IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i12p2330-d240827.html
   My bibliography  Save this article

Wind Power Cogeneration to Reduce Peak Electricity Demand in Mexican States Along the Gulf of Mexico

Author

Listed:
  • Quetzalcoatl Hernandez-Escobedo

    (Faculty of Engineering, Campus Coatzacoalcos, Universidad Veracruzana Mexico, Coatzacoalcos, Veracruz 96535, Mexico)

  • Javier Garrido

    (Faculty of Engineering, Campus Coatzacoalcos, Universidad Veracruzana Mexico, Coatzacoalcos, Veracruz 96535, Mexico)

  • Fernando Rueda-Martinez

    (Faculty of Engineering, Campus Coatzacoalcos, Universidad Veracruzana Mexico, Coatzacoalcos, Veracruz 96535, Mexico)

  • Gerardo Alcalá

    (Centro de Investigación en Recursos Energéticos y Sustentables, Universidad Veracruzana Mexico; Coatzacoalcos, Veracruz 96535, Mexico)

  • Alberto-Jesus Perea-Moreno

    (Departamento de Física Aplicada, Universidad de Córdoba, ceiA3, Campus de Rabanales, 14071 Córdoba, Spain)

Abstract

The Energetic Transition Law in Mexico has established that in the next years, the country has to produce at least 35% of its energy from clean sources in 2024. Based on this, a proposal in this study is the cogeneration between the principal thermal power plants along the Mexican states of the Gulf of Mexico with modeled wind farms near to these thermal plants with the objective to reduce peak electricity demand. These microscale models were done with hourly MERRA-2 data that included wind speed, wind direction, temperature, and atmospheric pressure with records from 1980–2018 and taking into account roughness, orography, and climatology of the site. Wind speed daily profile for each model was compared to electricity demand trajectory, and it was seen that wind speed has a peak at the same time. The amount of power delivered to the electric grid with this cogeneration in Rio Bravo and Altamira (Northeast region) is 2657.02 MW and for Tuxpan and Dos Bocas from the Eastern region is 3196.18 MW. This implies a reduction at the peak demand. In the Northeast region, the power demand at the peak is 8000 MW, and for Eastern region 7200 MW. If wind farms and thermal power plants work at the same time in Northeast and Eastern regions, the amount of power delivered by other sources of energy at this moment will be 5342.98 MW and 4003.82 MW, respectively.

