Integrated Assessment of Nuclear-Renewable Hybrid Energy Systems: A Pathway to Sustainable and Resilient Industrial Electrification in Ghana
Bibliographic record
Abstract
Purpose: Achieving sustainable and cost-effective industrial electrification in Africa necessitates an integrated energy approach that optimally combines Small Modular Reactor (SMR) and renewables, mainly solar and wind energy, as two clean energy sources. Design/Methodology/Approach: Using HOMER Pro software, system performance was simulated to assess energy generation, economic viability, and environmental benefits. The analysis examined annual energy output, levelised cost of energy (LCOE), and carbon emission reductions to determine system sustainability. Findings: Due to the integrated energy system, a net energy surplus of 206,079,408 kWh is achieved, enabling grid exports and the potential production of green hydrogen if effectively harnessed. Economic assessments indicate an LCOE of $0.185/kWh, 34% lower than Ghana’s industrial 2024 grid tariff. Additionally, CO2 emissions are reduced by 15,824,965 kg annually, supporting Ghana’s National Energy Transition Agenda. Research Limitation: Further research is needed to optimise hybrid energy systems, particularly in waste management, policy frameworks, and national grid stability. Practical Implication: SMRs and renewables can enhance energy reliability and affordability, ensure sustainable industrial development, and drastically lower energy sector emissions. Social Implications: Integrating nuclear and renewable energy as a hybrid system can reduce energy poverty, drive industrial growth, support sustainable development, and lower environmental impact. Originality/Value: This study underscores the potential of nuclear-renewable hybrid energy systems to enhance energy security, reduce emissions, and stabilise industrial electricity supply.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".