Published

2024-12-01

Genetic Algorithm-Based Optimization of Solar Photovol-taic Integration and Demand Response for CO2 Reduction in Indian Coal Power

Optimización basada en algoritmos genéticos de integración de energía solar fotovoltaica y respuesta a la demanda para la reducción de CO2 en la energía de carbón de la India

DOI:

https://doi.org/10.15446/ing.investig.111248

Keywords:

bi-level optimization, distribution network, power quality, renewable energy (en)
optimización bifásica, red de distribución, calidad de energía, energía renovable (es)

Authors

In 2022, global coal combustion contributed significantly to global pollution, producing 15.22 billion metric tons of carbon dioxide (CO2). This research addresses the urgent challenge of mitigating CO2 emissions in Indian coal power plants by strategically deploying solar photovoltaic (PV) systems and integrating demand response mechanisms. The imperative to reduce greenhouse gas emissions from coal-based electricity generation underscores the critical context of climate change. Emphasizing the vital role of integrating renewable energy-based distributed generators into the existing coal infrastructure, this study positions solar PV technology as a promising solution. Optimal solar PV system allocation is achieved through the implementation of the genetic algorithm technique. Factors such as solar resource availability, electricity demand patterns, and the CO2 intensity associated with coal power generation are considered in this process. The primary research objective is twofold: to minimize CO2 emissions and maximize the integration of solar PV systems while mitigating power losses. The proposed approach considers the intermittent nature of solar power and the dynamic characteristics of demand. Rigorous testing on an IEEE 33-bus system powered by the studied coal power plant reveals a substantial 29.31% reduction in CO2 generation following the implementation of the proposed strategy. This research represents a decisive step towards fostering a more sustainable and environmentally friendly energy landscape. Our study's outcomes offer valuable insights for policymakers and stakeholders in the energy sector, providing a robust foundation for the advancement of environmentally conscious practices within the coal power industry.

En 2022, la combustión global de carbón contribuyó significativamente a la contaminación mundial, produciendo 15.22 mil millones de toneladas métricas de dióxido de carbono (CO2). Esta investigación aborda el desafío urgente de mitigar las emisiones de CO2 en las plantas de energía de carbón en India mediante el despliegue estratégico de sistemas solares fotovoltaicos (FV) y la integración de mecanismos de respuesta a la demanda. La necesidad imperiosa de reducir las emisiones de gases de efecto invernadero derivadas de la generación eléctrica a base de carbón subraya el contexto crítico del cambio climático. Destacando el papel esencial de integrar generadores distribuidos basados en energías renovables en la infraestructura de carbón existente, este estudio posiciona la tecnología solar FV como una solución prometedora. La asignación óptima de sistemas solares FV se logra mediante la implementación de la técnica de algoritmo genético. En este proceso se consideran factores como la disponibilidad de recursos solares, los patrones de demanda eléctrica y la intensidad de CO2 asociada a la generación de energía por carbón. El objetivo principal de la investigación es doble: minimizar las emisiones de CO2 y maximizar la integración de sistemas solares FV mientras se mitigan las pérdidas de energía. El enfoque propuesto tiene en cuenta la naturaleza intermitente de la energía solar y las características dinámicas de la demanda. Pruebas rigurosas en un sistema IEEE de 33 nodos alimentado por la planta de energía de carbón estudiada revelan una reducción sustancial del 29.31 % en la generación de CO2 tras la implementación de la estrategia propuesta. Esta investigación representa un paso decisivo hacia la promoción de un panorama energético más sostenible y respetuoso con el medio ambiente. Los resultados de nuestro estudio ofrecen valiosos conocimientos para los formuladores de políticas y las partes interesadas del sector energético, proporcionando una base sólida para el avance de prácticas ambientalmente responsables dentro de la industria de energía a base de carbón.

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How to Cite

APA

Saxena, V. and Kumar Rajput, S. (2024). Genetic Algorithm-Based Optimization of Solar Photovol-taic Integration and Demand Response for CO2 Reduction in Indian Coal Power . Ingeniería e Investigación, 44(3), e111248. https://doi.org/10.15446/ing.investig.111248

ACM

[1]
Saxena, V. and Kumar Rajput, S. 2024. Genetic Algorithm-Based Optimization of Solar Photovol-taic Integration and Demand Response for CO2 Reduction in Indian Coal Power . Ingeniería e Investigación. 44, 3 (Dec. 2024), e111248. DOI:https://doi.org/10.15446/ing.investig.111248.

ACS

(1)
Saxena, V.; Kumar Rajput, S. Genetic Algorithm-Based Optimization of Solar Photovol-taic Integration and Demand Response for CO2 Reduction in Indian Coal Power . Ing. Inv. 2024, 44, e111248.

ABNT

SAXENA, V.; KUMAR RAJPUT, S. Genetic Algorithm-Based Optimization of Solar Photovol-taic Integration and Demand Response for CO2 Reduction in Indian Coal Power . Ingeniería e Investigación, [S. l.], v. 44, n. 3, p. e111248, 2024. DOI: 10.15446/ing.investig.111248. Disponível em: https://revistas.unal.edu.co/index.php/ingeinv/article/view/111248. Acesso em: 11 feb. 2025.

Chicago

Saxena, Vivek, and Saurabh Kumar Rajput. 2024. “Genetic Algorithm-Based Optimization of Solar Photovol-taic Integration and Demand Response for CO2 Reduction in Indian Coal Power ”. Ingeniería E Investigación 44 (3):e111248. https://doi.org/10.15446/ing.investig.111248.

Harvard

Saxena, V. and Kumar Rajput, S. (2024) “Genetic Algorithm-Based Optimization of Solar Photovol-taic Integration and Demand Response for CO2 Reduction in Indian Coal Power ”, Ingeniería e Investigación, 44(3), p. e111248. doi: 10.15446/ing.investig.111248.

IEEE

[1]
V. Saxena and S. Kumar Rajput, “Genetic Algorithm-Based Optimization of Solar Photovol-taic Integration and Demand Response for CO2 Reduction in Indian Coal Power ”, Ing. Inv., vol. 44, no. 3, p. e111248, Dec. 2024.

MLA

Saxena, V., and S. Kumar Rajput. “Genetic Algorithm-Based Optimization of Solar Photovol-taic Integration and Demand Response for CO2 Reduction in Indian Coal Power ”. Ingeniería e Investigación, vol. 44, no. 3, Dec. 2024, p. e111248, doi:10.15446/ing.investig.111248.

Turabian

Saxena, Vivek, and Saurabh Kumar Rajput. “Genetic Algorithm-Based Optimization of Solar Photovol-taic Integration and Demand Response for CO2 Reduction in Indian Coal Power ”. Ingeniería e Investigación 44, no. 3 (December 1, 2024): e111248. Accessed February 11, 2025. https://revistas.unal.edu.co/index.php/ingeinv/article/view/111248.

Vancouver

1.
Saxena V, Kumar Rajput S. Genetic Algorithm-Based Optimization of Solar Photovol-taic Integration and Demand Response for CO2 Reduction in Indian Coal Power . Ing. Inv. [Internet]. 2024 Dec. 1 [cited 2025 Feb. 11];44(3):e111248. Available from: https://revistas.unal.edu.co/index.php/ingeinv/article/view/111248

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