Comparative analysis of energy dispatch strategies in PV-integrated renewable energy systems
Bibliographic record
Abstract
The growing global population and escalating energy demands have highlighted the urgent need for a transition to sustainable and renewable energy sources. This study investigates the design and optimization of hybrid energy systems (HES) for Pelee Island, Canada—a remote community facing unreliable single-phase grid supply and increasing seasonal demand. The proposed HES integrates photovoltaic (PV) systems with tracking technologies, a biogas gasifier, diesel generator, lithium-ion battery storage, and grid interaction, under 2 dispatch strategies: Load Following (LF) and Cycle Charging (CC). Among 8 configurations, the CC-based system with VCA tracking (776 kW PV, 73 batteries) performs best, achieving a Net Present Cost (NPC) of $1.6 M, Cost of Electricity (COE) of $0.083/kWh, and Renewable Fraction (RF) of 78.7%. It meets 1,537,217 kWh of a 1,537,271 kWh annual load, with only 54.3 kWh unmet. The LF-VCA system offers the highest RF at 86.3% and the lowest emissions at 21.6 t/year but at a higher NPC of $1.62 M. Battery state of charge (SOC) stays above 60% in summer and often drops below 30% in winter. A 50% increase in SOC min raises NPC by 20%, and a 1.5× capital cost nearly doubles COE. A 60% albedo boosts RF by 7.6% and reduces NPC by 18%. Compared to LF, the optimal CC system lowers NPC by $0.02 M and COE by $0.002/kWh, while producing 90,551 kWh/year of surplus energy. Grid imports peak in winter (>100 kW) and fall near zero in summer, while surplus exports exceed 200 kW during peak solar hours, enhancing system revenue through $0.15/kWh sales. • Hybrid energy system with a VCA tracker achieves $0.083/kWh COE and 78.7% RF. • SOC min variations significantly impact NPC and COE in tracker-based systems. • Albedo improvements enhance solar efficiency, reducing NPC and renewable fraction. • CC dispatch strategy outperforms LF in cost-effectiveness and system efficiency.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".