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24 October 2023 Power grid portfolio investment decision optimization based on RWCS algorithm
Xin Mei, Xing Deng, Xiang Jin, Fangxu Han
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Proceedings Volume 12804, Second International Conference on Sustainable Technology and Management (ICSTM 2023); 128042N (2023) https://doi.org/10.1117/12.3006610
Event: 2nd International Conference on Sustainable Technology and Management (ICSTM2023), 2023, Dongguan, China
Abstract
The "14th Five-Year Plan" proposed the development of strategic emerging industries, accelerate the construction of a modern industrial system of the national major strategic deployment. Therefore, power grid enterprises need to expand new businesses to improve their own benefits. Based on RWCS algorithm, this paper optimizes the portfolio investment decision of emerging business and traditional business of power grid enterprises. Firstly, the key factors and indicators affecting the realization of investment value of emerging business are extracted, and the evaluation system of investment value of emerging business of power grid is proposed. Then, we construct a multi-business portfolio investment decision optimization model for the integration of grid emerging business and traditional business, and solve the multi-business portfolio investment decision optimization model using RWCS algorithm. Finally, the optimal model of emerging business portfolio decision-making is verified with examples to provide reference for grid business investment and support the transformation and upgrading of the grid industry and the company's high-quality development.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Xin Mei, Xing Deng, Xiang Jin, and Fangxu Han "Power grid portfolio investment decision optimization based on RWCS algorithm", Proc. SPIE 12804, Second International Conference on Sustainable Technology and Management (ICSTM 2023), 128042N (24 October 2023); https://doi.org/10.1117/12.3006610
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KEYWORDS
Power grids
Analytical research
Reliability
Factor analysis
Particle swarm optimization
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