IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i5p1863-d763207.html
   My bibliography  Save this article

Optimal Placement of Renewable Energy Generators Using Grid-Oriented Genetic Algorithm for Loss Reduction and Flexibility Improvement

Author

Listed:
  • Ekata Kaushik

    (School of Automation, Banasthali Vidyapith, Tonk 304022, Rajasthan, India)

  • Vivek Prakash

    (School of Automation, Banasthali Vidyapith, Tonk 304022, Rajasthan, India
    Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia)

  • Om Prakash Mahela

    (Power System Planning Division, Rajasthan Rajya Vidyut Prasaran Nigam Ltd., Jaipur 302005, Rajasthan, India)

  • Baseem Khan

    (Department of Electrical and Computer Engineering, Hawassa University, Awassa P.O. Box 5, Ethiopia)

  • Almoataz Y. Abdelaziz

    (Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, Egypt)

  • Junhee Hong

    (College of IT Convergence, Gachon University, Seongnam 13120, Korea)

  • Zong Woo Geem

    (College of IT Convergence, Gachon University, Seongnam 13120, Korea)

Abstract

Optimal planning of renewable energy generator (REG) units helps to meet future power demand with improved flexibility. Hence, this paper proposes a grid-oriented genetic algorithm (GOGA) based on a hybrid combination of a genetic algorithm (GA) and a solution using analytical power flow equations for optimal sizing and placement of REG units in a power system network. The objective of the GOGA is system loss minimization and flexibility improvement. The objective function expresses the system losses as a function of the power generated by different generators, using the Kron equation. A flexibility index (FI) is proposed to evaluate the improvement in the flexibility, based on the voltage deviations and system losses. A power flow run is performed after placement of REGs at various buses of the test system, and system losses are computed, which are considered as chromosome fitness values. The GOGA searches for the lowest value of the fitness function by changing the location of REG units. Crossover, mutation, and replacement operators are used by the GOGA to generate new chromosomes until the optimal solution is obtained in terms of size and location of REGs. A study is performed on a part of the practical transmission network of Rajasthan Rajya Vidyut Prasaran Nigam Ltd. (RVPN), India for the base year 2021 and the projected year 2031. Load forecasting for the 10-year time horizon is computed using a linear fit mathematical model. A cost–benefit analysis is performed, and it is established that the proposed GOGA provides a financially viable solution with improved flexibility. It is established that GOGA ensures high convergence speed and good solution accuracy. Further, the performance of the GOGA is superior compared to a conventional GA.

