IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i11p2039-d234900.html
   My bibliography  Save this article

Reliability Based Genetic Algorithm Applied to Allocation of Fiber Optics Links for Power Grid Automation

Author

Listed:
  • Henrique Pires Corrêa

    (Information and Communication Engineering Group—INCOMM, School of Electrical, Mechanical and Computer Engineering, Federal University of Goiás, Goiânia 74605-010, Brazil)

  • Rafael Ribeiro de Carvalho Vaz

    (Information and Communication Engineering Group—INCOMM, School of Electrical, Mechanical and Computer Engineering, Federal University of Goiás, Goiânia 74605-010, Brazil)

  • Flávio Henrique Teles Vieira

    (Information and Communication Engineering Group—INCOMM, School of Electrical, Mechanical and Computer Engineering, Federal University of Goiás, Goiânia 74605-010, Brazil)

  • Sérgio Granato de Araújo

    (Information and Communication Engineering Group—INCOMM, School of Electrical, Mechanical and Computer Engineering, Federal University of Goiás, Goiânia 74605-010, Brazil)

Abstract

In this work, we address the problem of allocating optical links for connecting automatic circuit breakers in a utility power grid. We consider the application of multi-objective optimization for improving costs and power network reliability. To this end, we propose a novel heuristic for attributing reliability values to the optical links, which makes the optimization converge to network topologies in which nodes with higher power outage indexes receive greater communication resources. We combine our heuristic with a genetic algorithm in order to solve the optimization problem. In order to validate the proposed method, simulations are carried out with real data from the local utility. The obtained results validate the allocation heuristic and show that the proposed algorithm outperforms gradient descent optimization in terms of the provided Pareto front.

