Version 1
: Received: 26 April 2024 / Approved: 28 April 2024 / Online: 28 April 2024 (09:57:16 CEST)
How to cite:
Konneh, D.A.; Howlader, H.O.R.; Elkholy, M.H.; Senjyu, T. An Agile Approach for Adopting Sustainable and Environmentally Friendly Energy Solutions with Advanced Computational Techniques. Preprints2024, 2024041846. https://doi.org/10.20944/preprints202404.1846.v1
Konneh, D.A.; Howlader, H.O.R.; Elkholy, M.H.; Senjyu, T. An Agile Approach for Adopting Sustainable and Environmentally Friendly Energy Solutions with Advanced Computational Techniques. Preprints 2024, 2024041846. https://doi.org/10.20944/preprints202404.1846.v1
Konneh, D.A.; Howlader, H.O.R.; Elkholy, M.H.; Senjyu, T. An Agile Approach for Adopting Sustainable and Environmentally Friendly Energy Solutions with Advanced Computational Techniques. Preprints2024, 2024041846. https://doi.org/10.20944/preprints202404.1846.v1
APA Style
Konneh, D.A., Howlader, H.O.R., Elkholy, M.H., & Senjyu, T. (2024). An Agile Approach for Adopting Sustainable and Environmentally Friendly Energy Solutions with Advanced Computational Techniques. Preprints. https://doi.org/10.20944/preprints202404.1846.v1
Chicago/Turabian Style
Konneh, D.A., M. H. Elkholy and Tomonobu Senjyu. 2024 "An Agile Approach for Adopting Sustainable and Environmentally Friendly Energy Solutions with Advanced Computational Techniques" Preprints. https://doi.org/10.20944/preprints202404.1846.v1
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 policy-makers 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 upon 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 also validate the strategic merit of combining multiple technologies and
empowering policymakers with a versatile toolkit for informed decision-making.
Keywords
n/a; 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 and Energy Security
Subject
Engineering, Energy and Fuel Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
