455-463.
    16. Elias Carayannis & Pantelis Kostis & Hasan Dinçer & Serhat Yüksel, 2022. "Balanced-Scorecard-Based Evaluation of Knowledge-Oriented Competencies of Distributed Energy Investments," Energies, MDPI, vol. 15(21), pages 1-23, November.
    17. Pless, Jacquelyn & Arent, Douglas J. & Logan, Jeffrey & Cochran, Jaquelin & Zinaman, Owen, 2016. "Quantifying the value of investing in distributed natural gas and renewable electricity systems as complements: Applications of discounted cash flow and real options analysis with stochastic inputs," Energy Policy, Elsevier, vol. 97(C), pages 378-390.
    18. Zakeri, Behnam & Gissey, Giorgio Castagneto & Dodds, Paul E. & Subkhankulova, Dina, 2021. "Centralized vs. distributed energy storage – Benefits for residential users," Energy, Elsevier, vol. 236(C).
    19. Karatepe, Engin & Ugranlı, Faruk & Hiyama, Takashi, 2015. "Comparison of single- and multiple-distributed generation concepts in terms of power loss, voltage profile, and line flows under uncertain scenarios," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 317-327.
    20. Hu, Ming-Che & Lu, Su-Ying & Chen, Yen-Haw, 2016. "Stochastic programming and market equilibrium analysis of microgrids energy management systems," Energy, Elsevier, vol. 113(C), pages 662-670.

    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:gam:jeners:v:14:y:2021:i:18:p:5694-:d:632724. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.