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Optimization of the Contracted Electric Power by Means of Genetic Algorithms

Author

Listed:
  • Alfredo Alcayde

    (Department of Engineering, University of Almería, 04120 Almería, Spain)

  • Raul Baños

    (Department of Engineering, University of Almería, 04120 Almería, Spain)

  • Francisco M. Arrabal-Campos

    (Department of Engineering, University of Almería, 04120 Almería, Spain)

  • Francisco G. Montoya

    (Department of Engineering, University of Almería, 04120 Almería, Spain)

Abstract

An adequate selection of an energy provider and tariff requires us to analyze the different alternatives to choose one that satisfies your needs. In particular, choosing the right electricity tariff is essential for reducing company costs and improving competitiveness. This paper analyzes the energy consumption of large consumers that make intensive use of electricity and proposes the use of genetic algorithms for optimizing the tariff selection. The aim is to minimize electricity costs including two factors: the cost of power contracted and the heavy penalties for excess of power demand over the power contracted in certain time periods. In order to validate the proposed methodology, a case study based on the real data of energy consumption of a large Spanish university is presented. The results obtained show that the genetic algorithm and other bio-inspired approaches are able to reduce the costs associated to the electricity bill.

Suggested Citation

  • Alfredo Alcayde & Raul Baños & Francisco M. Arrabal-Campos & Francisco G. Montoya, 2019. "Optimization of the Contracted Electric Power by Means of Genetic Algorithms," Energies, MDPI, vol. 12(7), pages 1-13, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1270-:d:219373
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    References listed on IDEAS

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    1. repec:aen:journl:dn-se-a03 is not listed on IDEAS
    2. Francisco G. Montoya & Alfredo Alcayde & Francisco M. Arrabal-Campos & Raul Baños, 2019. "Quadrature Current Compensation in Non-Sinusoidal Circuits Using Geometric Algebra and Evolutionary Algorithms," Energies, MDPI, vol. 12(4), pages 1-17, February.
    3. Ma, Jinlong, 2011. "On-grid electricity tariffs in China: Development, reform and prospects," Energy Policy, Elsevier, vol. 39(5), pages 2633-2645, May.
    4. repec:aen:journl:2005se-a02 is not listed on IDEAS
    5. Antonio J. Conejo & Miguel Carrión & Juan M. Morales, 2010. "Decision Making Under Uncertainty in Electricity Markets," International Series in Operations Research and Management Science, Springer, number 978-1-4419-7421-1, June.
    6. Tooraj Jamasb & Michael Pollitt, 2005. "Electricity Market Reform in the European Union: Review of Progress toward Liberalization &Integration," The Energy Journal, , vol. 26(1_suppl), pages 11-41, June.
    7. Seyedeh Narjes Fallah & Ravinesh Chand Deo & Mohammad Shojafar & Mauro Conti & Shahaboddin Shamshirband, 2018. "Computational Intelligence Approaches for Energy Load Forecasting in Smart Energy Management Grids: State of the Art, Future Challenges, and Research Directions," Energies, MDPI, vol. 11(3), pages 1-31, March.
    8. Suganthi, S.T. & Devaraj, D. & Ramar, K. & Hosimin Thilagar, S., 2018. "An Improved Differential Evolution algorithm for congestion management in the presence of wind turbine generators," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 635-642.
    9. Lin, Qiuzhen & Li, Jianqiang & Du, Zhihua & Chen, Jianyong & Ming, Zhong, 2015. "A novel multi-objective particle swarm optimization with multiple search strategies," European Journal of Operational Research, Elsevier, vol. 247(3), pages 732-744.
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    Cited by:

    1. Wen-Tung Lee & Shih-Cheng Horng & Chi-Fang Lin, 2019. "Application of Ordinal Optimization to Reactive Volt-Ampere Sources Planning Problems," Energies, MDPI, vol. 12(14), pages 1-16, July.
    2. Francisco G. Montoya & Raúl Baños & Alfredo Alcayde & Francisco Manzano-Agugliaro, 2019. "Optimization Methods Applied to Power Systems," Energies, MDPI, vol. 12(12), pages 1-8, June.
    3. Raheela Jamal & Baohui Men & Noor Habib Khan & Muhammad Asif Zahoor Raja, 2019. "Hybrid Bio-Inspired Computational Heuristic Paradigm for Integrated Load Dispatch Problems Involving Stochastic Wind," Energies, MDPI, vol. 12(13), pages 1-23, July.

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