Suggested Citation

  • Quetzalcoatl Hernandez-Escobedo & Javier Garrido & Fernando Rueda-Martinez & Gerardo Alcalá & Alberto-Jesus Perea-Moreno, 2019. "Wind Power Cogeneration to Reduce Peak Electricity Demand in Mexican States Along the Gulf of Mexico," Energies, MDPI, vol. 12(12), pages 1-22, June.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:12:p:2330-:d:240827
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/12/2330/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/12/12/2330/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Javier Sanz Rodrigo & Roberto Aurelio Chávez Arroyo & Patrick Moriarty & Matthew Churchfield & Branko Kosović & Pierre‐Elouan Réthoré & Kurt Schaldemose Hansen & Andrea Hahmann & Jeffrey D. Mirocha & , 2017. "Mesoscale to microscale wind farm flow modeling and evaluation," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 6(2), March.
    2. Wang, Yi-Hui & Walter, Ryan K. & White, Crow & Farr, Hayley & Ruttenberg, Benjamin I., 2019. "Assessment of surface wind datasets for estimating offshore wind energy along the Central California Coast," Renewable Energy, Elsevier, vol. 133(C), pages 343-353.
    3. Karimi, Ali & Aminifar, Farrokh & Fereidunian, Alireza & Lesani, Hamid, 2019. "Energy storage allocation in wind integrated distribution networks: An MILP-Based approach," Renewable Energy, Elsevier, vol. 134(C), pages 1042-1055.
    4. Ikegami, Takashi & Urabe, Chiyori T. & Saitou, Tetsuo & Ogimoto, Kazuhiko, 2018. "Numerical definitions of wind power output fluctuations for power system operations," Renewable Energy, Elsevier, vol. 115(C), pages 6-15.
    5. Faruqui, Ahmad & Hledik, Ryan & Newell, Sam & Pfeifenberger, Hannes, 2007. "The Power of 5 Percent," The Electricity Journal, Elsevier, vol. 20(8), pages 68-77, October.
    6. Wang, Meina & Ullrich, Paul & Millstein, Dev, 2018. "The future of wind energy in California: Future projections with the Variable-Resolution CESM," Renewable Energy, Elsevier, vol. 127(C), pages 242-257.
    7. Weiwei Cui & Lin Li & Zhiqiang Lu, 2019. "Energy‐efficient scheduling for sustainable manufacturing systems with renewable energy resources," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(2), pages 154-173, March.
    8. Waite, Michael & Modi, Vijay, 2019. "Impact of deep wind power penetration on variability at load centers," Applied Energy, Elsevier, vol. 235(C), pages 1048-1060.
    9. Lebotsa, Moshoko Emily & Sigauke, Caston & Bere, Alphonce & Fildes, Robert & Boylan, John E., 2018. "Short term electricity demand forecasting using partially linear additive quantile regression with an application to the unit commitment problem," Applied Energy, Elsevier, vol. 222(C), pages 104-118.
    10. Nosratabadi, Seyyed Mostafa & Hooshmand, Rahmat-Allah & Gholipour, Eskandar, 2017. "A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 341-363.
    11. Adefarati, T. & Bansal, R.C., 2019. "Reliability, economic and environmental analysis of a microgrid system in the presence of renewable energy resources," Applied Energy, Elsevier, vol. 236(C), pages 1089-1114.
    12. Hongyu Long & Kunyao Xu & Ruilin Xu & Jianjun He, 2012. "More Wind Power Integration with Adjusted Energy Carriers for Space Heating in Northern China," Energies, MDPI, vol. 5(9), pages 1-16, August.
    13. Zaroni, Hebert & Maciel, Letícia B. & Carvalho, Diego B. & Pamplona, Edson de O., 2019. "Monte Carlo Simulation approach for economic risk analysis of an emergency energy generation system," Energy, Elsevier, vol. 172(C), pages 498-508.
    14. Wang, Fei & Xu, Hanchen & Xu, Ti & Li, Kangping & Shafie-khah, Miadreza & Catalão, João. P.S., 2017. "The values of market-based demand response on improving power system reliability under extreme circumstances," Applied Energy, Elsevier, vol. 193(C), pages 220-231.
    15. Andersen, Frits Møller & Baldini, Mattia & Hansen, Lars Gårn & Jensen, Carsten Lynge, 2017. "Households’ hourly electricity consumption and peak demand in Denmark," Applied Energy, Elsevier, vol. 208(C), pages 607-619.
    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. Carlos Méndez & Yusuf Bicer, 2019. "Qatar’s Wind Energy Potential with Associated Financial and Environmental Benefits for the Natural Gas Industry," Energies, MDPI, vol. 12(17), pages 1-19, August.
    2. Alberto-Jesus Perea-Moreno & Francisco Manzano-Agugliaro, 2020. "Energy Saving at Cities," Energies, MDPI, vol. 13(15), pages 1-3, July.

