IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i17p4281-d400751.html
   My bibliography  Save this article

Solution to the Economic Emission Dispatch Problem Using Numerical Polynomial Homotopy Continuation

Author

Listed:
  • Oracio I. Barbosa-Ayala

    (Department of Mechanical Engineering, Universidad de Guanajuato, Salamanca, GTO 36885, Mexico)

  • Jhon A. Montañez-Barrera

    (Department of Mechanical Engineering, Universidad de Guanajuato, Salamanca, GTO 36885, Mexico)

  • Cesar E. Damian-Ascencio

    (Department of Mechanical Engineering, Universidad de Guanajuato, Salamanca, GTO 36885, Mexico)

  • Adriana Saldaña-Robles

    (Department of Agricultural Mechanical Engineering, Universidad de Guanajuato, Irapuato, GTO 36500, Mexico)

  • J. Arturo Alfaro-Ayala

    (Department of Chemical Engineering, Universidad de Guanajuato, Guanajuato, GTO 36050, Mexico)

  • Jose Alfredo Padilla-Medina

    (Department of Electronics Engineering, Technological Institute of Celaya, Celaya, GTO 38010, Mexico)

  • Sergio Cano-Andrade

    (Department of Mechanical Engineering, Universidad de Guanajuato, Salamanca, GTO 36885, Mexico)

Abstract

The economic emission dispatch (EED) is a highly constrained nonlinear multiobjective optimization problem with a convex (or nonconvex) solution space. These characteristics and constraints make the EED a difficult problem to solve. Several approaches for a solution have been proposed, such as deterministic techniques, stochastic techniques, or a combination of both. This work presents the use of an algebraic (deterministic) technique, the numerical polynomial homotopy continuation (NPHC) method, to solve the EED problem. A comparison with the sequential quadratic programming (SQP) algorithm and the nondominated sorting genetic algorithm II (NSGA-II) is also presented. Results show that the NPHC algorithm finds all the roots (solutions) of the problem starting from any initial point and assures an accurate solution with a good convergence time. In addition, the NPHC algorithm provides a more accurate solution than the SQP algorithm and the NSGA-II.

Suggested Citation

  • Oracio I. Barbosa-Ayala & Jhon A. Montañez-Barrera & Cesar E. Damian-Ascencio & Adriana Saldaña-Robles & J. Arturo Alfaro-Ayala & Jose Alfredo Padilla-Medina & Sergio Cano-Andrade, 2020. "Solution to the Economic Emission Dispatch Problem Using Numerical Polynomial Homotopy Continuation," Energies, MDPI, vol. 13(17), pages 1-15, August.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:17:p:4281-:d:400751
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/17/4281/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/17/4281/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Florios, Kostas & Mavrotas, George, 2014. "Generation of the exact Pareto set in multi-objective traveling salesman and set covering problems," MPRA Paper 105074, University Library of Munich, Germany.
    2. Shahbaz Hussain & Mohammed Al-Hitmi & Salman Khaliq & Asif Hussain & Muhammad Asghar Saqib, 2019. "Implementation and Comparison of Particle Swarm Optimization and Genetic Algorithm Techniques in Combined Economic Emission Dispatch of an Independent Power Plant," Energies, MDPI, vol. 12(11), pages 1-15, May.
    3. Thomas Covert & Michael Greenstone & Christopher R. Knittel, 2016. "Will We Ever Stop Using Fossil Fuels?," Journal of Economic Perspectives, American Economic Association, vol. 30(1), pages 117-138, Winter.
    4. Pfenninger, Stefan & Keirstead, James, 2015. "Renewables, nuclear, or fossil fuels? Scenarios for Great Britain’s power system considering costs, emissions and energy security," Applied Energy, Elsevier, vol. 152(C), pages 83-93.
    5. Liangce He & Zhigang Lu & Lili Pan & Hao Zhao & Xueping Li & Jiangfeng Zhang, 2019. "Optimal Economic and Emission Dispatch of a Microgrid with a Combined Heat and Power System," Energies, MDPI, vol. 12(4), pages 1-19, February.
    6. Panpan Mei & Lianghong Wu & Hongqiang Zhang & Zhenzu Liu, 2019. "A Hybrid Multi-Objective Crisscross Optimization for Dynamic Economic/Emission Dispatch Considering Plug-In Electric Vehicles Penetration," Energies, MDPI, vol. 12(20), pages 1-21, October.
    7. Jose R. Vargas-Jaramillo & Jhon A. Montanez-Barrera & Michael R. von Spakovsky & Lamine Mili & Sergio Cano-Andrade, 2019. "Effects of Producer and Transmission Reliability on the Sustainability Assessment of Power System Networks," Energies, MDPI, vol. 12(3), pages 1-21, February.
    8. Raza, Muhammad Qamar & Khosravi, Abbas, 2015. "A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1352-1372.
    9. Lee, Chia-Yen & Zhou, Peng, 2015. "Directional shadow price estimation of CO2, SO2 and NOx in the United States coal power industry 1990–2010," Energy Economics, Elsevier, vol. 51(C), pages 493-502.
    10. Boyang Qu & Baihao Qiao & Yongsheng Zhu & Jingjing Liang & Ling Wang, 2017. "Dynamic Power Dispatch Considering Electric Vehicles and Wind Power Using Decomposition Based Multi-Objective Evolutionary Algorithm," Energies, MDPI, vol. 10(12), pages 1-28, December.
    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. Liu, Zhi-Feng & Li, Ling-Ling & Liu, Yu-Wei & Liu, Jia-Qi & Li, Heng-Yi & Shen, Qiang, 2021. "Dynamic economic emission dispatch considering renewable energy generation: A novel multi-objective optimization approach," Energy, Elsevier, vol. 235(C).
    2. Kansal, Veenus & Dhillon, J.S., 2022. "Ameliorated artificial hummingbird algorithm for coordinated wind-solar-thermal generation scheduling problem in multiobjective framework," Applied Energy, Elsevier, vol. 326(C).
    3. Lingling Li & Jiarui Pei & Qiang Shen, 2023. "A Review of Research on Dynamic and Static Economic Dispatching of Hybrid Wind–Thermal Power Microgrids," Energies, MDPI, vol. 16(10), pages 1-23, May.

