IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i14p6261-d1440393.html
   My bibliography  Save this article

The Optimal Selection of Renewable Energy Systems Based on MILP for Two Zones in Mexico

Author

Listed:
  • Alan Ortiz Contreras

    (Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica (ESIME), Av. Luis Enrique Erro S/N, Unidad Profesional Adolfo López Mateos, Zacatenco, Mexico City 07738, Mexico)

  • Mohamed Badaoui

    (Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica (ESIME), Av. Luis Enrique Erro S/N, Unidad Profesional Adolfo López Mateos, Zacatenco, Mexico City 07738, Mexico)

  • David Sebastián Baltazar

    (Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica (ESIME), Av. Luis Enrique Erro S/N, Unidad Profesional Adolfo López Mateos, Zacatenco, Mexico City 07738, Mexico)

Abstract

This paper presents a series of enhancements to a previously proposed mixed-integer linear programming (MILP) model for investment decisions and operational planning in distributed generation (DG) systems. The main contribution of this study consists of integrating a wind generation system and multiple loads at different buses in a network. The model considers dynamic weather data, energy prices, costs related to photovoltaic and wind systems, storage systems, operational and maintenance costs, and other pertinent factors, such as efficiencies, geographical locations, resource availability, and different load profiles. The simulation results obtained through implementation in Julia’s programming language illustrate that the MILP formulation maximizes the net present value, and four configurations for hybrid power generation systems in Mexico are analyzed. The objective is to enable profitability assessment for investments in large-capacity DG systems in two strategic zones of Mexico. The results show that the configurations in the NE zone, especially in Tamaulipas, are the most cost-effective. Case 1 stands out for its highest net present value and shortest payback time, while Case 2 offers the highest energy savings. In addition, Cases 3 and 4, which incorporate storage systems, exhibit the longest payback periods and the lowest savings, indicating less favorable economic performance compared with Cases 1 and 2. Moreover, the sales of two case studies, one without a storage system and the other with a storage system, are shown. The model also incorporates instruments for buying or selling energy in the wholesale electricity market, including variables that depict the injected energy into the electrical grid. This comprehensive approach provides a detailed overview of optimal energy management.

