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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
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    References listed on IDEAS

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    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.
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