Smart Grid Renewable Energy Configuration Decision Model Based on Multi-Objective Optimization

Pengcheng Yuan

doi:10.62051/ijcsit.v3n2.18

Authors

Pengcheng Yuan

DOI:

https://doi.org/10.62051/ijcsit.v3n2.18

Keywords:

Smart grid, Renewable energy, Multi-objective, optimization, Genetic algorithm

Abstract

The paper constructs a multi-objective decision-making model in the configuration of renewable energy in the smart grid. This is a management tool for finding the configuration of renewable energy in the smart grid to get the maximum economic benefit and use energy most efficiently while considering environmental protection. This paper uses economic theory fully in the application, mathematical modeling, methods for conducting cost-benefit analysis, and optimization algorithms. The power demand, renewable energy supply, and economic and environmental data between the years 2020 and 2024 in one region are used in testing the performance and optimization of the model using genetic algorithms. The results show that the optimization model has significant effects in reducing costs, improving economic benefits and reducing environmental impacts, and the optimized energy configuration scheme significantly reduces CO2 emissions. More references are given on how a multi-objective optimization model is constructed and proved for its practicality for scientific planning and optimization of renewable energy in a smart grid, with high-level feasibility and application value in practicality.

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References

Jackson, A. S., Bulat, A., Argyriou, V., Tzimiropoulos, G.: Predicting patient survival after liver transplantation using evolutionary multi-objective artificial neural networks. Artificial intelligence in medicine, 58(1), 37-49(2013).

Busemeyer, J., Swenson, K. N., Lazarte, A.: An adaptive approach to resource allocation. Organizational Behavior and Human Decision Processes, 38, 318-341(1986).

Werff, E., Steg, L.: The psychology of participation and interest in smart energy systems: Comparing the value-belief-norm theory and the value-identity-personal norm model. Energy research and social science, 22, 107-114(2016).

Sohrabi, M., Tajik, A.: Multi-objective feature selection for warfarin dose prediction. Computational biology and chemistry, 69, 126-133(2017).

Chabris, C., Laibson, D., Morris, C. L., Schuldt, J. P., Taubinsky, D.: The allocation of time in decision-making. Journal of the European Economic Association, 7(2-3), 628-637(2009).

Noppers, E. H., Keizer, K., Milovanovic, M., Steg, L.: The importance of instrumental, symbolic, and environmental attributes for the adoption of smart energy systems. Energy Policy, 98, 12-18(2016).

Ratner, S., Salnikov, A. A., Berezin, A., Sergi, B., Sohag, K.: Customer engagement in innovative smart grid deployment projects: evidence from Russia. Environmental Science and Pollution Research, 29, 5902-5911(2021).

Sohrabi, M., Tajik, A.: Multi-objective feature selection for warfarin dose prediction. Computational biology and chemistry, 69, 126-133(2017).

Azriel, D., Feigin, P.: Adaptive Designs to Maximize Power in Clinical Trials with Multiple Treatments. Sequential Analysis, 33, 60-86(2014).

Hanley, N., Schläpfer, F., Spurgeon, J.: Aggregating the benefits of environmental improvements: distance-decay functions for use and non-use values. Journal of environmental management, 68(3), 297-304(2003).

Faccioli, M., Hanley, N., Torres, C., Font, A.: Do we care about sustainability? An analysis of time sensitivity of social preferences under environmental time-persistent effects. Journal of environmental management, 177, 356-364(2016).

Marinoski, A. K., Rupp, R. F., Ghisi, E.: Environmental benefit analysis of strategies for potable water savings in residential buildings. Journal of environmental management, 206, 28-39(2018).

Irfan, M., Zhao, Z., Rehman, A., Ozturk, I., Li, H.: Consumers’ intention-based influence factors of renewable energy adoption in Pakistan: a structural equation modeling approach. Environmental Science and Pollution Research, 28, 432-445(2020).

Obrecht, M., Kazançoğlu, Y., Denac, M.: Integrating Social Dimensions into Future Sustainable Energy Supply Networks. International Journal of Environmental Research and Public Health, 17, 6230(2020).

Peng, B., Tu, Y., Elahi, E., Wei, G.: Extended Producer Responsibility and corporate performance: Effects of environmental regulation and environmental strategy. Journal of environmental management, 218, 181-189(2018).