IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i12p4651-d1168888.html
   My bibliography  Save this article

Economic/Environmental Optimal Power Flow Using a Multiobjective Convex Formulation

Author

Listed:
  • Lucas do Carmo Yamaguti

    (Department of Electrical Engineering, São Paulo State University, Ilha Solteira 15385, Brazil)

  • Juan Manuel Home-Ortiz

    (Department of Electrical Engineering, São Paulo State University, Ilha Solteira 15385, Brazil)

  • Mahdi Pourakbari-Kasmaei

    (Department of Electrical Engineering and Automation, Aalto University, 01250 Espoo, Finland)

  • José Roberto Sanches Mantovani

    (Department of Electrical Engineering, São Paulo State University, Ilha Solteira 15385, Brazil)

Abstract

This paper addresses the problem of economic/environmental optimal power flow with a multiobjective formulation using a second-order conic programming (SOCP) optimization model. This problem formulation considers renewable energy sources (RES), fossil-fuel-based power generation units, and voltage control. The proposed SOCP model is a stochastic scenario-based approach to deal with RES and load behavior uncertainties. An ε-constrained algorithm is used to handle the following three objective functions: (1) the costs of power generation, (2) active power losses in the branches, and (3) the emission of pollutant gases produced by fossil-fuel-based power generation units. For comparative purposes, the SOCP model is also presented using a linearized formulation, and numerical results are presented using a 118-bus system. The results confirm that changing the energy matrices directly affects the cost of objective functions. Additionally, using a linearized SOCP model significantly reduces reactive power violation in the generation units when compared to the nonlinearized SOCP model, but also increases the computational time consumed.

