IDEAS home Printed from https://ideas.repec.org/a/spr/aodasc/v10y2023i4d10.1007_s40745-021-00334-z.html
   My bibliography  Save this article

A Case Study of Optimization of a Solar Power Plant Sizing and Placement in Madhya Pradesh, India Using Multi-Objective Genetic Algorithm

Author

Listed:
  • Manoj Verma

    (Chhattisgarh Swami Vivekananda Technical University)

  • Harish Kumar Ghritlahre

    (Chhattisgarh Swami Vivekananda Technical University)

  • Surendra Bajpai

    (MP Urja Vikas Nigam Ltd., Government of Madhya Pradesh)

Abstract

Increase of greenhouse gases and pollution of environment due to use of conventional sources of energy has made the world aware of the need to increase the use of renewable energy sources like solar power, wind power and hydropower. The scope of the solar power is vast and proper optimization of solar power plants can fulfill varying load demands. This paper studies an optimization technique for such a purpose. Estimation of ideal solar power plant sizes is done for fulfilling the load requirements of selected four districts of Madhya Pradesh, a state in the central part of India. The districts are chosen on the basis of solar irradiance and land availability. In this paper, installation of solar power plants of required sizes is recommended at each district to meet their power demands locally as well as to supply the nearby districts when needed. This will reduce the reliance on grid for energy supply and help in making the system more decentralized and distributed. It also reduces significantly the losses incurred during transmission and distribution. This paper presents the problem of power plant size estimation as a multi objective optimization problem. The first objective is to minimize the gap between power demand and generation in each district on a monthly basis. The second objective minimizes the cost of each unit of electricity generated. The third objective deals with minimizing the transmission and distribution losses on supplying power from one district to another. The genetic algorithm is used for solving this multi objective problem. The selected plant installation sites have the minimum capacity utilization factor of 18%. The simulation of the proposed optimization technique shows that the plant size obtained by the algorithm closely follows the objectives set.