Suggested Citation

  • Ekata Kaushik & Vivek Prakash & Om Prakash Mahela & Baseem Khan & Almoataz Y. Abdelaziz & Junhee Hong & Zong Woo Geem, 2022. "Optimal Placement of Renewable Energy Generators Using Grid-Oriented Genetic Algorithm for Loss Reduction and Flexibility Improvement," Energies, MDPI, vol. 15(5), pages 1-20, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:5:p:1863-:d:763207
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/5/1863/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/5/1863/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Guo, Zheyu & Zheng, Yanan & Li, Gengyin, 2020. "Power system flexibility quantitative evaluation based on improved universal generating function method: A case study of Zhangjiakou," Energy, Elsevier, vol. 205(C).
    2. Zhao, Haitao & Jiang, Peng & Chen, Zhe & Ezeh, Collins I. & Hong, Yuanda & Guo, Yishan & Zheng, Chenghang & Džapo, Hrvoje & Gao, Xiang & Wu, Tao, 2019. "Improvement of fuel sources and energy products flexibility in coal power plants via energy-cyber-physical-systems approach," Applied Energy, Elsevier, vol. 254(C).
    3. Kopiske, Jakob & Spieker, Sebastian & Tsatsaronis, George, 2017. "Value of power plant flexibility in power systems with high shares of variable renewables: A scenario outlook for Germany 2035," Energy, Elsevier, vol. 137(C), pages 823-833.
    4. Shrimali, Gireesh, 2021. "Managing power system flexibility in India via coal plants," Energy Policy, Elsevier, vol. 150(C).
    5. Das, Choton K. & Bass, Octavian & Kothapalli, Ganesh & Mahmoud, Thair S. & Habibi, Daryoush, 2018. "Optimal placement of distributed energy storage systems in distribution networks using artificial bee colony algorithm," Applied Energy, Elsevier, vol. 232(C), pages 212-228.
    6. Maeder, Mattia & Weiss, Olga & Boulouchos, Konstantinos, 2021. "Assessing the need for flexibility technologies in decarbonized power systems: A new model applied to Central Europe," Applied Energy, Elsevier, vol. 282(PA).
    7. Chen, J.J. & Qi, B.X. & Rong, Z.K. & Peng, K. & Zhao, Y.L. & Zhang, X.H., 2021. "Multi-energy coordinated microgrid scheduling with integrated demand response for flexibility improvement," Energy, Elsevier, vol. 217(C).
    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. Xiaoming Liu & Liang Wang & Yongji Cao & Ruicong Ma & Yao Wang & Changgang Li & Rui Liu & Shihao Zou, 2023. "Renewable Scenario Generation Based on the Hybrid Genetic Algorithm with Variable Chromosome Length," Energies, MDPI, vol. 16(7), pages 1-16, March.
    2. Bartłomiej Mroczek & Paweł Pijarski, 2022. "Machine Learning in Operating of Low Voltage Future Grid," Energies, MDPI, vol. 15(15), pages 1-30, 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. Deng, Xu & Lv, Tao & Hou, Xiaoran & Xu, Jie & Pi, Duyang & Liu, Feng & Li, Na, 2022. "Regional disparity of flexibility options for integrating variable renewable energy," Renewable Energy, Elsevier, vol. 192(C), pages 641-654.
    2. Mousavi, Navid & Kothapalli, Ganesh & Habibi, Daryoush & Das, Choton K. & Baniasadi, Ali, 2020. "A novel photovoltaic-pumped hydro storage microgrid applicable to rural areas," Applied Energy, Elsevier, vol. 262(C).
    3. Mansour-Saatloo, Amin & Pezhmani, Yasin & Mirzaei, Mohammad Amin & Mohammadi-Ivatloo, Behnam & Zare, Kazem & Marzband, Mousa & Anvari-Moghaddam, Amjad, 2021. "Robust decentralized optimization of Multi-Microgrids integrated with Power-to-X technologies," Applied Energy, Elsevier, vol. 304(C).
    4. Gao, Yang & Ai, Qian & He, Xing & Fan, Songli, 2023. "Coordination for regional integrated energy system through target cascade optimization," Energy, Elsevier, vol. 276(C).
    5. Barbara Uliasz-Misiak & Joanna Lewandowska-Śmierzchalska & Rafał Matuła & Radosław Tarkowski, 2022. "Prospects for the Implementation of Underground Hydrogen Storage in the EU," Energies, MDPI, vol. 15(24), pages 1-17, December.
    6. Dianfa Wu & Zhiping Yang & Ningling Wang & Chengzhou Li & Yongping Yang, 2018. "An Integrated Multi-Criteria Decision Making Model and AHP Weighting Uncertainty Analysis for Sustainability Assessment of Coal-Fired Power Units," Sustainability, MDPI, vol. 10(6), pages 1-27, May.
    7. Singh, Pushpendra & Meena, Nand K. & Yang, Jin & Vega-Fuentes, Eduardo & Bishnoi, Shree Krishna, 2020. "Multi-criteria decision making monarch butterfly optimization for optimal distributed energy resources mix in distribution networks," Applied Energy, Elsevier, vol. 278(C).
    8. Kang, Jidong & Wu, Zhuochun & Ng, Tsan Sheng & Su, Bin, 2023. "A stochastic-robust optimization model for inter-regional power system planning," European Journal of Operational Research, Elsevier, vol. 310(3), pages 1234-1248.
    9. Ji, Haoran & Wang, Chengshan & Li, Peng & Song, Guanyu & Yu, Hao & Wu, Jianzhong, 2019. "Quantified analysis method for operational flexibility of active distribution networks with high penetration of distributed generators," Applied Energy, Elsevier, vol. 239(C), pages 706-714.
    10. Ahmed Alzahrani & Hussain Alharthi & Muhammad Khalid, 2019. "Minimization of Power Losses through Optimal Battery Placement in a Distributed Network with High Penetration of Photovoltaics," Energies, MDPI, vol. 13(1), pages 1-16, December.
    11. Song, Hongqing & Lao, Junming & Zhang, Liyuan & Xie, Chiyu & Wang, Yuhe, 2023. "Underground hydrogen storage in reservoirs: pore-scale mechanisms and optimization of storage capacity and efficiency," Applied Energy, Elsevier, vol. 337(C).
    12. Wang, Liying & Lin, Jialin & Dong, Houqi & Wang, Yuqing & Zeng, Ming, 2023. "Demand response comprehensive incentive mechanism-based multi-time scale optimization scheduling for park integrated energy system," Energy, Elsevier, vol. 270(C).
    13. Haibing Wang & Chengmin Wang & Weiqing Sun & Muhammad Qasim Khan, 2022. "Energy Pricing and Management for the Integrated Energy Service Provider: A Stochastic Stackelberg Game Approach," Energies, MDPI, vol. 15(19), pages 1-15, October.
    14. Zezhong Li & Xiangang Peng & Yilin Xu & Fucheng Zhong & Sheng Ouyang & Kaiguo Xuan, 2023. "A Stackelberg Game-Based Model of Distribution Network-Distributed Energy Storage Systems Considering Demand Response," Mathematics, MDPI, vol. 12(1), pages 1-21, December.
    15. Eyni, Leila & Stanko, Milan & Schümann, Heiner, 2022. "Methods for early-phase planning of offshore fields considering environmental performance," Energy, Elsevier, vol. 256(C).
    16. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    17. Yin, Guangzhi & Duan, Maosheng, 2022. "Pricing the deep peak regulation service of coal-fired power plants to promote renewable energy integration," Applied Energy, Elsevier, vol. 321(C).
    18. Lauven, Lars-Peter & Geldermann, Jutta & Desideri, Umberto, 2019. "Estimating the revenue potential of flexible biogas plants in the power sector," Energy Policy, Elsevier, vol. 128(C), pages 402-410.
    19. Teirilä, Juha, 2020. "The value of the nuclear power plant fleet in the German power market under the expansion of fluctuating renewables," Energy Policy, Elsevier, vol. 136(C).
    20. Liu, Ming & Wang, Shan & Yan, Junjie, 2021. "Operation scheduling of a coal-fired CHP station integrated with power-to-heat devices with detail CHP unit models by particle swarm optimization algorithm," Energy, Elsevier, vol. 214(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:jeners:v:15:y:2022:i:5:p:1863-:d:763207. 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.