Modeling Grid-Connected EV Fleets, Heat Pumps, and Solar PV in Residential Communities
Bibliographic record
Abstract
Environmental concerns are driving a global shift toward sustainable solutions. Renewable energy technologies, such as solar photovoltaics (PV) and air source heat pumps (ASHPs), along with electric vehicles (EVs), are increasingly being integrated into residential buildings as part of this transition. This integration is particularly crucial in developing countries, where energy systems must accommodate growing demand while ensuring environmental sustainability. This paper presents a scenario-based study for a residential community of 500 detached residences, incorporating a fleet of EVs- two vehicles for each household- and a renewable energy system (RES) that includes ASHPs and rooftop and ground-mounted PV generation. The system aims to enhance alignment between local energy generation and consumption while minimizing energy costs and emissions in the Mahone Bay community, Nova Scotia. The EV fleet model was simulated in Python, with data derived from 1,000 EVs across Canada between 2017 and 2019, encompassing charging patterns for thirty-five different vehicle models. The heating power model for residential buildings is simulated using TRNSYS, with data gathered from energy bills collected over the course of a year. The optimal solution employs HOMER GRID software to improve the economic and environmental performance of the RES. The bestperforming system comprises a 10-MW grid-connected rooftop $P V$ and ground-mounted solar power generation to supply the community load. It generates annual savings for the residential community of ${\$}$1,492,910 and a total of ${\$}$ 37,322,740 in utility bills over its 25-year lifetime.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".