Suggested Citation

  • Alan Ortiz Contreras & Mohamed Badaoui & David Sebastián Baltazar, 2024. "The Optimal Selection of Renewable Energy Systems Based on MILP for Two Zones in Mexico," Sustainability, MDPI, vol. 16(14), pages 1-26, July.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:14:p:6261-:d:1440393
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/14/6261/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/14/6261/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Soini, Martin Christoph & Parra, David & Patel, Martin Kumar, 2020. "Does bulk electricity storage assist wind and solar in replacing dispatchable power production?," Energy Economics, Elsevier, vol. 85(C).
    2. Khalilpour, Rajab & Vassallo, Anthony, 2016. "Planning and operation scheduling of PV-battery systems: A novel methodology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 194-208.
    3. Andrés Rengel & Alexander Aguila Téllez & Leony Ortiz & Milton Ruiz, 2023. "Optimal Insertion of Energy Storage Systems Considering the Economic Dispatch and the Minimization of Energy Not Supplied," Energies, MDPI, vol. 16(6), pages 1-26, March.
    4. Radu, David & Berger, Mathias & Dubois, Antoine & Fonteneau, Raphaël & Pandžić, Hrvoje & Dvorkin, Yury & Louveaux, Quentin & Ernst, Damien, 2022. "Assessing the impact of offshore wind siting strategies on the design of the European power system," Applied Energy, Elsevier, vol. 305(C).
    5. Hou, Peng & Hu, Weihao & Soltani, Mohsen & Chen, Cong & Chen, Zhe, 2017. "Combined optimization for offshore wind turbine micro siting," Applied Energy, Elsevier, vol. 189(C), pages 271-282.
    6. An, Xiangxin & Si, Guojin & Xia, Tangbin & Wang, Dong & Pan, Ershun & Xi, Lifeng, 2023. "An energy-efficient collaborative strategy of maintenance planning and production scheduling for serial-parallel systems under time-of-use tariffs," Applied Energy, Elsevier, vol. 336(C).
    7. Lüth, Alexandra & Zepter, Jan Martin & Crespo del Granado, Pedro & Egging, Ruud, 2018. "Local electricity market designs for peer-to-peer trading: The role of battery flexibility," Applied Energy, Elsevier, vol. 229(C), pages 1233-1243.
    8. Hou, Peng & Enevoldsen, Peter & Hu, Weihao & Chen, Cong & Chen, Zhe, 2017. "Offshore wind farm repowering optimization," Applied Energy, Elsevier, vol. 208(C), pages 834-844.
    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. Ma, Hongliang & Ge, Mingwei & Wu, Guangxing & Du, Bowen & Liu, Yongqian, 2021. "Formulas of the optimized yaw angles for cooperative control of wind farms with aligned turbines to maximize the power production," Applied Energy, Elsevier, vol. 303(C).
    2. Houssem R. E. H. Bouchekara & Yusuf A. Sha’aban & Mohammad S. Shahriar & Makbul A. M. Ramli & Abdullahi A. Mas’ud, 2023. "Wind Farm Layout Optimization/Expansion with Real Wind Turbines Using a Multi-Objective EA Based on an Enhanced Inverted Generational Distance Metric Combined with the Two-Archive Algorithm 2," Sustainability, MDPI, vol. 15(3), pages 1-32, January.
    3. Artigao, Estefania & Martín-Martínez, Sergio & Honrubia-Escribano, Andrés & Gómez-Lázaro, Emilio, 2018. "Wind turbine reliability: A comprehensive review towards effective condition monitoring development," Applied Energy, Elsevier, vol. 228(C), pages 1569-1583.
    4. Andreolli, Francesca & D’Alpaos, Chiara & Moretto, Michele, 2022. "Valuing investments in domestic PV-Battery Systems under uncertainty," Energy Economics, Elsevier, vol. 106(C).
    5. Serrano González, Javier & Burgos Payán, Manuel & Riquelme Santos, Jesús Manuel, 2018. "Optimal design of neighbouring offshore wind farms: A co-evolutionary approach," Applied Energy, Elsevier, vol. 209(C), pages 140-152.
    6. Ziad Ragab & Ehsan Pashajavid & Sumedha Rajakaruna, 2024. "Optimal Sizing and Economic Analysis of Community Battery Systems Considering Sensitivity and Uncertainty Factors," Energies, MDPI, vol. 17(18), pages 1-20, September.
    7. Zhou, Yuekuan & Lund, Peter D., 2023. "Peer-to-peer energy sharing and trading of renewable energy in smart communities ─ trading pricing models, decision-making and agent-based collaboration," Renewable Energy, Elsevier, vol. 207(C), pages 177-193.
    8. Qinqin Xia & Yao Zou & Qianggang Wang, 2024. "Optimal Capacity Planning of Green Electricity-Based Industrial Electricity-Hydrogen Multi-Energy System Considering Variable Unit Cost Sequence," Sustainability, MDPI, vol. 16(9), pages 1-20, April.
    9. Mehrabankhomartash, Mahmoud & Rayati, Mohammad & Sheikhi, Aras & Ranjbar, Ali Mohammad, 2017. "Practical battery size optimization of a PV system by considering individual customer damage function," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 36-50.
    10. You, Zhengjie & Lumpp, Sebastian Dirk & Doepfert, Markus & Tzscheutschler, Peter & Goebel, Christoph, 2024. "Leveraging flexibility of residential heat pumps through local energy markets," Applied Energy, Elsevier, vol. 355(C).
    11. Radünz, William Corrêa & Mattuella, Jussara M. Leite & Petry, Adriane Prisco, 2020. "Wind resource mapping and energy estimation in complex terrain: A framework based on field observations and computational fluid dynamics," Renewable Energy, Elsevier, vol. 152(C), pages 494-515.
    12. Cervantes, Jairo & Choobineh, Fred, 2018. "Optimal sizing of a nonutility-scale solar power system and its battery storage," Applied Energy, Elsevier, vol. 216(C), pages 105-115.
    13. Wang, Long & Wu, Jianghai & Wang, Tongguang & Han, Ran, 2020. "An optimization method based on random fork tree coding for the electrical networks of offshore wind farms," Renewable Energy, Elsevier, vol. 147(P1), pages 1340-1351.
    14. Javier Serrano González & Manuel Burgos Payán & Jesús Manuel Riquelme Santos & Ángel Gaspar González Rodríguez, 2021. "Optimal Micro-Siting of Weathervaning Floating Wind Turbines," Energies, MDPI, vol. 14(4), pages 1-19, February.
    15. Wen, Shuli & Lan, Hai & Hong, Ying-Yi & Yu, David C. & Zhang, Lijun & Cheng, Peng, 2016. "Allocation of ESS by interval optimization method considering impact of ship swinging on hybrid PV/diesel ship power system," Applied Energy, Elsevier, vol. 175(C), pages 158-167.
    16. Zhou, Hou Sheng & Passey, Rob & Bruce, Anna & Sproul, Alistair B., 2021. "A case study on the behaviour of residential battery energy storage systems during network demand peaks," Renewable Energy, Elsevier, vol. 180(C), pages 712-724.
    17. Se Hoon Baik & Young Gyu Jin & Yong Tae Yoon, 2018. "Determining Equipment Capacity of Electric Vehicle Charging Station Operator for Profit Maximization," Energies, MDPI, vol. 11(9), pages 1-15, September.
    18. Beuse, Martin & Dirksmeier, Mathias & Steffen, Bjarne & Schmidt, Tobias S., 2020. "Profitability of commercial and industrial photovoltaics and battery projects in South-East-Asia," Applied Energy, Elsevier, vol. 271(C).
    19. Vakilifard, Negar & A. Bahri, Parisa & Anda, Martin & Ho, Goen, 2018. "A two-level decision making approach for optimal integrated urban water and energy management," Energy, Elsevier, vol. 155(C), pages 408-425.
    20. Zhang, Lijun & Li, Ye & Xu, Wenhao & Gao, Zhiteng & Fang, Long & Li, Rongfu & Ding, Boyin & Zhao, Bin & Leng, Jun & He, Fenglan, 2022. "Systematic analysis of performance and cost of two floating offshore wind turbines with significant interactions," Applied Energy, Elsevier, vol. 321(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:jsusta:v:16:y:2024:i:14:p:6261-:d:1440393. 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.