Performance of Heuristic Optimization in Coordination of Plug-In Electric Vehicles Charging
DOI: 10.5171/2013.898203
archive: archived pipeline: cataloged verified
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Summary
This paper addresses the challenge of coordinating Plug-in Electric Vehicle (PEV) charging in distribution networks to mitigate adverse grid impacts, such as increased system losses, voltage deviations, and transformer overloading. Motivated by the projected dominance of PEVs and the detrimental effects of uncoordinated charging during peak hours, the authors propose a Heuristic Load Management Algorithm (H-LMA). The study aims to minimize total system power losses and regulate bus voltages while respecting substation transformer loading limits and prioritizing consumer preferences. The methodology involves an offline optimization scheme implemented in MATLAB, assuming all PEVs are plugged in at 18:00. The H-LMA minimizes system losses over an optimization period $T$ using a time interval $\Delta t$, subject to constraints on node voltage limits (0.9–1.1 pu) and maximum demand levels. The algorithm categorizes PEV owners into three priority zones based on tariff willingness: "Red" (high priority, 18:00–22:00), "Blue" (medium priority, 18:00–01:00), and "Green" (low priority, 18:00–08:00). The algorithm iteratively assigns charging schedules starting from the highest priority group, selecting the node and start time that yield minimum losses without violating constraints. The performance is evaluated on a modified 449-node distribution network (IEEE 31 bus combined with residential low-voltage networks) with PEV penetration levels of 16%, 32%, 47%, and 63%. Each PEV is modeled with a 10 kWh battery, 70% depth of discharge, and a fixed 4 kW charging power. Simulation results demonstrate that uncoordinated random charging significantly increases system losses and causes unacceptable voltage deviations, particularly at higher penetration levels. In contrast, the H-LMA effectively reduces these impacts. For instance, at 63% penetration with a 6 PM–10 PM charging window, uncoordinated charging resulted in a 17.15% voltage deviation and 0.89% transformer load current increase, whereas the H-LMA reduced these to 10% and 0.52%, respectively. The study also investigates the sensitivity of the algorithm to the optimization period $T$ (15 minutes to 24 hours) and time interval $\Delta t$ (15 minutes to 1 hour), finding that longer periods and intervals improve accuracy but increase computational time. The significance of this work lies in providing a practical, heuristic-based approach for online or offline PEV coordination that balances grid efficiency with consumer priorities. The findings confirm that coordinated charging can maintain network operation within permissible limits even at high PEV penetration levels, offering a viable solution for integrating electric vehicles into existing distribution infrastructure without requiring extensive grid upgrades.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-19 |
| archive | success | canonical_url | — | — | 1 | 2026-06-25 |
| extract | success | cached | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-20 |
| chunk | success | chunk | — | — | 1 | 2026-06-20 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-20 |
| enrich | success | openalex | — | — | 1 | 2026-06-20 |
| promote | success | — | — | — | 1 | 2026-06-19 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-20 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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