    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. Luo, Zhe & Hong, SeungHo & Ding, YueMin, 2019. "A data mining-driven incentive-based demand response scheme for a virtual power plant," Applied Energy, Elsevier, vol. 239(C), pages 549-559.
    2. Wang, Richard & Lam, Chor-Man & Hsu, Shu-Chien & Chen, Jieh-Haur, 2019. "Life cycle assessment and energy payback time of a standalone hybrid renewable energy commercial microgrid: A case study of Town Island in Hong Kong," Applied Energy, Elsevier, vol. 250(C), pages 760-775.
    3. Mbungu, Nsilulu T. & Bansal, Ramesh C. & Naidoo, Raj M. & Bettayeb, Maamar & Siti, Mukwanga W. & Bipath, Minnesh, 2020. "A dynamic energy management system using smart metering," Applied Energy, Elsevier, vol. 280(C).
    4. Sandelic, Monika & Peyghami, Saeed & Sangwongwanich, Ariya & Blaabjerg, Frede, 2022. "Reliability aspects in microgrid design and planning: Status and power electronics-induced challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    5. Li, Jinghua & Lu, Bo & Wang, Zhibang & Zhu, Mengshu, 2021. "Bi-level optimal planning model for energy storage systems in a virtual power plant," Renewable Energy, Elsevier, vol. 165(P2), pages 77-95.
    6. Andrew J. Satchwell & Peter A. Cappers & Jeff Deason & Sydney P. Forrester & Natalie Mims Frick & Brian F. Gerke & Mary Ann Piette, 2020. "A Conceptual Framework to Describe Energy Efficiency and Demand Response Interactions," Energies, MDPI, vol. 13(17), pages 1-14, August.
    7. Yohannes Biru Aemro & Pedro Moura & Aníbal T. de Almeida, 2020. "Design and Modeling of a Standalone DC-Microgrid for Off-Grid Schools in Rural Areas of Developing Countries," Energies, MDPI, vol. 13(23), pages 1-24, December.
    8. Saffari, Mohammad & de Gracia, Alvaro & Fernández, Cèsar & Belusko, Martin & Boer, Dieter & Cabeza, Luisa F., 2018. "Optimized demand side management (DSM) of peak electricity demand by coupling low temperature thermal energy storage (TES) and solar PV," Applied Energy, Elsevier, vol. 211(C), pages 604-616.
    9. Ramos, Dorel Soares & Del Carpio Huayllas, Tesoro Elena & Morozowski Filho, Marciano & Tolmasquim, Mauricio Tiomno, 2020. "New commercial arrangements and business models in electricity distribution systems: The case of Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 117(C).
    10. Markus Gross & Vanesa Magar & Alfredo Peña, 2020. "The Effect of Averaging, Sampling, and Time Series Length on Wind Power Density Estimations," Sustainability, MDPI, vol. 12(8), pages 1-13, April.
    11. Emrani-Rahaghi, Pouria & Hashemi-Dezaki, Hamed & Ketabi, Abbas, 2023. "Efficient voltage control of low voltage distribution networks using integrated optimized energy management of networked residential multi-energy microgrids," Applied Energy, Elsevier, vol. 349(C).
    12. Abolhosseini, Shahrouz & Heshmati, Almas & Altmann, Jörn, 2014. "A Review of Renewable Energy Supply and Energy Efficiency Technologies," IZA Discussion Papers 8145, Institute of Labor Economics (IZA).
    13. 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.
    14. Radünz, William Corrêa & de Almeida, Everton & Gutiérrez, Alejandro & Acevedo, Otávio Costa & Sakagami, Yoshiaki & Petry, Adriane Prisco & Passos, Júlio César, 2022. "Nocturnal jets over wind farms in complex terrain," Applied Energy, Elsevier, vol. 314(C).
    15. Mohseni, Soheil & Brent, Alan C. & Burmester, Daniel, 2020. "A comparison of metaheuristics for the optimal capacity planning of an isolated, battery-less, hydrogen-based micro-grid," Applied Energy, Elsevier, vol. 259(C).
    16. Dario Garozzo & Giuseppe Marco Tina, 2020. "Evaluation of the Effective Active Power Reserve for Fast Frequency Response of PV with BESS Inverters Considering Reactive Power Control," Energies, MDPI, vol. 13(13), pages 1-16, July.
    17. 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.
    18. Tsao, Yu-Chung & Thanh, Vo-Van, 2021. "Toward sustainable microgrids with blockchain technology-based peer-to-peer energy trading mechanism: A fuzzy meta-heuristic approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
    19. Xinyu Han & Rongrong Li, 2019. "Comparison of Forecasting Energy Consumption in East Africa Using the MGM, NMGM, MGM-ARIMA, and NMGM-ARIMA Model," Energies, MDPI, vol. 12(17), pages 1-24, August.
    20. Nor Liza Tumeran & Siti Hajar Yusoff & Teddy Surya Gunawan & Mohd Shahrin Abu Hanifah & Suriza Ahmad Zabidi & Bernardi Pranggono & Muhammad Sharir Fathullah Mohd Yunus & Siti Nadiah Mohd Sapihie & Asm, 2023. "Model Predictive Control Based Energy Management System Literature Assessment for RES Integration," Energies, MDPI, vol. 16(8), pages 1-27, 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:gam:jeners:v:12:y:2019:i:12:p:2330-:d:240827. 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.