Impact of Local Energy Trading on LV Distribution Network: A Case Study in New Zealand
Bibliographic record
Abstract
This paper explores the potential of local energy market (LEM) for optimising energy management in residential communities, focusing on the rise of rooftop solar photovoltaic (PV) systems and electric vehicles (EVs) in New Zealand. A mixed-integer linear programming (MILP) based optimisation model is developed to minimize operation costs and temperature deviation of household hot water cylinders (HWCs) within the LEM framework. For this, a detailed modelling of flexible devices within the LV distribution network is implemented. The study considers various scenarios with different penetration levels of rooftop PV systems and EVs to assess the impact of local energy trading on distribution networks. Real-time load data, market prices, and statistical models are utilized for numerical simulations. Results from numerical simulations demonstrate that LEM significantly reduces operational costs compared to traditional Home Energy Management Systems (HEMS). However, the presence of storage devices can lead to increased peak grid import in LEMs. The sensitivity analysis further revealed that the LEM based model has fewer scenarios with reverse power flow. The findings from this study highlighted the need of appropriate energy management strategies for storage devices within LEMs to mitigate grid overloading and to facilitate the increased integration of EVs and PVs in distribution networks.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".