A multi-objective strategy for cost-effective microgrid solutions based on renewable energy sources


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Authors

  • Ihtisham Ullah Khan University of Engineering & Technology
  • Dr. Gul Rukh University of Engineering & Technology
  • Mian Farhan Ullah School Leader / Elementary & Secondary Education Department Khyber Pakhtunkhwa

DOI:

https://doi.org/10.59287/icaens.1128

Keywords:

Renewable Energy Sources (RESs), Energy Management, Cost Optimization, Multi-Verse Optimizer, Microgrid, Multi-Objective Genetic Algorithm

Abstract

With the rapid urbanization and increasing energy demands, microgrids are recognizing renewable energy sources (RESs) as a valuable power generation option. However, efficiently managing the energy cost poses a significant challenge in integrating RESs with microgrids. To address this challenge, this study presents a novel approach utilizing a cost-effective multi-objective genetic algorithm (MOGA) to optimize power allocation among diverse generation units within the microgrid. The proposed MOGA algorithm aims to minimize generation costs by efficiently distributing the generated power from different sources in the microgrid vs the CO2 emission. By leveraging the genetic algorithm population, MOGA generates a diverse set of non-dominated solutions. Simulation results demonstrate the effectiveness of the proposed approach in reducing the cost of RESs in microgrids, surpassing the performance of other multi-objective optimization methods such as multi-objective particle swarm (MOPSO) and multi-objective wind-driven optimization (MOWDO).

Author Biographies

Ihtisham Ullah Khan, University of Engineering & Technology

Electrical Engineering Department /  Mardan, Pakistan

Dr. Gul Rukh, University of Engineering & Technology

Electrical Engineering Department /  Mardan, Pakistan

Mian Farhan Ullah, School Leader / Elementary & Secondary Education Department Khyber Pakhtunkhwa

Pakistan

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Published

2023-07-22

How to Cite

Khan, I. U., Rukh, D. G., & Ullah, M. F. (2023). A multi-objective strategy for cost-effective microgrid solutions based on renewable energy sources. International Conference on Applied Engineering and Natural Sciences, 1(1), 1057–1061. https://doi.org/10.59287/icaens.1128