    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. Chen Shi & Yujiao Xian & Zhixin Wang & Ke Wang, 2023. "Marginal abatement cost curve of carbon emissions in China: a functional data analysis," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 28(2), pages 1-25, February.
    2. Lukas Kriechbaum & Philipp Gradl & Romeo Reichenhauser & Thomas Kienberger, 2020. "Modelling Grid Constraints in a Multi-Energy Municipal Energy System Using Cumulative Exergy Consumption Minimisation," Energies, MDPI, vol. 13(15), pages 1-23, July.
    3. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    4. Zunian Luo, 2022. "Cap or No Cap? What Can Governments Do to Promote EV Sales?," Papers 2212.08137, arXiv.org.
    5. Eid Gul & Giorgio Baldinelli & Pietro Bartocci, 2022. "Energy Transition: Renewable Energy-Based Combined Heat and Power Optimization Model for Distributed Communities," Energies, MDPI, vol. 15(18), pages 1-18, September.
    6. Skare, Marinko & Gavurova, Beata & Sinkovic, Dean, 2023. "Regional aspects of financial development and renewable energy: A cross-sectional study in 214 countries," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 1142-1157.
    7. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
    8. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    9. Maruf, Md. Nasimul Islam, 2021. "Open model-based analysis of a 100% renewable and sector-coupled energy system–The case of Germany in 2050," Applied Energy, Elsevier, vol. 288(C).
    10. Hobley, Alexander, 2019. "Will gas be gone in the United Kingdom (UK) by 2050? An impact assessment of urban heat decarbonisation and low emission vehicle uptake on future UK energy system scenarios," Renewable Energy, Elsevier, vol. 142(C), pages 695-705.
    11. Lucas W. Davis, 2017. "The Environmental Cost of Global Fuel Subsidies," The Energy Journal, International Association for Energy Economics, vol. 0(KAPSARC S).
    12. V. Y. Kondaiah & B. Saravanan, 2022. "Short-Term Load Forecasting with a Novel Wavelet-Based Ensemble Method," Energies, MDPI, vol. 15(14), pages 1-17, July.
    13. Gregory Casey, 2024. "Energy Efficiency and Directed Technical Change: Implications for Climate Change Mitigation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 91(1), pages 192-228.
    14. Abdirizak Omar & Mouadh Addassi & Volker Vahrenkamp & Hussein Hoteit, 2021. "Co-Optimization of CO 2 Storage and Enhanced Gas Recovery Using Carbonated Water and Supercritical CO 2," Energies, MDPI, vol. 14(22), pages 1-21, November.
    15. Xian, Yujiao & Hu, Zhihui & Wang, Ke, 2023. "The least-cost abatement measure of carbon emissions for China's glass manufacturing industry based on the marginal abatement costs," Energy, Elsevier, vol. 284(C).
    16. Namrye Son, 2021. "Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting," Sustainability, MDPI, vol. 13(22), pages 1-25, November.
    17. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    18. Child, Michael & Breyer, Christian, 2017. "Transition and transformation: A review of the concept of change in the progress towards future sustainable energy systems," Energy Policy, Elsevier, vol. 107(C), pages 11-26.
    19. Yves Achdou & Charles Bertucci & Jean-Michel Lasry & Pierre-Louis Lions & Antoine Rostand & José A. Scheinkman, 2022. "A class of short-term models for the oil industry that accounts for speculative oil storage," Finance and Stochastics, Springer, vol. 26(3), pages 631-669, July.
    20. He, Weijun & Wang, Bo & Danish, & Wang, Zhaohua, 2018. "Will regional economic integration influence carbon dioxide marginal abatement costs? Evidence from Chinese panel data," Energy Economics, Elsevier, vol. 74(C), pages 263-274.

    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:13:y:2020:i:17:p:4281-:d:400751. 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.