Suggested Citation

  • Manoj Verma & Harish Kumar Ghritlahre & Surendra Bajpai, 2023. "A Case Study of Optimization of a Solar Power Plant Sizing and Placement in Madhya Pradesh, India Using Multi-Objective Genetic Algorithm," Annals of Data Science, Springer, vol. 10(4), pages 933-966, August.
    • Handle: RePEc:spr:aodasc:v:10:y:2023:i:4:d:10.1007_s40745-021-00334-z
    DOI: 10.1007/s40745-021-00334-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40745-021-00334-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40745-021-00334-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ge, Ya & Liu, Zhichun & Sun, Henan & Liu, Wei, 2018. "Optimal design of a segmented thermoelectric generator based on three-dimensional numerical simulation and multi-objective genetic algorithm," Energy, Elsevier, vol. 147(C), pages 1060-1069.
    2. Arush Jasuja, 2020. "Feature Selection Using Diploid Genetic Algorithm," Annals of Data Science, Springer, vol. 7(1), pages 33-43, March.
    3. Khorasaninejad, Ehsan & Hajabdollahi, Hassan, 2014. "Thermo-economic and environmental optimization of solar assisted heat pump by using multi-objective particle swam algorithm," Energy, Elsevier, vol. 72(C), pages 680-690.
    4. Rabia Aziz & C. K. Verma & Namita Srivastava, 2018. "Artificial Neural Network Classification of High Dimensional Data with Novel Optimization Approach of Dimension Reduction," Annals of Data Science, Springer, vol. 5(4), pages 615-635, December.
    5. Abdul Majeed, 2019. "Improving Time Complexity and Accuracy of the Machine Learning Algorithms Through Selection of Highly Weighted Top k Features from Complex Datasets," Annals of Data Science, Springer, vol. 6(4), pages 599-621, December.
    6. James M. Tien, 2017. "Internet of Things, Real-Time Decision Making, and Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 149-178, June.
    7. Johannes Urpelainen & Thijs Van de Graaf, 2015. "The International Renewable Energy Agency: a success story in institutional innovation?," International Environmental Agreements: Politics, Law and Economics, Springer, vol. 15(2), pages 159-177, May.
    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. Prashant Singh & Prashant Verma & Nikhil Singh, 2022. "Offline Signature Verification: An Application of GLCM Features in Machine Learning," Annals of Data Science, Springer, vol. 9(6), pages 1309-1321, December.
    2. Firuz Kamalov & Fadi Thabtah & Ho Hon Leung, 2023. "Feature Selection in Imbalanced Data," Annals of Data Science, Springer, vol. 10(6), pages 1527-1541, December.
    3. Nikhil J. Rathod & Manoj K. Chopra & Prem Kumar Chaurasiya & Umesh S. Vidhate & Abhishek Dasore, 2023. "Optimization on the Turning Process Parameters of SS 304 Using Taguchi and TOPSIS," Annals of Data Science, Springer, vol. 10(5), pages 1405-1419, October.
    4. Mohamed Ibrahim & Khaoula Aidi & M. Masoom Ali & Haitham M. Yousof, 2023. "A Novel Test Statistic for Right Censored Validity under a new Chen extension with Applications in Reliability and Medicine," Annals of Data Science, Springer, vol. 10(5), pages 1285-1299, October.
    5. Shu, Gequn & Ma, Xiaonan & Tian, Hua & Yang, Haoqi & Chen, Tianyu & Li, Xiaoya, 2018. "Configuration optimization of the segmented modules in an exhaust-based thermoelectric generator for engine waste heat recovery," Energy, Elsevier, vol. 160(C), pages 612-624.
    6. Heba Soltan Mohamed & M. Masoom Ali & Haitham M. Yousof, 2023. "The Lindley Gompertz Model for Estimating the Survival Rates: Properties and Applications in Insurance," Annals of Data Science, Springer, vol. 10(5), pages 1199-1216, October.
    7. Roberto Moro-Visconti & Salvador Cruz Rambaud & Joaquín López Pascual, 2023. "Artificial intelligence-driven scalability and its impact on the sustainability and valuation of traditional firms," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    8. Shicheng Wang & Chenyi Xu & Wei Liu & Zhichun Liu, 2019. "Numerical Study on Heat Transfer Performance in Packed Bed," Energies, MDPI, vol. 12(3), pages 1-22, January.
    9. Jorge E. De León-Ruiz & Ignacio Carvajal-Mariscal & Antonin Ponsich, 2019. "Feasibility Analysis and Performance Evaluation and Optimization of a DXSAHP Water Heater Based on the Thermal Capacity of the System: A Case Study," Energies, MDPI, vol. 12(20), pages 1-38, October.
    10. M. Sridharan, 2023. "Generalized Regression Neural Network Model Based Estimation of Global Solar Energy Using Meteorological Parameters," Annals of Data Science, Springer, vol. 10(4), pages 1107-1125, August.
    11. Xueyan Xu & Fusheng Yu & Runjun Wan, 2023. "A Determining Degree-Based Method for Classification Problems with Interval-Valued Attributes," Annals of Data Science, Springer, vol. 10(2), pages 393-413, April.
    12. Qinghua Zheng & Chutong Yang & Haijun Yang & Jianhe Zhou, 2020. "A Fast Exact Algorithm for Deployment of Sensor Nodes for Internet of Things," Information Systems Frontiers, Springer, vol. 22(4), pages 829-842, August.
    13. Shah Hussain & Muhammad Qasim Khan, 2023. "Student-Performulator: Predicting Students’ Academic Performance at Secondary and Intermediate Level Using Machine Learning," Annals of Data Science, Springer, vol. 10(3), pages 637-655, June.
    14. A. R. Sherwani & Q. M. Ali, 2023. "Parametric Classification using Fuzzy Approach for Handling the Problem of Mixed Pixels in Ground Truth Data for a Satellite Image," Annals of Data Science, Springer, vol. 10(6), pages 1459-1472, December.
    15. Hui Zheng & Peng LI & Jing HE, 2022. "A Novel Association Rule Mining Method for Streaming Temporal Data," Annals of Data Science, Springer, vol. 9(4), pages 863-883, August.
    16. Ge, Ya & Lin, Yousheng & He, Qing & Wang, Wenhao & Chen, Jiechao & Huang, Si-Min, 2021. "Geometric optimization of segmented thermoelectric generators for waste heat recovery systems using genetic algorithm," Energy, Elsevier, vol. 233(C).
    17. Rakhal Das & Anjan Mukherjee & Binod Chandra Tripathy, 2022. "Application of Neutrosophic Similarity Measures in Covid-19," Annals of Data Science, Springer, vol. 9(1), pages 55-70, February.
    18. Jorge E. De León-Ruiz & Ignacio Carvajal-Mariscal, 2018. "Mathematical Thermal Modelling of a Direct-Expansion Solar-Assisted Heat Pump Using Multi-Objective Optimization Based on the Energy Demand," Energies, MDPI, vol. 11(7), pages 1-27, July.
    19. Afzal, Asif & Buradi, Abdulrajak & Jilte, Ravindra & Shaik, Saboor & Kaladgi, Abdul Razak & Arıcı, Muslum & Lee, Chew Tin & Nižetić, Sandro, 2023. "Optimizing the thermal performance of solar energy devices using meta-heuristic algorithms: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    20. Mohanraj, M. & Belyayev, Ye. & Jayaraj, S. & Kaltayev, A., 2018. "Research and developments on solar assisted compression heat pump systems – A comprehensive review (Part A: Modeling and modifications)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 83(C), pages 90-123.

    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:spr:aodasc:v:10:y:2023:i:4:d:10.1007_s40745-021-00334-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.