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Erschienen in: Annals of Data Science 4/2023

30.04.2021

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

verfasst von: Manoj Verma, Harish Kumar Ghritlahre, Surendra Bajpai

Erschienen in: Annals of Data Science | Ausgabe 4/2023

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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.

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Literatur
5.
Zurück zum Zitat Verma M (2020) Wind farm repowering using WAsP software – an approach for reducing CO2 emissions in the environment. In: Hashmi S, Choudhury IA (eds) Encyclopedia of renewable and sustainable materials, vol 3. Elsevier, pp 844–859 Verma M (2020) Wind farm repowering using WAsP software – an approach for reducing CO2 emissions in the environment. In: Hashmi S, Choudhury IA (eds) Encyclopedia of renewable and sustainable materials, vol 3. Elsevier, pp 844–859
6.
Zurück zum Zitat Olson DL, Shi Y, Shi Y (2007) Introduction to business data mining, vol 10. McGraw-Hill/Irwin, New York, pp 2250–2254 Olson DL, Shi Y, Shi Y (2007) Introduction to business data mining, vol 10. McGraw-Hill/Irwin, New York, pp 2250–2254
7.
Zurück zum Zitat Shi Y, Tian Y, Kou G, Peng Y, Li J (2011) Optimization based data mining: theory and applications. Springer, BerlinCrossRef Shi Y, Tian Y, Kou G, Peng Y, Li J (2011) Optimization based data mining: theory and applications. Springer, BerlinCrossRef
11.
Zurück zum Zitat Haesens E, Espinoza M, Pluymers B, Goethals I, Thong VV, Driesen J, Belmanss R, Moor BD (2005) Optimal placement and sizing of distributed generator units using genetic optimization algorithms. Electr Power Qual Util J 11(1):97–104 Haesens E, Espinoza M, Pluymers B, Goethals I, Thong VV, Driesen J, Belmanss R, Moor BD (2005) Optimal placement and sizing of distributed generator units using genetic optimization algorithms. Electr Power Qual Util J 11(1):97–104
13.
26.
Zurück zum Zitat Schaffer JD (1985) Multiple objective optimization with vector evaluated genetic algorithms. In: Proceedings of the first international conference on genetic algorithms and their applications, 1985. Lawrence Erlbaum Associates. Inc., Publishers. https://dl.acm.org/doi/proceedings/https://doi.org/10.5555/645511 Schaffer JD (1985) Multiple objective optimization with vector evaluated genetic algorithms. In: Proceedings of the first international conference on genetic algorithms and their applications, 1985. Lawrence Erlbaum Associates. Inc., Publishers. https://​dl.​acm.​org/​doi/​proceedings/​https://​doi.​org/​10.​5555/​645511
Metadaten
Titel
A Case Study of Optimization of a Solar Power Plant Sizing and Placement in Madhya Pradesh, India Using Multi-Objective Genetic Algorithm
verfasst von
Manoj Verma
Harish Kumar Ghritlahre
Surendra Bajpai
Publikationsdatum
30.04.2021
Verlag
Springer Berlin Heidelberg
Erschienen in
Annals of Data Science / Ausgabe 4/2023
Print ISSN: 2198-5804
Elektronische ISSN: 2198-5812
DOI
https://doi.org/10.1007/s40745-021-00334-z

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