Power Industry’s Life Cycle Simulation using Agent Based Modeling

Document Type : Original Article

Authors

1 PhD student in Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.

2 Assistant Professor, Department of Industrial Management, South Tehran Branch, Islamic Azad University, Tehran, Iran.

3 Professor, Department of Industrial Management, Allameh Tabatabai University, Tehran, Iran.

4 Assistant Professor, Department of Industrial Management, Karaj Branch, Islamic Azad University, Alborz, Iran.

Abstract

In this paper we try to simulate the life cycle of the electricity industry using the agent-based simulation method. For this purpose, 5 Agents were mentioned and they were simulated by Anylogic software .Then, four scenarios were investigated with experts' opinions. In the first scenario, it is assumed that the attractiveness of gas power and combined has been reduced and the attractiveness of the other two power has been added, the result of this scenario is the increase in hydroelectric power production, which is not cost effective due to the lack of water resources for the country. In the second scenario, with the arrival of a new technology, the household consumer decreased by 40%, and hydro and steam power with a capacity of 40% were working in the power generation cycle, the result of this scenario is the reduction of fuel consumption and also the shortage of electricity produced relative to the electricity consumption. In the third scenario, it is assumed that the consumption rate of all consumer factors has decreased, which results in the lack of investor factor investment. the fourth scenario assumes that all consumers' consumption is constant, as well as keeping the attractiveness developed by the government steady. As a result, the capacity of electricity generation has increased, but we are still facing power shortages.

Keywords

Main Subjects

References

  1. Ameli, M., Mansour, S., & Ahmadi-Javid, A. (2019). A simulation-optimization model for sustainable product design and efficient end-of-life management based on individual producer responsibility. Resources, Conservation and Recycling, 140, 246-258.
  2. Azar, A., mashayekhi, M., Amiri, M., Safari, H., (2021). Modeling Steel Supply Chain and Estimating Its Consumption through ABM Methodology. The Jornal of Industrial management Perspective, 11(1), 33-52.( In Persian)
  3. Chau, C.K., Leung, T., & Ng, W. (2015). A review on life cycle assessment, life cycle energy assessment and life cycle carbon emissions assessment on buildings. Applied energy, 143, 395-413.
  4. Diaz, A., Schoggl, J., Reyes, T., Baumgartner, R. (2021). Sustainable product development in a circular economy: Implications for products, actors, decision-making support and lifecycle information management. Sustainable Production and Consumption, 26, 1031-1045.
  5. Energy, M.o., (2021). 53 years of Iran's electricity industry in the mirror of statistics (1346-1398). January 2021, Tavanir specialized parent company. p. 46.
  6. Guinée, J.B. (2002). Handbook on life cycle assessment operational guide to the ISO standards. The international journal of life cycle assessment, 7(5), 311-313.
  7. Khodaparastan, A.S.N.J.M. (2021). Designing an Integrated Method for Increasing Quality of Product through Its Lifetime by Taguchi Design of Experiments and PAF Model (The Case of Entekhab Industrial Group). The Journal of Industrial management Perspective, 11(4), 37-57.( In Persian)
  8. Kwok, S.Y., Schulte, J., & Hallstedt, S. (2020). Approach for sustainability criteria and product life-cycle data simulation in concept selection. in Proceedings of the Design Society: DESIGN Conference. Cambridge University Press.
  9. Li, J., Tao, F., Cheng, Y., Zhao, L,. (2015). Big data in product lifecycle management. The International Journal of Advanced Manufacturing Technology, 81(1), 667-684.
  10. Manda, B.K., Bosch, H., Karanam, S., Beers, H., Bosman, H., Rietveld, E., Worrell, E., Patel, M., (2016). Value creation with life cycle assessment: an approach to contextualize the application of life cycle assessment in chemical companies to create sustainable value. Journal of Cleaner Production, 126, 337-351.
  11. Niven, T. & Kao, H.-Y. (2019). Probing neural network comprehension of natural language arguments. arXiv preprint arXiv:1907.07355.
  12. Pishvaee, E.B& Sahebi, H. (2016). A Simulation-based Optimization Model for Integration of Cash and Material-Flow Planning within a Supply Chain. The Journal of Industrial management Perspective, 6(1), 31-51(In Persian).
  13. Poh, G.K., Chew, I.M. & Tan, J. (2019). Life cycle optimization for synthetic rubber glove manufacturing. Chemical Engineering & Technology, 42(9), 1771-1779.
  14. Rand, W. & Stummer, C. (2021). Agent‐based modeling of new product market diffusion: an overview of strengths and criticisms. Annals of Operations Research, 305(1), 425-447.
  15. Solis, C.M.A., San Juan, J.L., Mayol, A.P., Sy, C.L., Ubando, A.T., Culaba, A.B.,. (2021). A multi-objective life cycle optimization model of an integrated algal biorefinery toward a sustainable circular bioeconomy considering resource recirculation. Energies, 14(5),
  16. Walter, M., Leyh, C., & Strahringer, S. (2017). Knocking on industry’s door: needs in product-cost optimization in the early product life cycle stages. Complex Systems Informatics and Modeling Quarterly, (13),43-60.
  17. Zupko, R. (2021). Application of agent-based modeling and life cycle sustainability assessment to evaluate biorefinery placement. Biomass and Bioenergy, 144,

Files

Share

How to cite

Statistics