Suggested Citation

  • Lucas do Carmo Yamaguti & Juan Manuel Home-Ortiz & Mahdi Pourakbari-Kasmaei & José Roberto Sanches Mantovani, 2023. "Economic/Environmental Optimal Power Flow Using a Multiobjective Convex Formulation," Energies, MDPI, vol. 16(12), pages 1-21, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:12:p:4651-:d:1168888
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/12/4651/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/12/4651/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dahlke, Steven, 2019. "Short run effects of carbon policy on U.S. electricity markets," SocArXiv b79yu, Center for Open Science.
    2. Shargh, S. & Khorshid ghazani, B. & Mohammadi-ivatloo, B. & Seyedi, H. & Abapour, M., 2016. "Probabilistic multi-objective optimal power flow considering correlated wind power and load uncertainties," Renewable Energy, Elsevier, vol. 94(C), pages 10-21.
    3. Steve Dahlke, 2019. "Short Run Effects of Carbon Policy on U.S. Electricity Markets," Energies, MDPI, vol. 12(11), pages 1-21, June.
    4. Ragab El-Sehiemy & Abdullah Shaheen & Ahmed Ginidi & Mostafa Elhosseini, 2022. "A Honey Badger Optimization for Minimizing the Pollutant Environmental Emissions-Based Economic Dispatch Model Integrating Combined Heat and Power Units," Energies, MDPI, vol. 15(20), pages 1-22, October.
    5. Shaheen, Abdullah M. & El-Sehiemy, Ragab A. & Hasanien, Hany M. & Ginidi, Ahmed R., 2022. "An improved heap optimization algorithm for efficient energy management based optimal power flow model," Energy, Elsevier, vol. 250(C).
    6. Baringo, L. & Conejo, A.J., 2013. "Correlated wind-power production and electric load scenarios for investment decisions," Applied Energy, Elsevier, vol. 101(C), pages 475-482.
    7. Abdullah Shaheen & Ahmed Ginidi & Ragab El-Sehiemy & Abdallah Elsayed & Ehab Elattar & Hassen T. Dorrah, 2022. "Developed Gorilla Troops Technique for Optimal Power Flow Problem in Electrical Power Systems," Mathematics, MDPI, vol. 10(10), pages 1-29, May.
    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. Marko Milojević & Mariusz Urbański & Ivica Terzić & Valeriy Prasolov, 2020. "Impact of Non-Financial Factors on the Effectiveness of Audits in Energy Companies," Energies, MDPI, vol. 13(23), pages 1-17, November.
    2. Nuño Martinez, Edgar & Cutululis, Nicolaos & Sørensen, Poul, 2018. "High dimensional dependence in power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 197-213.
    3. Després, Jacques & Hadjsaid, Nouredine & Criqui, Patrick & Noirot, Isabelle, 2015. "Modelling the impacts of variable renewable sources on the power sector: Reconsidering the typology of energy modelling tools," Energy, Elsevier, vol. 80(C), pages 486-495.
    4. Baringo, Luis & Boffino, Luigi & Oggioni, Giorgia, 2020. "Robust expansion planning of a distribution system with electric vehicles, storage and renewable units," Applied Energy, Elsevier, vol. 265(C).
    5. Guelpa, Elisa & Bischi, Aldo & Verda, Vittorio & Chertkov, Michael & Lund, Henrik, 2019. "Towards future infrastructures for sustainable multi-energy systems: A review," Energy, Elsevier, vol. 184(C), pages 2-21.
    6. Cheng, Yaohua & Zhang, Ning & Kirschen, Daniel S. & Huang, Wujing & Kang, Chongqing, 2020. "Planning multiple energy systems for low-carbon districts with high penetration of renewable energy: An empirical study in China," Applied Energy, Elsevier, vol. 261(C).
    7. Rintamäki, Tuomas & Siddiqui, Afzal S. & Salo, Ahti, 2020. "Strategic offering of a flexible producer in day-ahead and intraday power markets," European Journal of Operational Research, Elsevier, vol. 284(3), pages 1136-1153.
    8. Xiaoyang Deng & Jinghan He & Pei Zhang, 2017. "A Novel Probabilistic Optimal Power Flow Method to Handle Large Fluctuations of Stochastic Variables," Energies, MDPI, vol. 10(10), pages 1-21, October.
    9. Khaled Nusair & Lina Alhmoud, 2020. "Application of Equilibrium Optimizer Algorithm for Optimal Power Flow with High Penetration of Renewable Energy," Energies, MDPI, vol. 13(22), pages 1-35, November.
    10. Morshed, Mohammad Javad & Hmida, Jalel Ben & Fekih, Afef, 2018. "A probabilistic multi-objective approach for power flow optimization in hybrid wind-PV-PEV systems," Applied Energy, Elsevier, vol. 211(C), pages 1136-1149.
    11. Ghareeb Moustafa & Ali M. El-Rifaie & Idris H. Smaili & Ahmed Ginidi & Abdullah M. Shaheen & Ahmed F. Youssef & Mohamed A. Tolba, 2023. "An Enhanced Dwarf Mongoose Optimization Algorithm for Solving Engineering Problems," Mathematics, MDPI, vol. 11(15), pages 1-26, July.
    12. Sergio Montoya-Bueno & Jose Ignacio Muñoz-Hernandez & Javier Contreras & Luis Baringo, 2020. "A Benders’ Decomposition Approach for Renewable Generation Investment in Distribution Systems," Energies, MDPI, vol. 13(5), pages 1-19, March.
    13. Shahenda Sarhan & Abdullah Shaheen & Ragab El-Sehiemy & Mona Gafar, 2023. "An Augmented Social Network Search Algorithm for Optimal Reactive Power Dispatch Problem," Mathematics, MDPI, vol. 11(5), pages 1-42, March.
    14. Martín M. Sánchez-Mora & Walter M. Villa-Acevedo & Jesús M. López-Lezama, 2023. "Multi-Area and Multi-Period Optimal Reactive Power Dispatch in Electric Power Systems," Energies, MDPI, vol. 16(17), pages 1-24, September.
    15. Jaime Pilatásig & Diego Carrión & Manuel Jaramillo, 2022. "Resilience Maximization in Electrical Power Systems through Switching of Power Transmission Lines," Energies, MDPI, vol. 15(21), pages 1-15, November.
    16. Mohamed A. M. Shaheen & Zia Ullah & Mohammed H. Qais & Hany M. Hasanien & Kian J. Chua & Marcos Tostado-Véliz & Rania A. Turky & Francisco Jurado & Mohamed R. Elkadeem, 2022. "Solution of Probabilistic Optimal Power Flow Incorporating Renewable Energy Uncertainty Using a Novel Circle Search Algorithm," Energies, MDPI, vol. 15(21), pages 1-19, November.
    17. Samet, Haidar & Khorshidsavar, Morteza, 2018. "Analytic time series load flow," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3886-3899.
    18. Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
    19. Ian M. Trotter & Torjus F. Bolkesj{o} & Eirik O. J{aa}stad & Jon Gustav Kirkerud, 2021. "Increased Electrification of Heating and Weather Risk in the Nordic Power System," Papers 2112.02893, arXiv.org.
    20. Boffino, Luigi & Conejo, Antonio J. & Sioshansi, Ramteen & Oggioni, Giorgia, 2019. "A two-stage stochastic optimization planning framework to decarbonize deeply electric power systems," Energy Economics, Elsevier, vol. 84(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:gam:jeners:v:16:y:2023:i:12:p:4651-:d:1168888. 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.