Hydro and wave generation integration planning for an isolated diesel system in Hot Springs Cove, Canada
Notice bibliographique
Résumé
Most remote communities in Canada and around the world rely on diesel power for their electricity. Remote diesel power is emissions intensive, expensive to service, noisy, unreliable, costly and risky to transport. Governments, communities, utilities and industry want to displace diesel generation with renewable energy. Renewable electric generation is intermittent and cannot meet electrical demand without energy storage or combination with another generation source. This work examines the cost optimization of renewable energy integration with existing diesel infrastructure in remote communities. \nGiven the variety of geographical locations of remote communities and their proximity to different renewable resources, there is value in developing and understanding a variety of alternative electric supply systems. This work focuses on integrating micro-hydro and wave energy because the case study community is near excellent wave energy and hydro energy resources. \nMost remote communities in Canada receive electrical services from regional utilities. These utilities have moved towards net-metering programs and power purchase agreements (PPAs) with the goal of integrating renewable energy into isolated diesel systems. This approach has the benefit of outsourcing a difficult technical challenge and controlling costs. Such PPA programs are designed to be cost neutral, without raising community electric rates. Rates offered under PPAs are based on avoided diesel fuel cost. Thus far, these rates have encouraged little renewable energy investment. \nThis work provides an alternative method for calculating allowable costs for renewable energy integration that could facilitate crafting new utility policy, including setting optimal incentives for PPA contracts with Independent Power Producers. A detailed computer-based model of a case study community electric system was used to calculate allowable Levelized Cost of Electricity (LCOE) using the following inputs: electric demand, local renewable resources, generator models and existing costs. Hydro-diesel, wave-diesel and wave-hydro-diesel energy inputs with different capacities were modeled to provide greater insight into the value of renewable energy resources to mitigate diesel use. \nThe hydro-diesel systems performance had little variability in operations and costs for selected hydro capacities of 225kW, 275kW and 325kW. The 225kW hydro-diesel system had the best utilization, meeting 65.2% of annual demand and reducing fuel by 65.8%. The variability in the hydro resource will cause year-to-year variability in fuel use reductions ranging from 64-92%. The emissions rate for this system is 293gCO2/kWh. The allowable costs for 225kW hydro generation are $0.68/kWh and 17,000$/kWinstalled. \nFor the wave-diesel system, wave capacity ranges from 200kW to 90kW with respective fuel use reductions of 68.4% to 39.6%. The emissions rate is 271 gCO2/kWh to 518gCO2/kWh. The range of allowable LCOE values of the wave systems are 0.51-0.60$/kWh and the range of allowable installed costs are 19,800$/kWinstalled to 25,400$/kWinstalled. \nFor the 200kW wave plus 225kW hydro scenario, the allowable LCOE is 0.67$/kWh where 80% of the wave supply is utilized and 24% of the hydro supply is utilized. For the 90kW wave plus 225kW hydro scenario, the allowable LCOE is 0.66$/kWh where 93% of the wave supply is utilized and 58% of the hydro supply is utilized.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».