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An Agile Approach for Adopting Sustainable Energy Solutions with Advanced Computational Techniques

David Abdul Konneh (), Harun Or Rashid Howlader (), M. H. Elkholy and Tomonobu Senjyu
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David Abdul Konneh: Department of Electrical and Electronics Engineering, University of the Ryukyus, Okinawa 903-0213, Japan
Harun Or Rashid Howlader: Grid START, Hawaii Natural Energy Institute, Research Corporation of The University of Hawaii, Honolulu, HI 96822, USA
M. H. Elkholy: Department of Electrical and Electronics Engineering, University of the Ryukyus, Okinawa 903-0213, Japan
Tomonobu Senjyu: Department of Electrical and Electronics Engineering, University of the Ryukyus, Okinawa 903-0213, Japan

Energies, 2024, vol. 17, issue 13, 1-26

Abstract: In the face of the burgeoning electricity demands and the imperative for sustainable development amidst rapid industrialization, this study introduces a dynamic and adaptable framework suitable for policymakers and renewable energy experts working on integrating and optimizing renewable energy solutions. While using a case study representative model for Sub-Saharan Africa (SSA) to demonstrate the challenges and opportunities present in introducing optimization methods to bridge power supply deficits and the scalability of the model to other regions, this study presents an agile multi-criteria decision tool that pivots on four key development phases, advancing established methodologies and pioneering refined computational techniques, to select optimal configurations from a set of Policy Decision-Making Metrics (PDM-DPS). Central to this investigation lies a rigorous comparative analysis of variants of three advanced algorithmic approaches: Swarm-Based Multi-objective Particle Swarm Optimization (MOPSO), Decomposition-Based Multi-objective Evolutionary Algorithm (MOEA/D), and Evolutionary-Based Strength Pareto Evolutionary Algorithm (SPEA2). These are applied to a grid-connected hybrid system, evaluated through a comprehensive 8760-hour simulation over a 20-year planning horizon. The evaluation is further enhanced by a set of refined Algorithm Performance Evaluation Metrics (AL-PEM) tailored to the specific constraints. The findings not only underscore the robustness and consistency of the SPEA2 variant over 15 runs of 200 generations each, which ranks first on the AL-PEM scale, but the findings also validate the strategic merit of combining multiple technologies and empowering policymakers with a versatile toolkit for informed decision-making.

Keywords: sustainable energy solutions; renewable energy integration; multi-criteria decision tool; advanced computational techniques; grid-connected hybrid system; optimization algorithms; policy decision-making metrics; environmental sustainability; energy security (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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