Suggested Citation

  • Henrique Pires Corrêa & Rafael Ribeiro de Carvalho Vaz & Flávio Henrique Teles Vieira & Sérgio Granato de Araújo, 2019. "Reliability Based Genetic Algorithm Applied to Allocation of Fiber Optics Links for Power Grid Automation," Energies, MDPI, vol. 12(11), pages 1-26, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:11:p:2039-:d:234900
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/11/2039/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/11/2039/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xuebing Chen & Lu Sun & Tengpeng Chen & Yuhao Sun & Rusli & King Jet Tseng & Keck Voon Ling & Weng Khuen Ho & Gehan A. J. Amaratunga, 2019. "Full Coverage of Optimal Phasor Measurement Unit Placement Solutions in Distribution Systems Using Integer Linear Programming," Energies, MDPI, vol. 12(8), pages 1-19, April.
    2. Md Shafiullah & M. A. Abido & Md Ismail Hossain & A. H. Mantawy, 2018. "An Improved OPP Problem Formulation for Distribution Grid Observability," Energies, MDPI, vol. 11(11), pages 1-16, November.
    3. Li, Yang & Wang, Jinlong & Zhao, Dongbo & Li, Guoqing & Chen, Chen, 2018. "A two-stage approach for combined heat and power economic emission dispatch: Combining multi-objective optimization with integrated decision making," Energy, Elsevier, vol. 162(C), pages 237-254.
    4. Zhi Wu & Xiao Du & Wei Gu & Ping Ling & Jinsong Liu & Chen Fang, 2018. "Optimal Micro-PMU Placement Using Mutual Information Theory in Distribution Networks," Energies, MDPI, vol. 11(7), pages 1-19, July.
    5. Li, Yang & Li, Yahui & Li, Guoqing & Zhao, Dongbo & Chen, Chen, 2018. "Two-stage multi-objective OPF for AC/DC grids with VSC-HVDC: Incorporating decisions analysis into optimization process," Energy, Elsevier, vol. 147(C), pages 286-296.
    6. Li, Yang & Feng, Bo & Li, Guoqing & Qi, Junjian & Zhao, Dongbo & Mu, Yunfei, 2018. "Optimal distributed generation planning in active distribution networks considering integration of energy storage," Applied Energy, Elsevier, vol. 210(C), pages 1073-1081.
    7. Dariush Khezrimotlagh & Yao Chen, 2018. "The Optimization Approach," International Series in Operations Research & Management Science, in: Decision Making and Performance Evaluation Using Data Envelopment Analysis, chapter 0, pages 107-134, Springer.
    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. Li, Yang & Yang, Zhen & Li, Guoqing & Mu, Yunfei & Zhao, Dongbo & Chen, Chen & Shen, Bo, 2018. "Optimal scheduling of isolated microgrid with an electric vehicle battery swapping station in multi-stakeholder scenarios: A bi-level programming approach via real-time pricing," Applied Energy, Elsevier, vol. 232(C), pages 54-68.
    2. Simon Pezzutto & Giulio Quaglini & Andrea Zambito & Antonio Novelli & Philippe Riviere & Lukas Kranzl & Eric Wilczynski, 2022. "Potential Evolution of the Cooling Market in the EU27+UK: An Outlook until 2030," Sustainability, MDPI, vol. 14(8), pages 1-25, April.
    3. Jamin Koo & Soung-Ryong Oh & Yeo-Ul Choi & Jae-Hoon Jung & Kyungtae Park, 2019. "Optimization of an Organic Rankine Cycle System for an LNG-Powered Ship," Energies, MDPI, vol. 12(10), pages 1-17, May.
    4. Shitu Zhang & Zhixun Zhu & Yang Li, 2021. "A Critical Review of Data-Driven Transient Stability Assessment of Power Systems: Principles, Prospects and Challenges," Energies, MDPI, vol. 14(21), pages 1-13, November.
    5. Hossam M J Mustafa & Masri Ayob & Mohd Zakree Ahmad Nazri & Graham Kendall, 2019. "An improved adaptive memetic differential evolution optimization algorithms for data clustering problems," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-28, May.
    6. Changyu Zhou & Guohe Huang & Jiapei Chen, 2019. "A Type-2 Fuzzy Chance-Constrained Fractional Integrated Modeling Method for Energy System Management of Uncertainties and Risks," Energies, MDPI, vol. 12(13), pages 1-21, June.
    7. Li, Yang & Wang, Jinlong & Zhao, Dongbo & Li, Guoqing & Chen, Chen, 2018. "A two-stage approach for combined heat and power economic emission dispatch: Combining multi-objective optimization with integrated decision making," Energy, Elsevier, vol. 162(C), pages 237-254.
    8. Donovin D. Lewis & Aron Patrick & Evan S. Jones & Rosemary E. Alden & Abdullah Al Hadi & Malcolm D. McCulloch & Dan M. Ionel, 2023. "Decarbonization Analysis for Thermal Generation and Regionally Integrated Large-Scale Renewables Based on Minutely Optimal Dispatch with a Kentucky Case Study," Energies, MDPI, vol. 16(4), pages 1-23, February.
    9. Li, Yang & Wang, Bin & Yang, Zhen & Li, Jiazheng & Chen, Chen, 2022. "Hierarchical stochastic scheduling of multi-community integrated energy systems in uncertain environments via Stackelberg game," Applied Energy, Elsevier, vol. 308(C).
    10. Kumar Jadoun, Vinay & Rahul Prashanth, G & Suhas Joshi, Siddharth & Narayanan, K. & Malik, Hasmat & García Márquez, Fausto Pedro, 2022. "Optimal fuzzy based economic emission dispatch of combined heat and power units using dynamically controlled Whale Optimization Algorithm," Applied Energy, Elsevier, vol. 315(C).
    11. Zhou, Xu & Ma, Zhongjing & Zou, Suli & Zhang, Jinhui, 2022. "Consensus-based distributed economic dispatch for Multi Micro Energy Grid systems under coupled carbon emissions," Applied Energy, Elsevier, vol. 324(C).
    12. Wang, Jiawei & You, Shi & Zong, Yi & Træholt, Chresten & Dong, Zhao Yang & Zhou, You, 2019. "Flexibility of combined heat and power plants: A review of technologies and operation strategies," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    13. Xu Chen & Shuai Fang & Kangji Li, 2023. "Reinforcement-Learning-Based Multi-Objective Differential Evolution Algorithm for Large-Scale Combined Heat and Power Economic Emission Dispatch," Energies, MDPI, vol. 16(9), pages 1-23, April.
    14. Shaheen, Abdullah M. & El-Sehiemy, Ragab A. & Elattar, Ehab & Ginidi, Ahmed R., 2022. "An Amalgamated Heap and Jellyfish Optimizer for economic dispatch in Combined heat and power systems including N-1 Unit outages," Energy, Elsevier, vol. 246(C).
    15. Muhammad Faisal Shehzad & Mainak Dan & Valerio Mariani & Seshadhri Srinivasan & Davide Liuzza & Carmine Mongiello & Roberto Saraceno & Luigi Glielmo, 2021. "A Heuristic Algorithm for Combined Heat and Power System Operation Management," Energies, MDPI, vol. 14(6), pages 1-22, March.
    16. Li, Yang & Wang, Ruinong & Li, Yuanzheng & Zhang, Meng & Long, Chao, 2023. "Wind power forecasting considering data privacy protection: A federated deep reinforcement learning approach," Applied Energy, Elsevier, vol. 329(C).
    17. Li, Yang & Han, Meng & Shahidehpour, Mohammad & Li, Jiazheng & Long, Chao, 2023. "Data-driven distributionally robust scheduling of community integrated energy systems with uncertain renewable generations considering integrated demand response," Applied Energy, Elsevier, vol. 335(C).
    18. Ali Sulaiman Alsagri & Abdulrahman A. Alrobaian, 2022. "Optimization of Combined Heat and Power Systems by Meta-Heuristic Algorithms: An Overview," Energies, MDPI, vol. 15(16), pages 1-34, August.
    19. Li, Yang & Feng, Bo & Wang, Bin & Sun, Shuchao, 2022. "Joint planning of distributed generations and energy storage in active distribution networks: A Bi-Level programming approach," Energy, Elsevier, vol. 245(C).
    20. Henan Dong & Shun Yuan & Zijiao Han & Zhiyuan Cai & Guangdong Jia & Yangyang Ge, 2018. "A Comprehensive Strategy for Accurate Reactive Power Distribution, Stability Improvement, and Harmonic Suppression of Multi-Inverter-Based Micro-Grid," Energies, MDPI, vol. 11(4), pages 1-16, March.

    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:11:p:2039-:d:234900. 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.