Hydro and wave generation integration planning for an isolated diesel system in Hot Springs Cove, Canada
Bibliographic record
Abstract
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. Given 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. Most 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. This 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. The 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. For 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$/kWinstalled. For the 200kW wave plus 225kW hydro scenario, the allowable LCOE is 0.67$/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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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".