Optimization of renewable energy project portfolio selection using hybrid AIS-AFS algorithm in an international case study

In the coming decade, as restrictions on fossil fuel usage become more stringent, investment in renewable energy projects presents an increasingly appealing opportunity. Evaluating investment attractiveness involves considering both profitability and investment risk. This study proposes a multi-obje...

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Vydáno v:Scientific reports Ročník 14; číslo 1; s. 17388 - 25
Hlavní autor: Goli, Alireza
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 29.07.2024
Nature Publishing Group
Nature Portfolio
Témata:
639/4077
639/705
Algorithms
Artificial immune system algorithm
Economics
Fish Swarm algorithm
Fossil fuels
Geographical distribution
Humanities and Social Sciences
Immune system
International studies
Investment risk
Mathematical models
multidisciplinary
Profitability
Renewable energy
Renewable energy project portfolio
Renewable resources
Science
Science (multidisciplinary)
Sustainable development
Renewable energy project portfolio
Artificial immune system algorithm
Investment risk
Fish Swarm algorithm
Profitability
ISSN:2045-2322, 2045-2322
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Shrnutí:In the coming decade, as restrictions on fossil fuel usage become more stringent, investment in renewable energy projects presents an increasingly appealing opportunity. Evaluating investment attractiveness involves considering both profitability and investment risk. This study proposes a multi-objective mathematical model for identifying the optimal Renewable Energy Project Portfolio (REPP), aiming to maximize net present value while minimizing investment risk. The key innovation of this model is its incorporation of project lifetime and workforce employment considerations to discern the best REPP. To optimize the objective functions of this mathematical model, a hybrid meta-heuristic algorithm combining Artificial Immune System (AIS) and Artificial Fish Swarm (AFS) algorithms is introduced. Genuine data from a varied spectrum of renewable energy projects spanning 20 countries has been meticulously collected. The proposed model is optimized using this dataset, considering portfolio sizes of 3, 5, 10, and 15. The numerical results indicate that, at a specific investment risk threshold, the proposed hybrid algorithm outperforms both AIS and AFS in terms of profitability. Furthermore, the assessment of the geographical distribution of selected projects reveals a deliberate effort to avoid concentration in any specific region, demonstrating a commitment to identifying optimal investment opportunities globally. This research advances the understanding of renewable energy project portfolio optimization, providing valuable insights for investors, policymakers, and sustainable development practitioners.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-68449-w